Reviewerly/exHarmony huggingface dataset:
Accelerating deep learning with memcomputing

Restricted Boltzmann machines (RBMs) and their extensions, often called “deep-belief networks”, are powerful neural networks that have found applications in the fields of machine learning and artificial intelligence. The standard way to train these models resorts to an iterative unsupervised procedure based on Gibbs sampling, called “contrastive divergence”, and additional supervised tuning via back-propagation. However, this procedure has been shown not to follow any gradient and can lead to suboptimal solutions. In this paper, we show an efficient alternative to contrastive divergence by means of simulations of digital memcomputing machines (DMMs) that compute the gradient of the log-likelihood involved in unsupervised training. We test our approach on pattern recognition using a modified version of the MNIST data set of hand-written numbers. DMMs sample effectively the vast phase space defined by the probability distribution of RBMs, and provide a good approximation close to the optimum. This efficient search significantly reduces the number of generative pretraining iterations necessary to achieve a given level of accuracy in the MNIST data set, as well as a total performance gain over the traditional approaches. In fact, the acceleration of the pretraining achieved by simulating DMMs is comparable to, in number of iterations, the recently reported hardware application of the quantum annealing method on the same network and data set. Notably, however, DMMs perform far better than the reported quantum annealing results in terms of quality of the training. Finally, we also compare our method to recent advances in supervised training, like batch-normalization and rectifiers, that seem to reduce the advantage of pretraining. We find that the memcomputing method still maintains a quality advantage (>1% in accuracy, corresponding to a 20% reduction in error rate) over these approaches, despite the network pretrained with memcomputing defines a more non-convex landscape using sigmoidal activation functions without batch-normalization. Our approach is agnostic about the connectivity of the network. Therefore, it can be extended to train full Boltzmann machines, and even deep networks at once.
Connectionist Temporal Modeling of Video and Language: a Joint Model for Translation and Sign Labeling

Online sign interpretation suffers from challenges presented by hybrid semantics learning among sequential variations of visual representations, sign linguistics, and textual grammars. This paper proposes a Connectionist Temporal Modeling (CTM) network for sentence translation and sign labeling. To acquire short-term temporal correlations, a Temporal Convolution Pyramid (TCP) module is performed on 2D CNN features to realize (2D+1D)=pseudo 3D' CNN features. CTM aligns the pseudo 3D' with the original 3D CNN clip features and fuses them. Next, we implement a connectionist decoding scheme for long-term sequential learning. Here, we embed dynamic programming into the decoding scheme, which learns temporal mapping among features, sign labels, and the generated sentence directly. The solution using dynamic programming to sign labeling is considered as pseudo labels. Finally, we utilize the pseudo supervision cues in an end-to-end framework. A joint objective function is designed to measure feature correlation, entropy regularization on sign labeling, and probability maximization on sentence decoding. The experimental results using the RWTH-PHOENIX-Weather and USTC-CSL datasets demonstrate the effectiveness of the proposed approach.
Hierarchical Representation Learning for Bipartite Graphs

Recommender systems on E-Commerce platforms track users' online behaviors and recommend relevant items according to each user’s interests and needs. Bipartite graphs that capture both user/item feature and use-item interactions have been demonstrated to be highly effective for this purpose. Recently, graph neural network (GNN) has been successfully applied in representation of bipartite graphs in industrial recommender systems. Providing individualized recommendation on a dynamic platform with billions of users is extremely challenging. A key observation is that the users of an online E-Commerce platform can be naturally clustered into a set of communities. We propose to cluster the users into a set of communities and make recommendations based on the information of the users in the community collectively. More specifically, embeddings are assigned to the communities and the user embedding is decomposed into two parts, each of which captures the community-level generalizations and individualized preferences respectively. The community embedding can be considered as an enhancement to the GNN methods that are inherently flat and do not learn hierarchical representations of graphs. The performance of the proposed algorithm is demonstrated on a public dataset and a world-leading E-Commerce company dataset.
Selecting response anchors with equal intervals for summated rating scales.

