Reviewerly/exHarmony huggingface dataset:
The development of Latin alphabet identity markers: A comparison among three Romance graphemes

This study aims to broaden the knowledge of graphic ideologies in Latin alphabetical spelling. Even in a script considered extremely analytical as the Latin alphabet, a graphic character alone or a part of it can possess wide and complete communicative messages of social meaning. Three graphemes belonging to three Romance languages, specifically the Spanish <ñ>, Italian <gli> and Romanian <â>, are analyzed from grapholinguistic, historical-linguistic and sociolinguistic perspectives. This investigation considers the relationship with Latin and other Romance or neighbor languages’ graphemes. Furthermore, it examines the presence of the target graphemes in identity words and in brand logos. I attempt to verify whether or not the attribution and branding processes as related to these three alphabetical graphemes have been achieved. The results for the single graphemes are compared, and it is noted that in just one case, the grapheme or its diacritical mark can semantically represent the language and the speakers’ community that recognize and use it.
Data Sharing Attitudes and Practices in the Plant Sciences: Results from a Mixed Method Study

Data sharing attitudes and practices of the faculty of the plant sciences departments at the University of Minnesota-Twin Cities were explored in a mixed method study consisting of an online survey and semi-structured in-depth interviews. Results show the majority of participating faculty responded positively regarding data sharing and practice it in some form. However, several challenges exist, such as the time needed to prepare metadata, long-term storage and preservation, collaboration and communication with fellow researchers, and gaps in coding skill. Librarians are uniquely suited to help researchers face these challenges and support increased data sharing.
Investigating the Potential Impact of Values on Requirements and Software Engineering

This paper describes an investigation into value-based software engineering and proposes a comprehensive value taxonomy with interpretation of design feature implications. The value taxonomy is used to assess the design of Covid-19 symptom tracker applications, contrasting the UK's NHS phase 1 and 2 designs which adopted centralized, then decentralized, architectures. The value/feature analysis is also applied to the King's/Zoe Covid app which does not detect proximity, instead relying on user self-reporting. Value analysis illuminated design choices but was insufficient to account for download acceptance of the apps. We argue that motivational cost-benefit analysis needs to complement a values-based approach.
Stratified Multi-Task Learning for Robust Spotting of Scene Texts

Gaining control over the dynamics of multi-task learning should help to unlock the potential of the deep network to a great extent. In the existing multi-task learning (MTL) approaches of deep network, all the parameters of its feature encoding part are subjected to adjustments corresponding to each of the underlying sub-tasks. On the other hand, different functional areas of the human brain are responsible for distinct functions such as the Broca's area of the cerebrum is responsible for speech formation whereas its Wernicke's area is related to the language development etc. Inspired by this fact, in the present study, we used here a Feature Representation Block (FRB) of connection weights spanned over a few successive layers of a deep multi-task learning architecture and stratify the same into distinct subsets for their adjustments exclusively corresponding to different sub-tasks. Additionally, we have introduced a novel regularization component for controlled training of this Feature Representation Block. The purpose of the development of this learning framework is efficient end-to-end recognition of scene texts. Simulation results of the proposed strategy on various benchmark scene text datasets such as ICDAR 2015, ICDAR 2017 MLT, COCO-Text and MSRA-TD500 have improved respective SOTA performance.
Contrastive Self-supervised Representation Learning Using Synthetic Data

Abstract Learning discriminative representations with deep neural networks often relies on massive labeled data, which is expensive and difficult to obtain in many real scenarios. As an alternative, self-supervised learning that leverages input itself as supervision is strongly preferred for its soaring performance on visual representation learning. This paper introduces a contrastive self-supervised framework for learning generalizable representations on the synthetic data that can be obtained easily with complete controllability. Specifically, we propose to optimize a contrastive learning task and a physical property prediction task simultaneously. Given the synthetic scene, the first task aims to maximize agreement between a pair of synthetic images generated by our proposed view sampling module, while the second task aims to predict three physical property maps, i.e., depth, instance contour maps, and surface normal maps. In addition, a feature-level domain adaptation technique with adversarial training is applied to reduce the domain difference between the realistic and the synthetic data. Experiments demonstrate that our proposed method achieves state-of-the-art performance on several visual recognition datasets.
Semantic Collaborative Filtering Recommender System Using CNNs

