Reviewerly/exHarmony huggingface dataset:
Have We Crossed the Uncanny Valley? Understanding Affinity, Trustworthiness, and Preference for Realistic Digital Humans in Immersive Environments

Developers have long strived to create virtual avatars that are more realistic because they are believed to be preferred over less realistic avatars. However, an “uncanny valley” exists in which avatars trigger aversion when they are almost but not quite realistic. We used a field study to investigate whether users had different affinity, trustworthiness, and preferences for avatars with two levels of realism, one that was close to human-realistic and one a cartoon caricature. We observed behavior, conducted one-on-one interviews, and collected survey data from SIGGRAPH conference attendees who either participated in a live discussion session between two avatars in a VR environment, or observed it via 3D VR headsets or on a large screen 2D video display. Eighteen sessions were conducted over four days, with the same person animating the human-realistic avatar and different guests animating the caricature avatars. The guests who interacted with the human-realistic avatar had a positive experience in the VR environment. The observers had positive evaluations of both avatars while acknowledging their different levels of realism. They rated the human-realistic avatar as more trustworthy, had more affinity for it, and preferred it as a virtual agent. Participants who observed the interview through VR headsets had an even stronger affinity for the human-realistic avatar and stronger preferences for it than those who observed via the 2D screen. Effect sizes ranged from medium to large. Our results suggest that it is now possible to cross the uncanny valley with human-realistic avatars rendered in real time.
Measurement Cost of Metric-Aware Variational Quantum Algorithms

Variational quantum algorithms are promising tools for near-term quantum computers as their shallow circuits are robust to experimental imperfections. Their practical applicability, however, strongly depends on how many times their circuits need to be executed for sufficiently reducing shot-noise. We consider metric-aware quantum algorithms: variational algorithms that use a quantum computer to efficiently estimate both a matrix and a vector object. For example, the recently introduced quantum natural gradient approach uses the quantum Fisher information matrix as a metric tensor to correct the gradient vector for the co-dependence of the circuit parameters. We rigorously characterise and upper bound the number of measurements required to determine an iteration step to a fixed precision, and propose a general approach for optimally distributing samples between matrix and vector entries. Finally, we establish that the number of circuit repetitions needed for estimating the quantum Fisher information matrix is asymptotically negligible for an increasing number of iterations and qubits.
Blockchain Adoption for Sustainable Supply Chain Management: Economic, Environmental, and Social Perspectives

Due to the rapid increase in environmental degradation and depletion of natural resources, the focus of researchers is shifted from economic to socio-environmental problems. Blockchain is a disruptive technology that has the potential to restructure the entire supply chain for sustainable practices. Blockchain is a distributed ledger that provides a digital database for recording all the transactions of the supply chain. The main purpose of this research is to explore the literature relevant to blockchain for sustainable supply chain management. The focus of this review is on the sustainability of the blockchain-based supply chain concerning environmental conservation, social equality, and governance effectiveness. Using a systematic literature review, a total of 136 articles were evaluated and categorized according to the triple bottom-line aspects of sustainability. Challenges and barriers during blockchain adoption in different industrial sectors such as aviation, shipping, agriculture and food, manufacturing, automotive, pharmaceutical, and textile industries were critically examined. This study has not only explored the economic, environmental, and social impacts of blockchain but also highlighted the emerging trends in a circular supply chain with current developments of advanced technologies along with their critical success factors. Furthermore, research areas and gaps in the existing research are discussed, and future research directions are suggested. The findings of this study show that blockchain has the potential to revolutionize the entire supply chain from a sustainability perspective. Blockchain will not only improve the economic sustainability of the supply chain through effective traceability, enhanced visibility through information sharing, transparency in processes, and decentralization of the entire structure but also will help in achieving environmental and social sustainability through resource efficiency, accountability, smart contracts, trust development, and fraud prevention. The study will be helpful for managers and practitioners to understand the procedure of blockchain adoption and to increase the probability of its successful implementation to develop a sustainable supply chain network.
A neural network solves, explains, and generates university math problems by program synthesis and few-shot learning at human level