Summated rating scales are ubiquitous in organizational research, and there are well-delineated guidelines for scale development (e.g., Hinkin, 1998). Nevertheless, there has been less research on the explicit selection of the response anchors. Constructing survey questions with equal-interval properties (i.e., interval or ratio data) is important if researchers plan to analyze their data using parametric statistics. As such, the primary objectives of the current study were to (a) determine the most common contexts in which summated rating scales are used (e.g., agreement, similarity, frequency, amount, and judgment), (b) determine the most commonly used anchors (e.g., strongly disagree, often, very good), and (c) provide empirical data on the conceptual distance between these anchors. We present the mean and standard deviation of scores for estimates of each anchor and the percentage of distribution overlap between the anchors. Our results provide researchers with data that can be used to guide the selection of verbal anchors with equal-interval properties so as to reduce measurement error and improve confidence in the results of subsequent analyses. We also conducted multiple empirical studies to examine the consequences of measuring constructs with unequal-interval anchors. A clear pattern of results is that correlations involving unequal-interval anchors are consistently weaker than correlations involving equal-interval anchors. (APA PsycInfo Database Record (c) 2020 APA, all rights reserved)
En-Ar Bilingual Word Embeddings without Word Alignment: Factors Effects

This paper introduces the first attempt to investigate morphological segmentation on En-Ar bilingual word embeddings using bilingual word embeddings model without word alignment (BilBOWA). We investigate the effect of sentence length and embedding size on the learning process. Our experiment shows that using the D3 segmentation scheme improves the accuracy of learning bilingual word embeddings up to 10 percentage points compared to the ATB and D0 schemes in all different training settings.
Examining the Potential of Blockchain Technology to Meet the Needs of 21st-Century Japanese Health Care: Viewpoint on Use Cases and Policy

Japan is undergoing a major population health transition as its society ages, and it continues to experience low birth rates. An aging Japan will bring new challenges to its public health system, highlighted as a model for universal health coverage (UHC) around the world. Specific challenges Japan’s health care system will face include an increase in national public health expenditures, higher demand for health care services, acute need for elder and long-term care, shortage of health care workers, and disparities between health care access in rural versus urban areas. Blockchain technology has the potential to address some of these challenges, but only if a health blockchain is conceptualized, designed, localized, and deployed in a way that is compatible with Japan’s centralized UHC-centric public health system. Blockchain solutions must also be adaptive to opportunities and barriers unique to Japan’s national health and innovation policy, including its regulatory sandbox system, while also seeking to learn from blockchain adoption in the private sector and in other countries. This viewpoint outlines the major opportunities and potential challenges to blockchain adoption for the future of Japan’s health care.
Multiclass Deep Active Learning for Detecting Red Blood Cell Subtypes in Brightfield Microscopy

The recent success of deep learning approaches relies partly on large amounts of well annotated training data. For natural images object annotation is easy and cheap. For biomedical images however, annotation crucially depends on the availability of a trained expert whose time is typically expensive and scarce. To ensure efficient annotation, only the most relevant objects should be presented to the expert. Currently, no approach exists that allows to select those for a multiclass detection problem. Here, we present an active learning framework that identifies the most relevant samples from a large set of not annotated data for further expert annotation. Applied to brightfield images of red blood cells with seven subtypes, we train a faster R-CNN for single cell identification and classification, calculate a novel confidence score using dropout variational inference and select relevant images for annotation based on (i) the confidence of the single cell detection and (ii) the rareness of the classes contained in the image. We show that our approach leads to a drastic increase of prediction accuracy with already few annotated images. Our original approach improves classification of red blood cell subtypes and speeds up the annotation. This important step in diagnosing blood diseases will profit from our framework as well as many other clinical challenges that suffer from the lack of annotated training data.
Towards Fast Adaptation of Neural Architectures with Meta Learning

Recently, Neural Architecture Search (NAS) has been successfully applied to multiple artificial intelligence areas and shows better performance compared with hand-designed networks. However, the existing NAS methods only target a specific task. Most of them usually do well in searching an architecture for single task but are troublesome for multiple datasets or multiple tasks. Generally, the architecture for a new task is either searched from scratch, which is neither efficient nor flexible enough for practical application scenarios, or borrowed from the ones searched on other tasks, which might be not optimal. In order to tackle the transferability of NAS and conduct fast adaptation of neural architectures, we propose a novel Transferable Neural Architecture Search method based on meta-learning in this paper, which is termed as T-NAS. T-NAS learns a meta-architecture that is able to adapt to a new task quickly through a few gradient steps, which makes the transferred architecture suitable for the specific task. Extensive experiments show that T-NAS achieves state-of-the-art performance in few-shot learning and comparable performance in supervised learning but with 50x less searching cost, which demonstrates the effectiveness of our method.
Robustness Verification for Transformers

Robustness verification that aims to formally certify the prediction behavior of neural networks has become an important tool for understanding the behavior of a given model and for obtaining safety guarantees. However, previous methods are usually limited to relatively simple neural networks. In this paper, we consider the robustness verification problem for Transformers. Transformers have complex self-attention layers that pose many challenges for verification, including cross-nonlinearity and cross-position dependency, which have not been discussed in previous work. We resolve these challenges and develop the first verification algorithm for Transformers. The certified robustness bounds computed by our method are significantly tighter than those by naive Interval Bound Propagation. These bounds also shed light on interpreting Transformers as they consistently reflect the importance of words in sentiment analysis.
Dual Embedding Learning for Video Instance Segmentation