According to the expansion of users and the variety of products in the World Wide Web, users have been surrounded by a huge amount of data and information, so without proper guidance and navigation, they may make wrong or non-optimal choices. Recommender systems (RS) are useful in guiding the user to reach his/her favorite option among a huge volume of possible choices, so this process is specific to that user. Collaborative Filtering (CF) recommender system is one of the most popular approaches that exploits information about the past behavior or the opinions of an existing user community for predicting which items the current user of the system will most probably like or dislike. This paper improves Item-based collaborative filtering recommender system by finding similar items based on their content. Our main objectives are to solve the item cold start problem and to improve the quality of user's recommended list, or in other words, to improve the items ranking. To achieve these goals, the latent semantic features of the items have been first extracted using a Convolutional Neural Network (CNN) and, then the semantic similarity between the items has been calculated and finally used in the numerical prediction, indicating to what degree the current user will like or be interested in a certain item. Our experiments on the Jester Dataset3 and Jester Dataset4 show that the proposed method has not only been effective in solving the above two problems but has also improved the ratings prediction accuracy and the recommendation quality.
Adversarial Attacks on Audio Source Separation

Despite the excellent performance of neural-network-based audio source separation methods and their wide range of applications, their robustness against intentional attacks has been largely neglected. In this work, we reformulate various adversarial attack methods for the audio source separation problem and intensively investigate them under different attack conditions and target models. We further propose a simple yet effective regularization method to obtain imperceptible adversarial noise while maximizing the impact on separation quality with low computational complexity. Experimental results show that it is possible to largely degrade the separation quality by adding imperceptibly small noise when the noise is crafted for the target model. We also show the robustness of source separation models against a black-box attack. This study provides potentially useful insights for developing content protection methods against the abuse of separated signals and improving the separation performance and robustness.
Deep Partitioned Training From Near-Storage Computing to DNN Accelerators

In this letter, we present deep partitioned training to accelerate computations involved in training DNN models. This is the first work that partitions a DNN model across storage devices, an NPU and a host CPU forming a unified compute node for training workloads. To validate the benefit of using the proposed system during DNN training, a trace-based simulator or an FPGA prototype is used to estimate the overall performance and obtain the layer index to be partitioned that provides the minimum latency. As a case study, we select two benchmarks, i.e., vision-related tasks and a recommendation system. As a result, the training time reduces by 12.2 ~ 31.0 percent with four near-storage computing devices in vision-related tasks with a mini-batch size of 512 and 40.6 ~ 44.7 percent with one near-storage computing device in the selected recommendation system with a mini-batch size of 64.
Enhancing Deep Paraphrase Identification via Leveraging Word Alignment Information

Recent deep learning based methods have achieved impressive performance on paraphrase identification (PI), a fundamental NLP task, judging whether two sentences are semantically equivalent or not. However, their success heavily relies on massive labeled samples, which are time-consuming and expensive to obtain. To alleviate this problem, this study explores the effect of word alignment information (WAI), extracted by existing monolingual alignment tools, on deep PI baseline models. Apart from directly encoding WAI into fixed-size embeddings, we propose a novel auxiliary task so that the baselines can be pre-trained using a large amount of unlabeled in-domain data. Moreover, our proposed auxiliary task can also jointly train with the baselines, aiming to eliminate the overheads of preprocessing WAI at the test period. Experimental results verify that our methods can significantly outperform the deep PI baseline model.
Experimental evaluation of deep learning method in reticulocyte enumeration in peripheral blood

Abstract Introduction Reticulocytes (RET) are immature red blood cells, and RET enumeration in peripheral blood has important clinical value in diagnosis, treatment efficacy observation, and prognosis of anemic diseases. For RET enumeration, flow cytometric reference method has shown to be more precise than the manual method by light microscopy. However, flow cytometric method generates occasionally spurious RET counts in some situations. The manual method, which is subjective, imprecise, and tedious, currently remains as an accepted reference method. As a result, there is a need for manual method to be more objective, precise, and rapid. Methods 40 EDTA‐K2 anticoagulated whole blood samples were randomly selected for the study. 784 microscopic images were taken from blood slides as dataset, and all mature RBCs and RETs in these images were located and labeled by experienced experts. Then, we leverage a Faster R‐CNN deep neural network to train a RET detection model and evaluate the model. Results Both the recall and precision rate of the model are more than 97%, and average analysis time of a single image is 0.21 seconds. Conclusion The deep learning method shows outstanding performance including high accuracy and fast speed. The experimental results show that the deep learning method holds the potential to act as a rapid computer‐aid method for manual RET enumeration for cytological examiners.
Research on digital copyright blockchain technology