We demonstrate that a neural network pretrained on text and fine-tuned on code solves mathematics course problems, explains solutions, and generates questions at a human level. We automatically synthesize programs using few-shot learning and OpenAI's Codex transformer and execute them to solve course problems at 81% automatic accuracy. We curate a dataset of questions from Massachusetts Institute of Technology (MIT)'s largest mathematics courses (Single Variable and Multivariable Calculus, Differential Equations, Introduction to Probability and Statistics, Linear Algebra, and Mathematics for Computer Science) and Columbia University's Computational Linear Algebra. We solve questions from a MATH dataset (on Prealgebra, Algebra, Counting and Probability, Intermediate Algebra, Number Theory, and Precalculus), the latest benchmark of advanced mathematics problems designed to assess mathematical reasoning. We randomly sample questions and generate solutions with multiple modalities, including numbers, equations, and plots. The latest GPT-3 language model pretrained on text automatically solves only 18.8% of these university questions using zero-shot learning and 30.8% using few-shot learning and the most recent chain of thought prompting. In contrast, program synthesis with few-shot learning using Codex fine-tuned on code generates programs that automatically solve 81% of these questions. Our approach improves the previous state-of-the-art automatic solution accuracy on the benchmark topics from 8.8 to 81.1%. We perform a survey to evaluate the quality and difficulty of generated questions. This work automatically solves university-level mathematics course questions at a human level and explains and generates university-level mathematics course questions at scale, a milestone for higher education.
Recovering Implicit Thread Structure in Newsgroup Style Conversations

On-line discussions are composed of multiple inter-woven threads, regardless of whether that threaded structure is made explicit in the representation and presentation of the conversational data. Recovering the thread structure is valuable since it makes it possible to isolate discussion related to specific subtopics or related to particular conversational goals. In prior work, thread structure has been reconstructed using explicit meta-data features such as posted by and reply to relationships. The contribution of this paper is a novel approach to recovering thread structure in discussion forums where this explicit meta-data is missing. This approach uses a graph-based representation of a collection of messages where connections between messages are postulated based on inter-message similarity. We evaluate three variations of this simple baseline approach that exploit in different ways the temporal relationships between messages. The results show that the three proposed approaches outperform the simple threshold-cut baseline.
A new pattern classification improvement method with local quality matrix based on K-NN

In order to pursue the high accuracy in classification task, we propose a new pattern classification accuracy improvement (CAI) method working with local quality matrix. The quality matrix expresses the conditional probability of the object belonging to one class when classified to another class, and it is estimated based on the K-nearest neighbors (K-NN) of object. The K-NN selected from the labeled data set are classified using a given classifier, and their classification results represented by either probability or belief functions could be modified using a quality matrix to make them as close as possible to the ground truth. Hence, the quality matrix can be estimated by minimizing the sum of distances between the modified classification results of the K-NN and the truth. In the optimization procedure, the K-NN are considered with different weights depending on their distances to the object. The smaller distance, the bigger weight. This is to control the influence of the neighbors far from object. The classification result of each object will be corrected by the corresponding optimized quality matrix via an appropriate probability redistribution way. The performance of CAI method is tested with respect to other related methods using some real data sets. The experimental results show that CAI substantially improves the classification accuracy, and it is robust to the choice of K value.
Research on a learning rate with energy index in deep learning

The stochastic gradient descent algorithm (SGD) is the main optimization solution in deep learning. The performance of SGD depends critically on how learning rates are tuned over time. In this paper, we propose a novel energy index based optimization method (EIOM) to automatically adjust the learning rate in the backpropagation. Since a frequently occurring feature is more important than a rarely occurring feature, we update the features to different extents according to their frequencies. We first define an energy neuron model and then design an energy index to describe the frequency of a feature. The learning rate is taken as a hyperparameter function according to the energy index. To empirically evaluate the EIOM, we investigate different optimizers with three popular machine learning models: logistic regression, multilayer perceptron, and convolutional neural network. The experiments demonstrate the promising performance of the proposed EIOM compared with that of other optimization algorithms.
Adaptive null interferometric test using spatial light modulator for free-form surfaces

We report a method of using a liquid-crystal spatial light modulator (LC-SLM) as reconfigurable multi-level interferogram-type computer generated holograms (ICGHs) to perform dynamic null tests for aspheric and free-form surfaces. With the proposed multi-level ICGHs encoding method, amplitude and accuracy of the applicable aberration of LC-SLMs are both suitable for interferometric test. No other equipment is required to monitor the dynamic phase of LC-SLM for guaranteeing test accuracy. Moreover, complicated phase response calibration of the LC-SLM is not required. Besides being used in collimated beams, the LC-SLM is demonstrated for the first time to be used in divergent beams; hence, concave surfaces with apertures larger than that of the LC-SLMs can be tested. For realizing practical tests, the calibration of inherit wavefront distortion of the LC-SLM, diffraction orders isolation, and alignment are analyzed in detail. Two free-form surfaces with about 20 μm departure from flat and spherical surfaces are successfully measured in collimated beam and divergent beam, respectively. Cross tests are provided to verify the test accuracy.
The Disruptive Blockchain: Types, Platforms and Applications