In this paper, we propose a novel framework to generate high-quality segmentation results in a two-stage style, aiming at video instance segmentation task which requires simultaneous detection, segmentation and tracking of instances. To address this multi-task efficiently, we opt to first select high-quality detection proposals in each frame. The categories of the proposals are calibrated with the global context of video. Then, each selected proposal is extended temporally by a bi-directional Instance-Pixel Dual-Tracker (IPDT) which synchronizes the tracking on both instance-level and pixel-level. The instance-level module concentrates on distinguishing the target instance from other objects while the pixel-level module focuses more on the local feature of the instance. Our proposed method achieved a competitive result of mAP 45.0% on the Youtube-VOS dataset, ranking the 3rd in Track 2 of the 2nd Large-scale Video Object Segmentation Challenge.
Impact of Noisy Labels in Learning Techniques: A Survey

Noisy data isNigam, Nitika theDutta, Tanima mainGupta, Hari Prabhat issue in classification. The possible sources of noise label can be insufficient availability of information or encoding/communication problems, or data entry error by experts/nonexperts, etc., which can deteriorate the model’s performance and accuracy. However, in a real-world dataset, like Flickr, the likelihood of containing the noisy label is high. Initially, few methods such as identification, correcting, and elimination of noisy data was used to enhance the performance. Various machine learning algorithms are used to diminish the noisy environment, but in the recent studies, deep learning models are resolving this issue. In this survey, a brief introduction about the solution for the noisy label is provided.
MULE: Multimodal Universal Language Embedding

Existing vision-language methods typically support two languages at a time at most. In this paper, we present a modular approach which can easily be incorporated into existing vision-language methods in order to support many languages. We accomplish this by learning a single shared Multimodal Universal Language Embedding (MULE) which has been visually-semantically aligned across all languages. Then we learn to relate MULE to visual data as if it were a single language. Our method is not architecture specific, unlike prior work which typically learned separate branches for each language, enabling our approach to easily be adapted to many vision-language methods and tasks. Since MULE learns a single language branch in the multimodal model, we can also scale to support many languages, and languages with fewer annotations can take advantage of the good representation learned from other (more abundant) language data. We demonstrate the effectiveness of our embeddings on the bidirectional image-sentence retrieval task, supporting up to four languages in a single model. In addition, we show that Machine Translation can be used for data augmentation in multilingual learning, which, combined with MULE, improves mean recall by up to 20.2% on a single language compared to prior work, with the most significant gains seen on languages with relatively few annotations. Our code is publicly available1.
AR/VR Based Smart Policing For Fast Response to Crimes in Safe City

With advances in information and communication technologies, cities are getting smarter to enhance the quality of human life. In smart cities, safety (including security) is an essential issue. In this paper, by reviewing several safe city projects, smart city facilities for the safety are presented. With considering the facilities, a design for a crime intelligence system is introduced. Then, concentrating on how to support police activities (i.e., emergency call reporting reception, patrol activity, investigation activity, and arrest activity) with immersive technologies in order to reduce a crime rate and to quickly respond to emergencies in the safe city, smart policing with augmented reality (AR) and virtual reality (VR) is explained.
2M BeautyNet: Facial Beauty Prediction Based on Multi-Task Transfer Learning

Facial beauty prediction (FBP) has become an emerging area in the field of artificial intelligence. However, the lacks of data and accurate face representation hinder the development of FBP. Multi-task transfer learning can effectively avoid over-fitting, and utilize auxiliary information of related tasks to optimize the main task. In this paper, we present a network named Multi-input Multi-task Beauty Network (2M BeautyNet) and use transfer learning to predict facial beauty. In the experiment, beauty prediction is the main task, and gender recognition is the auxiliary. For multi-task training, we employ multi-task loss weights automatic learning strategy to improve the performance of FBP. Finally, we replace the softmax classifier with a random forest. We conduct experiments on the Large Scale Facial Beauty Database (LSFBD) and SCUT-FBP5500 database. Results show that our method has achieved good results on LSFBD, the accuracy of FBP is up to 68.23%. Our 2M BeautyNet structure is suitable for multiple inputs of different databases.
A hybrid multi‐objective tour route optimization algorithm based on particle swarm optimization and artificial bee colony optimization

