Reviewerly/exHarmony huggingface dataset:
Rate-Distortion Optimized Learning-Based Image Compression using an Adaptive Hierachical Autoencoder with Conditional Hyperprior

Deep-learning-based compressive autoencoders consist of a single non-linear function mapping the image to a latent space which is quantized and transmitted. Afterwards, a second non-linear function transforms the received latent space back to a reconstructed image. This method achieves superior quality than many traditional image coders, which is due to a non-linear generalization of linear transforms used in traditional coders. However, modern image and video coder achieve large coding gains by applying rate-distortion optimization on dynamic block-partitioning. In this paper, we present RDONet, a novel approach to achieve similar effects in compression with full image autoencoders by using different hierarchical levels, which are transmitted adaptively after performing an external rate-distortion optimization. Using our model, we are able to save up to 20% rate over comparable non-hierarchical models while maintaining the same quality.
MAEDyS

Evolving effective coordination strategies in tightly coupled multi-agent settings with sparse team fitness evaluations is challenging. It relies on multiple agents simultaneously stumbling upon the goal state to generate a learnable feedback signal. In such settings, estimating an agent's contribution to the overall team performance is extremely difficult, leading to a well-known structural credit assignment problem. This problem is further exacerbated when agents must complete sub-tasks with added spatial and temporal constraints, and different sub-tasks may require different local skills. We introduce MAEDyS, Multiagent Evolution via Dynamic Skill Selection, a hybrid bi-level optimization framework that augments evolutionary methods with policy gradient methods to generate effective coordination policies. MAEDyS learns to dynamically switch between multiple local skills towards optimizing the team fitness. It adopts fast policy gradients to learn several local skills using dense local rewards. It utilizes an evolutionary process to optimize the delayed team fitness by recruiting the most optimal skill at any given time. The ability to switch between various local skills during an episode eliminates the need for designing heuristic mixing functions. We evaluate MAEDyS in complex multiagent coordination environments with spatial and temporal constraints and show that it outperforms prior methods.
Property Graph Schema Optimization for Domain-Specific Knowledge Graphs

Enterprises are creating domain-specific knowledge graphs by curating and integrating their business data from multiple sources. Ontologies provide a semantic abstraction for such knowledge graphs to describe their data in terms of the entities involved and their relationships. There has been a lot of effort to build systems that enable efficient querying over knowledge graphs, represented as property graphs. However the problem of schema optimization in the property graph setting has been largely ignored. In this work, we show that graph schema design has significant impact on query performance, and propose two algorithms to generate an optimized property graph schema from the domain ontology. To the best of our knowledge, we are the first to present an ontology-driven approach for property graph schema optimization. The rich semantic relationships in an ontology contain a variety of opportunities to reduce edge traversals and consequently improve the graph query performance. Our experimental study with two real-world knowledge graphs shows that our algorithms produce high-quality schemas, achieving up to 2 orders of magnitude speed-up compared to alternative schema designs.
iQUANT: Interactive Quantitative Investment Using Sparse Regression Factors

Abstract The model‐based investing using financial factors is evolving as a principal method for quantitative investment. The main challenge lies in the selection of effective factors towards excess market returns. Existing approaches, either hand‐picking factors or applying feature selection algorithms, do not orchestrate both human knowledge and computational power. This paper presents iQUANT, an interactive quantitative investment system that assists equity traders to quickly spot promising financial factors from initial recommendations suggested by algorithmic models, and conduct a joint refinement of factors and stocks for investment portfolio composition. We work closely with professional traders to assemble empirical characteristics of “good” factors and propose effective visualization designs to illustrate the collective performance of financial factors, stock portfolios, and their interactions. We evaluate iQUANT through a formal user study, two case studies, and expert interviews, using a real stock market dataset consisting of 3000 stocks × 6000 days × 56 factors.
Exergy Efficiency and Enviroeconomic Analysis of Solar Photovoltaic Power in Nepal

As Nepal has been combating the energy crisis for decades, renewable energy will be crucial for its sustainable solution. This study aims to explore exergetic efficiency and environmental cost of solar photovoltaic (PV) technology in different cities of Nepal. The solar exergy efficiency of 12 cities of Nepal is evaluated herein with various parameters. It is observed that the city of Janakpur carries the highest amount of solar PV exergy efficiency of 25.77% and 36.27%, considering solar cell efficiency of 12% and 22.5%, respectively. Similarly, Bharatpur has the lowest solar PV exergy efficiency of 23.96% and 34.46%, with solar cell efficiency of 12% and 22.5%, respectively. In addition, average thermal exergy efficiency of solar PV varies between 7.69% and 17.70%, whereas total exergy efficiency varies between 19.71–29.58% and 30.19–40.2%. It is also found that the months of March and April carry the maximum average solar exergy efficiency, whereas July and August bring the least. In addition, an enviroeconomic study on various cities demonstrates that the cities of Mahendranagar and Dhangadhi produce solar power with higher environmental cost. Finally, the study encourages relevant policymakers to develop robust renewable energy policies that enhance the solar energy share in Nepalese energy dynamics.
Noise Conditional Flow Model for Learning the Super-Resolution Space