As a decentralized network structure based on P2P network and with cryptography as its core, blockchain technology establishes trust relationship on the network by pure mathematical method without relying on intermediate platform. There are three problems in traditional digital copyright industry: difficult to confirm the rights, difficult to authorize rights and difficult to maintain rights. This paper makes a deep research on the blockchain technology of digital copyright. With the help of self-supervision, traceability and decentralization of blockchain technology, as well as the Map function, we can improve the data transmission rate in the multi-channel model and obtain accurate time complexity values. Hence the probability of all kinds of digital copyright infringement events is greatly reduced.
Local estimators and Bayesian inverse problems with non-unique solutions

Bayesian approach is effective for inverse problems. The posterior density distribution provides useful information of the unknowns. However, for problems with non-unique solutions, the classical estimators such as the maximum a posterior (MAP) and conditional mean (CM) are not suitable. We introduce two new estimators, the local maximum a posterior (LMAP) and local conditional mean (LCM). A simple algorithm based on clustering to compute LMAP and LCM is proposed. Their applications are demonstrated by three inverse problems: an inverse spectral problem, an inverse source problem, and an inverse medium problem.
Cautious Reinforcement Learning via Distributional Risk in the Dual Domain

We study the estimation of risk-sensitive policies in reinforcement learning (RL) problems defined by a Markov Decision Process (MDPs) whose state and action spaces are countably finite. Prior efforts are predominately afflicted by computational challenges associated with the fact that risk-sensitive MDPs are time-inconsistent, and hence modifications of Bellman's equations are required, which often do not permit efficient fixed point schemes. To ameliorate this issue, we propose a new definition of risk, which we call caution, as a penalty function added to the dual of the linear programming (LP) formulation of tabular RL. Caution quantifies the distributional risk of a policy, and is a function of the policy's long-term state occupancy measure. When the risk is a convex function of the occupancy measure, we propose to solve this problem in an online model-free manner with a stochastic variant of primal-dual method that uses Kullback-Lieber (KL) divergence as its proximal term. We establish that the sample complexity required to attain near-optimal solutions matches tight dependencies on the cardinality of the spaces, but also depends on the infinity norm of the risk measure's gradient. Moreover, when the risk is non-convex, we propose a block-coordinate augmentation of the aforementioned approach, and establish its convergence to KKT points. Experiments demonstrate that this approach improves the reliability of reward accumulation without additional computation as compared to risk-neutral LP solvers, both for convex and non-convex risks, respectively, for the cases of KL divergence to a prior, and the variance.
Template matching for simple waveforms with low signal-to-noise ratio and its application to icequake detection*

Template matching is a useful method to detect seismic events through waveform similarity between two signals. The traditional template matching method works well in detecting small tectonic earthquakes. However, the method has some difficulty when the signals have relatively low signal-to-noise ratios (SNRs) and simple shapes, e. g. a sinusoidal function. In this study, we modify the traditional template matching approach for this situation. We first construct a virtual three-component seismic station using vertical-component waveforms recorded by three stations. Next, we select a template event from the virtual station, and apply the traditional template matching. We then verify this method by detecting icequakes with simple waveforms on the Urumqi Glacier No. 1 and compare the results with those from the short-term-averages over long-term-average (STA/LTA), the REST method, and traditional template matching method. It can be concluded that the modified template matching method using virtual stations has some advantages for seismic data with low SNRs.
User Naming Conventions Mapping Learning for Social Network Alignment

The existing research on social network alignment using usernames is mainly based on the similarity between usernames calculated by different classifiers. However, if the number of available annotations and training time are limited and feature extraction is incomplete, the accuracy of social network alignment would have been be reduced. Based on the above, this paper proposes a BP neural network mapping for social network alignment (BSNA). The BP neural network is used to realize the mapping between two social network user name vectors, and the classification problem is transformed into a mapping problem between vectors. The experimental results on several social network data sets show that compared with the benchmark method, the social network alignment precision of the proposed model is improved by 4%, and the experiments with smaller training set ratio and less training time have higher precision and faster convergence than the benchmark method.
Automating Cancer Genetic Counseling with an Adaptive Pedagogical Agent

Effective communication of genetic risk is increasingly important for prevention and treatment of hereditary cancer syndromes. Many individuals do not have access to genetic counselors, or lack the ability to understand and act on genetic risks, due to the complexity of the information. Automated approaches that incorporate animated pedagogical conversational agents may address these barriers. We describe a pedagogical agent that functions in the role of a virtual genetic counselor that discusses hereditary breast cancer risk and motivates women to obtain breast cancer screening. We report the design and evaluation of two prototypes of the virtual counselor. Our results demonstrate the feasibility of this approach, and the effectiveness in improving breast cancer genetics knowledge, by adapting the virtual counselor's pedagogical strategies to an individual's comprehension based on dynamic assessments and preferences.
A research on foot size measurement algorithm based on image