Summary Computational intelligence techniques have widespread applications in the field of engineering process optimization, which typically comprises of multiple conflicting objectives. An efficient hybrid algorithm for solving multi‐objective optimization, based on particle swarm optimization (PSO) and artificial bee colony optimization (ABCO) has been proposed in this paper. The novelty of this algorithm lies in allocating random initial solutions to the scout bees in the ABCO phase which are subsequently optimized in the PSO phase with respect to the velocity vector. The last phase involves loyalty decision‐making for the uncommitted bees based on the waggle dance phase of ABCO. This procedure continues for multiple generations yielding optimum results. The algorithm is applied to a real life problem of intercity route optimization comprising of conflicting objectives like minimization of travel cost, maximization of the number of tourist spots visited and minimization of the deviation from desired tour duration. Solutions have been obtained using both pareto optimality and the classical weighted sum technique. The proposed algorithm, when compared analytically and graphically with the existing ABCO algorithm, has displayed consistently better performance for fitness values as well as for standard benchmark functions and performance metrics for convergence and coverage.
Output-Feedback Image-Based Visual Servoing for Multirotor Unmanned Aerial Vehicle Line Following

This article considers visual servoing-based motion control of multirotor unmanned aerial vehicles. We employ output feedback and image-based visual servoing to control the vehicle's pose with respect to a static planar visual target with a linear structure (e.g., electric transmission lines or pipelines). The method uses measurements from inexpensive sensors typically found on-board: an inertial measurement unit, and a monocular computer vision system. Unlike existing work, it does not require linear velocity, position measurements, or an optical flow sensor. The method directly controls the relative pose to the visual target and does not require global navigation satellite system measurements of the vehicle or target. The visual servoing method ensures the vehicle flies centered above the lines at specified height and yaw. Such motion control is important in a number of applications such as efficient data collection for infrastructure inspection. Our article exploits the inherent robustness of an image-based approach where feature error is computed directly in the image plane. A virtual camera is combined with output feedback and convergence of the closed loop is proven. The method is adaptive to vehicle mass, thrust constant, desired depth, and a constant disturbance force. Simulation and experimental results illustrate the method's performance and robustness to model uncertainty.
Two-stage particle filtering for non-Gaussian state estimation with fading measurements

In this paper, the filtering problem for systems with fading measurements is considered. Taking advantage of the cascaded structure of the system, the original filtering problem is decomposed into two subproblems: 1) recovery of the original measurements from the faded ones, and 2) state estimation based on the recovered signals. A two-stage particle filtering (PF) algorithm is proposed to achieve simultaneous measurement recovery and state estimation. In our scheme, the second-stage resampling procedure can be implemented concurrently, resulting in a significant reduction of execution time. Two examples are provided to demonstrate the effectiveness of our algorithm. A benchmark problem for nonlinear filtering is tackled in the first example and, in the second example, the proposed algorithm is applied to the object tracking problem where the measured signal is distorted by the communication channel. Simulation results show that, compared with the brute force PF, the two-stage PF can strike better balance between tracking accuracy and execution time.
New Caledonian crow learning algorithm: A new metaheuristic algorithm for solving continuous optimization problems

Several metaheuristic algorithms have been introduced to solve different optimization problems. Such algorithms are inspired by a wide range of natural phenomena or behaviors. We introduced a new metaheuristic algorithm called New Caledonian (NC) crow learning algorithm (NCCLA), inspired by efficient social, asocial, and reinforcement mechanisms that NC-crows use to learn behaviors for developing tools from Pandanus trees to obtain food. Such mechanisms were modeled mathematically to develop NCCLA, whose performance was subsequently evaluated and statistically analyzed using 23 classical benchmark functions and 4 engineering problems. The results verify NCCLA’s performance efficiency and highlight its accelerated convergence and ability to escape from local minima. An extensive comparative study was conducted to demonstrate that the solution accuracy and convergence rate of NCCLA were better than those of other state-of-the-art metaheuristics. The results also indicate that NCCLA is a promising algorithm that can be applied to solve other optimization and real-world problems.
Learning Decentralized Controllers for Robot Swarms with Graph Neural Networks

We consider the problem of finding distributed controllers for large networks of mobile robots with interacting dynamics and sparsely available communications. Our approach is to learn local controllers that require only local information and communications at test time by imitating the policy of centralized controllers using global information at training time. By extending aggregation graph neural networks to time varying signals and time varying network support, we learn a single common local controller which exploits information from distant teammates using only local communication interchanges. We apply this approach to the problem of flocking to demonstrate performance on communication graphs that change as the robots move. We examine how a decreasing communication radius and faster velocities increase the value of multi-hop information.