Fundamentally, super-resolution is ill-posed problem because a low-resolution image can be obtained from many high-resolution images. Recent studies for super-resolution cannot create diverse super-resolution images. Although SRFlow tried to account for ill-posed nature of the super-resolution by predicting multiple high-resolution images given a low-resolution image, there is room to improve the diversity and visual quality. In this paper, we propose Noise Conditional flow model for Super-Resolution, NCSR, which increases the visual quality and diversity of images through noise conditional layer. To learn more diverse data distribution, we add noise to training data. However, low-quality images are resulted from adding noise. We propose the noise conditional layer to overcome this phenomenon. The noise conditional layer makes our model generate more diverse images with higher visual quality than other works. Furthermore, we show that this layer can overcome data distribution mismatch, a problem that arises in normalizing flow models. With these benefits, NCSR outperforms baseline in diversity and visual quality and achieves better visual quality than traditional GAN-based models. We also get outperformed scores at NTIRE 2021 challenge [21].
Scale-Sensitive Feature Reassembly Network for Pedestrian Detection

Serious scale variation is a key challenge in pedestrian detection. Most works typically employ a feature pyramid network to detect objects at diverse scales. Such a method suffers from information loss during channel unification. Inadequate sampling of the backbone network also affects the power of pyramidal features. Moreover, an arbitrary RoI (region of interest) allocation scheme of these detectors incurs coarse RoI representation, which becomes worse under the dilemma of small pedestrian relative scale (PRS). In this paper, we propose a novel scale-sensitive feature reassembly network (SSNet) for pedestrian detection in road scenes. Specifically, a multi-parallel branch sampling module is devised with flexible receptive fields and an adjustable anchor stride to improve the sensitivity to pedestrians imaged at multiple scales. Meanwhile, a context enhancement fusion module is also proposed to alleviate information loss by injecting various spatial context information into the original features. For more accurate prediction, an adaptive reassembly strategy is designed to obtain recognizable RoI features in the proposal refinement stage. Extensive experiments are conducted on CityPersons and Caltech datasets to demonstrate the effectiveness of our method. The detection results show that our SSNet surpasses the baseline method significantly by integrating lightweight modules and achieves competitive performance with other methods without bells and whistles.
Factors Influencing Older Adults’ Acceptance of Health Monitoring Smart Clothing

We aimed to examine the key factors that influence older adults’ perception and intention to wear health monitoring smart clothing. A convenience sample of 376 U.S. older adults was used for data analyses. Influence of functional and aesthetic attributes on older adults’ perception and intention of its use was not supported. The findings imply that older adults who are satisfied with expressive and tracking attributes of health monitoring smart clothing find it useful, easier to use, and socially acceptable, which leads them to be more likely to use it.
Entangled Reflections on Designing with Leaky Breastfeeding Bodies

Bodily transformations that attend breastfeeding include entanglements of more-than-human materials and agencies. These can be seen in exchanges of physical matter, such as bacteria, that blur bodily boundaries. I present three design explorations of my breastfeeding experiences as entangled: knitting bras for lopsided breasts, transforming milk into fiddling necklaces, and site-writing around breastfeeding. Through spatial and conceptual mappings of the explorations, I propose them as alternative narratives in designing for leaky breastfeeding bodies. I also offer two broader reflections on designing with, for, and among more-than-human bodily materials: generous absence and bodily mappings. The accompanying reading instructions to this bodily research open for further encounters and reflections between the three explorations.
Hierarchical Motion Understanding via Motion Programs