Organizations with high motivation for growth and cost-effective operation efficiencies, are always trying to bring new technologies to their operations.These organizations are very sensitive to change and value driven thus constant change is the only law for them to achieve their goals and to be in the market.When Bitcoin jumps into the market, the whole world wanted to own it but now after Bitcoin and Cryptocurrency bubble, there are significant shift towards Blockchain related products, services, solution developments, researches and use-case studies.From technical-financial opportunist, to evangelist, to researchers, to Tech enterprises, to financial institutions, to governments, the whole world is behind Blockchain and now it has the technological spot light.Blockchain has left behind all other technologies as far as research initiatives, investments and financial funding are concern.There are substantial research growth on, how Blockchain can be useful in specific area?Objectives of this paper are to highlight some facts about Blockchain that were misinterpreted and misrepresented due to this sudden shift.In this research article, authors are presenting comprehensive literature review of Blockchain Technologies and its applications in various sectors.Our research supports that Blockchain is revolutionizing and disrupting organizations across all industries.Blockchain is really a next big technological invention after Internet.In Blockchain Code is the law and Smart Contracts are the new way of doing business.
DeepCompare: Visual and Interactive Comparison of Deep Learning Model Performance

Deep learning models have become the state-of-the-art for many tasks, from text sentiment analysis to facial image recognition. However, understanding why certain models perform better than others or how one model learns differently than another is often difficult yet critical for increasing their effectiveness, improving prediction accuracy, and enabling fairness. Traditional methods for comparing models' efficacy, such as accuracy, precision, and recall provide a quantitative view of performance; however, the qualitative intricacies of why one model performs better than another are hidden. In this paper, we interview machine learning practitioners to understand their evaluation and comparison workflow. From there, we iteratively design a visual analytic approach, DeepCompare, to systematically compare the results of deep learning models, in order to provide insight into the model behavior and interactively assess tradeoffs between two such models. The tool allows users to evaluate model results, identify and compare activation patterns for misclassifications, and link the test results back to specific neurons. We conduct a preliminary evaluation through two real-world case studies to show that experts can make more informed decisions about the effectiveness of different types of models, understand in more detail the strengths and weaknesses of the models, and holistically evaluate the behavior of the models.
Vehicle Detection in Aerial Images

The detection of vehicles in aerial images is widely applied in many applications.Comparing with object detection in the ground view images, vehicle detection in aerial images remains a challenging problem because of small vehicle size and the complex background.In this paper, we propose a novel double focal loss convolutional neural network (DFL-CNN) framework.In the proposed framework, the skip connection is used in the CNN structure to enhance the feature learning.Also, the focal loss function is used to substitute for conventional cross entropy loss function in both of the region proposal network (RPN) and the final classifier.We further introduce the first large-scale vehicle detection dataset ITCVD with ground truth annotations for all the vehicles in the scene.We demonstrate the performance of our model on the existing benchmark German Aerospace Center (DLR) 3K dataset as well as the ITCVD dataset.The experimental results show that our DFL-CNN outperforms the baselines on vehicle detection.
Full-Gradient Representation for Neural Network Visualization

We introduce a new tool for interpreting neural nets, namely full-gradients, which decomposes the neural net response into input sensitivity and per-neuron sensitivity components. This is the first proposed representation which satisfies two key properties: completeness and weak dependence, which provably cannot be satisfied by any saliency map-based interpretability method. Using full-gradients, we also propose an approximate saliency map representation for convolutional nets dubbed FullGrad, obtained by aggregating the full-gradient components. We experimentally evaluate the usefulness of FullGrad in explaining model behaviour with two quantitative tests: pixel perturbation and remove-and-retrain. Our experiments reveal that our method explains model behavior correctly, and more comprehensively than other methods in the literature. Visual inspection also reveals that our saliency maps are sharper and more tightly confined to object regions than other methods.
Characterizing Dynamic Load Balancing in Cloud Environments Using Virtual Machine Deployment Models

The ever growing computational demands of users call for efficient cloud resource management to avoid service-level agreement (SLA) violation.Virtualization co-locates multiple virtual machines (VMs) on a single physical server to share the underlying resources for efficient resource management.However, the decision about ''what'' and ''where'' to place workloads significantly impacts performance of hosted workloads.Existing cloud schedulers consider a single resource (RAM) to co-locate workloads that as a result lead to SLA violation due to non-optimal VM placement.To handle this issue, current study has updated nova scheduler to propose a multi-resource based VM placement approach to improve application performance in terms of central processing unit (CPU) utilization and execution time.Experimentally we have shown that our proposed method has lessened application execution time by 50% when compared with one of the well-known technique.
Curriculum Loss: Robust Learning and Generalization against Label Corruption