Abstract Foot size measurement has a wide range of applications. It can not only be used in medical foot health, evaluate the health index of the foot, improve the health of the human foot, but also provide a large amount of foot data for the study of kinematics. Due to the limitations of measurement equipment and related algorithms, 3D foot measurement cannot be widely used. We proposed a fast and convenient foot measurement method based on deep learning and image segmentation algorithms. The HED model is used to detect the edges of A4 paper; the traditional image segmentation algorithm GrabCut is used for segmentation of the foot area. The results show that the algorithm proposed in this paper has a faster measurement speed and higher accuracy for foot size measurement.
Feature Integration via Semi-Supervised Ordinally Multi-Modal Gaussian Process Latent Variable Model

This paper presents a method of feature integration via semi-supervised ordinally multi-modal Gaussian process latent variable model (Semi-OMGP). The proposed method transforms multimodal features into common latent variables suitable for users' interest level estimation. For dealing with the multi-modal features, the proposed method newly derives Semi-OMGP. Semi-OMGP has two contributions. First, Semi-OMGP is suitable for integration between heterogeneous modalities with different distributions by assuming that the similarity matrices of these modalities as observations are generated from latent variables. Second, Semi-OMGP can efficiently use label information by introducing an operator considering the ordinal grade into the prior distribution of latent variables when obtained label information is partially given. Semi-OMGP can simultaneously realize the above contributions, and successful multi-modal feature integration becomes feasible. Experimental results show the effectiveness of the proposed method.
Machine Learning on Small UAVs

Commonly, machine learning (ML) workflows for training and inferencing occur in resource rich environments. Draper Laboratory is pushing ML to the edge. This paper shows the concept of operations (CONOPs), design parameters, and constraints the team faced for edge implementation. The overarching requirement is to fully integrate the machine learning element into the small unmanned aerial vehicle (UAV) or drone. Given the limited payload capacity and power available on small UAVs, integration of computing resources sufficient to host both ML and Autonomy functions is a challenge. Past efforts have relied on an Intel NUC as the primary processing unit. However, recent advances in GPUs provide greater computational power at low-SWaP, compatibility with ML algorithms, and sufficient CPU resources to host the UAVs autonomy element. More recently developed processing units, designed specifically for ML applications at the edge, enable scaled down variants of the algorithms for integration onto significantly smaller platforms. In this paper, we identify a common software architecting strategy that enables a micro UAV (~ 150 grams) supported by a traditional CPU and a small UAV (3 kg) configured with a GPU. Draper's automation strategy leverages the open-source Robotic Operating System (ROS). The ML models were built using open-source Python Pytorch libraries. We provide the flight test results for a vehicle detection algorithm. Future applications will include visual navigation and tracking.
Corona Beat - Kicking The Sedentary Habit Induced By Prolonged Social Distancing

Due to COVID-19 restrictions, most people have limited access to their typical physical activities. However, these activities play a significant role for human beings: improving cognitive, physical, and social well-being. We introduce "Corona Beat", a proof-of-concept augmented reality customizable mobile application promoting physical activity in home environments. We designed four movement-based game scenarios that players can perform with their mobile phones: torso rotation, isometric stretching, isometric squat, and randomized rotation. We encourage these movements using COVID-19 particles, which players must defeat. In the future work, game designers/clinicians may use this work to physically train people while informing them about COVID-19.
Nighttime Pedestrian Detection Based on Feature Attention and Transformation

Pedestrian detection at nighttime is an important yet challenging task, which is fundamental for many practical applications, e.g. autonomous driving, video surveillance. To address this problem, in this work we start with some analysis, from which we find that the nighttime features have much more noise than that of daytime, resulting in low discrimination ability. Besides, we also observe some pedestrian examples are under adverse illumination conditions, and they can hardly provide sufficient information for accurate detection. Based on these findings, we propose the Feature Attention Module (FAM) and Feature Transformation Module (FTM) to enhance nighttime features. In FAM, guided by progressive segmentation supervision, hierarchical feature attention is produced to enhance multilevel features. On the other hand, FTM is introduced to enforce features from adverse illumination to approach that from better illumination. Based on feature attention and transformation (FAT) mechanism, a two-stage detector called FATNet is constructed for nighttime pedestrian detection. We conduct extensive experiments on nighttime datasets of EuroCity Persons (Night) and NightOwls to demonstrate the effectiveness of our method. On both datasets, our method achieves significant improvements to the baseline and also outperforms state-of-the-art detectors.