Current approaches to video analysis of human motion focus on raw pixels or keypoints as the basic units of reasoning. We posit that adding higher-level motion primitives, which can capture natural coarser units of motion such as "backswing" or "follow-through", can be used to improve downstream analysis tasks. This higher level of abstraction can also capture key features, such as loops of repeated primitives, that are currently inaccessible at lower levels of representation. We therefore introduce Motion Programs, a neuro-symbolic, program-like representation that expresses motions as a composition of high-level primitives. We also present a system for automatically inducing motion programs from videos of human motion and for leveraging motion programs in video synthesis. Experiments show that motion programs can accurately describe a diverse set of human motions and the inferred programs contain semantically meaningful motion primitives, such as arm swings and jumping jacks. Our representation also benefits downstream tasks such as video interpolation and video prediction and outperforms off-the-shelf models. We further demonstrate how these programs can detect diverse kinds of repetitive motion and facilitate interactive video editing.
Multimodal Deep Learning Framework for Sentiment Analysis from Text-Image Web Data

Understanding people’s sentiments from data published on the web presents a significant research problem and has a variety of applications, such as learning the context, prediction of election results and opinion about an incident. So far, sentiment analysis from web data has focused primarily on a single modality, such as text or image. However, the readily available multiple modal information, such as image and different forms of texts, as a combination can help to estimate the sentiments more accurately. Further, blindly combining the visual and textual features increases the complexity of the model, which ultimately reduces the sentiment analysis performance as it often fails to capture the correct interrelationships between different modalities. Hence, in this study, a sentiment analysis framework that carefully fuses the salient visual cues and high attention textual cues is proposed, exploiting the interrelationships between multimodal web data. A multimodal deep association learner is stacked to learn the relationships between learned salient visual features and textual features. Further, to automatically learn the discriminative features from the image and text, two streams of unimodal deep feature extractors are proposed to extract the visual and textual features that are most relevant to the sentiments. Finally, the sentiment is estimated using the features that are combined using a late fusion mechanism. The extensive evaluations show that our proposed framework achieved promising results for sentiment analysis using web data, in comparison to existing unimodal approaches and multimodal approaches that blindly combine the visual and textual features.
Renofeation: A Simple Transfer Learning Method for Improved Adversarial Robustness

Fine-tuning through knowledge transfer from a pre-trained model on a large-scale dataset is a widely spread approach to effectively build models on small-scale datasets. In this work, we show that a recent adversarial attack designed for transfer learning via re-training the last linear layer can successfully deceive models trained with transfer learning via end-to-end fine-tuning. This raises security concerns for many industrial applications. In contrast, models trained with random initialization without transfer are much more robust to such attacks, although these models often exhibit much lower accuracy. To this end, we propose noisy feature distillation, a new transfer learning method that trains a network from random initialization while achieving clean-data performance competitive with fine-tuning.
Developing a Mobile Automated Medical Assistant for Hospitals in Bangladesh

This paper introduces a mobile robot that is designed to assist medical professionals in hospitals with daily simple tasks which allow the staff in turn to optimize their resources. A prototype of this robot was deployed at the Bangabandhu Sheikh Mujib Medical University in Bangladesh with funding from the Government of Bangladesh, USAID and UNDP. This robot performed its functions as an Automated Medical Assistant (AMA) by delivering to and interacting with patients and medical staff during its trial phase in 2018. This paper presents the robot to have an intuitive user interface, a user-friendly mode of communication, smart features in its command-and-control system, security, and energy optimization. The smart robot is able to navigate its way to specific patients as well as avoid obstacles and finally navigate back to its charging station once its energy is depleted.
Do All MobileNets Quantize Poorly? Gaining Insights into the Effect of Quantization on Depthwise Separable Convolutional Networks Through the Eyes of Multi-scale Distributional Dynamics

As the “Mobile AI” revolution continues to grow, so does the need to understand the behaviour of edge-deployed deep neural networks. In particular, MobileNets [9], [22] are the go-to family of deep convolutional neural networks (CNN) for mobile. However, they often have significant accuracy degradation under post-training quantization. While studies have introduced quantization-aware training and other methods to tackle this challenge, there is limited understanding into why MobileNets (and potentially depthwise-separable CNNs (DWSCNN) in general) quantize so poorly compared to other CNN architectures. Motivated to gain deeper insights into this phenomenon, we take a different strategy and study the multi-scale distributional dynamics of MobileNet-V1, a set of smaller DWSCNNs, and regular CNNs. Specifically, we investigate the impact of quantization on the weight and activation distributional dynamics as information propagates from layer to layer, as well as overall changes in distributional dynamics at the network level. This fine-grained analysis revealed significant dynamic range fluctuations and a “distributional mismatch” between channelwise and layerwise distributions in DWSCNNs that lead to increasing quantized degradation and distributional shift during information propagation. Furthermore, analysis of the activation quantization errors show that there is greater quantization error accumulation in DWSCNN compared to regular CNNs. The hope is that such insights can lead to innovative strategies for reducing such distributional dynamics changes and improve post-training quantization for mobile.
A Deep Learning Approach to Anomaly Sequence Detection for High-Resolution Monitoring of Power Systems