Deep neural networks (DNNs) have great expressive power, which can even memorize samples with wrong labels. It is vitally important to reiterate robustness and generalization in DNNs against label corruption. To this end, this paper studies the 0-1 loss, which has a monotonic relationship between empirical adversary (reweighted) risk (Hu et al. 2018). Although the 0-1 loss is robust to outliers, it is also difficult to optimize. To efficiently optimize the 0-1 loss while keeping its robust properties, we propose a very simple and efficient loss, i.e. curriculum loss (CL). Our CL is a tighter upper bound of the 0-1 loss compared with conventional summation based surrogate losses. Moreover, CL can adaptively select samples for stagewise training. As a result, our loss can be deemed as a novel perspective of curriculum sample selection strategy, which bridges a connection between curriculum learning and robust learning. Experimental results on noisy MNIST, CIFAR10 and CIFAR100 dataset validate the robustness of the proposed loss.
Blind channel estimation for continuous‐variable quantum key distribution

For the maximum-likelihood parameter estimation (MPE) of continuous-variable quantum key distribution (CVQKD), it sacrifices partial key when estimating the channel parameters, and when the channel is varying, even slowly, legitimate communication parties must constantly exchange partial input signal and output signal to update the channel estimation. Therefore, the MPE leads to the following problems in practical application: (a) MPE achieved by randomly sampling the states can not accurately reflect the dynamic channel; (b) there exist a trade-off between the secret key rate (SKR) and the accuracy of parameter estimation in the finite-size regime; and (c) information leakage may be caused by the correlation between key variables. In this article, a novel parameter estimation method for CVQKD is proposed without affecting the security, and the consistency between the method and the physical model is given. Experimental simulation results show that the method can effectively improve the SKR and secure propagation distance. The experimental verification of the proposed method is given through the fiber system. Besides, the security analysis demonstrates that the method is secure against entanglement-distillation attack.
The SIGMORPHON 2020 Shared Task on Multilingual Grapheme-to-Phoneme Conversion

We describe the design and findings of the SIGMORPHON 2020 shared task on multilingual grapheme-to-phoneme conversion.Participants were asked to submit systems which consume a sequence of graphemes then emit output a sequence of phonemes representing the pronunciation of that grapheme sequence in one of fifteen languages.Nine teams submitted a total of 23 systems, at best achieving an 18% relative reduction in word error rate (macro-averaged over languages), versus strong neural sequence-to-sequence baselines.To facilitate error analysis, we publicly release the complete outputs for all systems-a first for the SIGMORPHON workshop.
Balancing Personal Privacy and Public Safety During COVID-19: The Case of South Korea

There has been vigorous debate on how different countries responded to the COVID-19 pandemic. To secure public safety, South Korea actively used personal information at the risk of personal privacy whereas France encouraged voluntary cooperation at the risk of public safety. In this article, after a brief comparison of contextual differences with France, we focus on South Korea's approaches to epidemiological investigations. To evaluate the issues pertaining to personal privacy and public health, we examine the usage patterns of original data, de-identification data, and encrypted data. Our specific proposal discusses the COVID index, which considers collective infection, outbreak intensity, availability of medical infrastructure, and the death rate. Finally, we summarize the findings and lessons for future research and the policy implications.
How Does BERT Answer Questions?: A Layer-Wise Analysis of Transformer Representations

Bidirectional Encoder Representations from Transformers (BERT) reach state-of-the-art results in a variety of Natural Language Processing tasks. However, understanding of their internal functioning is still insufficient and unsatisfactory. In order to better understand BERT and other Transformer-based models, we present a layer-wise analysis of BERT's hidden states. Unlike previous research, which mainly focuses on explaining Transformer models by their attention weights, we argue that hidden states contain equally valuable information. Specifically, our analysis focuses on models fine-tuned on the task of Question Answering (QA) as an example of a complex downstream task. We inspect how QA models transform token vectors in order to find the correct answer. To this end, we apply a set of general and QA-specific probing tasks that reveal the information stored in each representation layer. Our qualitative analysis of hidden state visualizations provides additional insights into BERT's reasoning process. Our results show that the transformations within BERT go through phases that are related to traditional pipeline tasks. The system can therefore implicitly incorporate task-specific information into its token representations. Furthermore, our analysis reveals that fine-tuning has little impact on the models' semantic abilities and that prediction errors can be recognized in the vector representations of even early layers.