A deep learning approach is proposed to detect data and system anomalies using high-resolution continuous point-on-wave (CPOW) or phasor measurements. Both the anomaly and anomaly-free measurement models are assumed to have unknown temporal dependencies and probability distributions. Historical training samples are assumed for the anomaly-free model, while no training samples are available for the anomaly measurements. By transforming the anomaly-free observations into uniform independent and identically distributed sequences via a generative adversarial network, the proposed approach deploys a uniformity test for anomaly detection at the sensor level. A distributed detection scheme that combines sensor level detections at the control center is also proposed which combines local detections to form more reliable detections. Numerical results demonstrate significant improvement over the state-of-the-art solutions for various bad-data cases using real and synthetic CPOW and PMU data sets.
Text Summarization using TF-IDF and Textrank algorithm

In this digital era, a tremendous amount of information gets generated every day. The generated information is used for multiple purposes such as scientific/medical research, news generation, blogs, etc. Reading and gathering the important information and summarizing it manually can become a tedious task which is also prone to manual errors. Therefore, more time is required to read that information, and also unwanted information gets mixed up with important information. It is difficult for a person to manually summarize a large document. Also, there is an issue with finding necessary documents and absorbing relevant information from them. In this proposed system, we are implementing a combination of TFIDF and Textrank algorithm with some NLP methods which will efficiently summarize the given data and will perform better than the other systems.
Image Recommendation Algorithm Combined with Deep Neural Network Designed for Social Networks

In recent years, deep neural networks have achieved great success in many fields, such as computer vision and natural language processing. Traditional image recommendation algorithms use text-based recommendation methods. The process of displaying images requires a lot of time and labor, and the time-consuming labor is inefficient. Therefore, this article mainly studies image recommendation algorithms based on deep neural networks in social networks. First, according to the time stamp information of the dataset, the interaction records of each user are sorted by the closest time. Then, some feature vectors are created via traditional feature algorithms like LBP, BGC3, RTU, or CNN extraction. For image recommendation, two LSTM neural networks are established, which accept these feature vectors as input, respectively. The compressed output of the two sub-ESTM neural networks is used as the input of another LSTM neural network. The multilayer regression algorithm is adopted to randomly sample some network nodes to obtain the cognitive information of the nodes sampled in the entire network, predict the relationship between all nodes in the network based on the cognitive information, and perform low sampling to achieve relationship prediction. The experiments show that proposed LSTM model together with CNN feature vectors can outperform other algorithms.
Regularized evolutionary population-based training

Metalearning of deep neural network (DNN) architectures and hyperparameters has become an increasingly important area of research. At the same time, network regularization has been recognized as a crucial dimension to effective training of DNNs. However, the role of metalearning in establishing effective regularization has not yet been fully explored. There is recent evidence that loss-function optimization could play this role, however it is computationally impractical as an outer loop to full training. This paper presents an algorithm called Evolutionary Population-Based Training (EPBT) that interleaves the training of a DNN's weights with the metalearning of loss functions. They are parameterized using multivariate Taylor expansions that EPBT can directly optimize. Such simultaneous adaptation of weights and loss functions can be deceptive, and therefore EPBT uses a quality-diversity heuristic called Novelty Pulsation as well as knowledge distillation to prevent overfitting during training. On the CIFAR-10 and SVHN image classification benchmarks, EPBT results in faster, more accurate learning. The discovered hyperparameters adapt to the training process and serve to regularize the learning task by discouraging overfitting to the labels. EPBT thus demonstrates a practical instantiation of regularization metalearning based on simultaneous training.
The Embodiment of an Older Avatar in a Virtual Reality Setting Impacts the Social Motivation of Young Adults

Socio-emotional selectivity theory implies that an individual's motives change over their lifespan, starting with a focus on information seeking and shifting toward the motivation of maintaining emotionally meaningful social relationships in old age. The concept of future time perspective serves as an underlying mechanism for this phenomenon.This study aimed to capture how social motivation changes as a result of the manipulation of one's own visual appearance. Thus, the explicit age stereotypes of N = 74 participants were assessed, among other covariates. The following intervention consisted of a virtual reality (VR) scenario in which the experimental group embodied an old age avatar and the control group a young age avatar.Changes in social motivation were assessed using the concept of socio-emotional selectivity based on imagined situational preferences. Results with strong effect sizes indicate that changes in social motivation commonly connected with old age might be caused by visual cues when actively embodying a virtual avatar.