Reviewerly/exHarmony huggingface dataset:
A Convex Formulation of SVM-Based Multi-task Learning

Multi-task learning (MTL) is a powerful framework that allows to take advantage of the similarities between several machine learning tasks to improve on their solution by independent task specific models. Support Vector Machines (SVMs) are well suited for this and Cai et al. have proposed additive MTL SVMs, where the final model corresponds to the sum of a common one shared between all tasks, and each task specific model. In this work we will propose a different formulation of this additive approach, in which the final model is a convex combination of common and task specific ones. The convex mixing hyper-parameter $$\lambda $$ takes values between 0 and 1, where a value of 1 is mathematically equivalent to a common model for all the tasks, whereas a value of 0 corresponds to independent task-specific models. We will show that for $$\lambda $$ values between 0 and 1, this convex approach is equivalent to the additive one of Cai et al. when the other SVM parameters are properly selected. On the other hand, the predictions of the proposed convex model are also convex combinations of the common and specific predictions, making this formulation easier to interpret. Finally, this convex formulation is easier to hyper-parametrize since the hyper-parameter $$\lambda $$ is constrained to the [0, 1] region, in contrast with the unbounded range in the additive MTL SVMs.
Graph Regularization Network with Semantic Affinity for Weakly-Supervised Temporal Action Localization

This paper presents a novel deep architecture for weakly-supervised temporal action localization that not only generates segment-level action responses but also propagates segment-level responses to the neighborhood in a form of graph Laplacian regularization. Specifically, our approach consists of two sub-modules; a class activation module to estimate the action score map over time through the action classifiers, and a graph regularization module to refine the estimated action score map by solving a quadratic programming problem with the predicted segment-level semantic affinities. Since these two modules are integrated with fully differentiable layers, the proposed networks can be jointly trained in an end-to-end manner. Experimental results on Thumos14 and ActivityNet1.2 demonstrate that the proposed method provides outstanding performances in weakly-supervised temporal action localization.
An Effective Deep Transfer Learning and Information Fusion Framework for Medical Visual Question Answering

Medical visual question answering (Med-VQA) is very important for better clinical decision support and enhanced patient engagement in patient-centered medical care. Compared with open domain VQA tasks, VQA in medical domain becomes more challenging due to limited training resources as well as unique characteristics on medical images and domain vocabularies. In this paper, we propose and develop a novel deep transfer learning model, ETM-Trans, which exploits embedding topic modeling (ETM) on textual questions to derive topic labels to pair with associated medical images for finetuning the pre-trained ImageNet model. We also explore and implement a co-attention mechanism where residual networks is used to extract visual features from image interacting with the long-short term memory (LSTM) based question representation providing fine-grained contextual information for answer derivation. To efficiently integrate visual features from the image and textual features from the question, we employ Multimodal Factorized Bilinear (MFB) pooling as well as Multimodal Factorized High-order (MFH) pooling. The ETM-Trans model won the international Med-VQA 2018 challenge, achieving the best WBSS score of 0.186.
Hardware Implementation Design of Parallelized Fuzzy Adaptive Resonance Theory Neural Network

A hardware implementation design of parallelized fuzzy Adaptive Resonance Theory neural network is described and simulated. Parallel category choice and resonance are implemented in the network. Continuous-time and discrete-time winner-take-all neural circuits identifying the largest of M inputs are used as the winner-take-all units. The continuous-time circuit is described by a state equation with a discontinuous right-hand side. The discrete-time counterpart is governed by a difference equation. Corresponding functional block-diagrams of the circuits include M feed-forward hard-limiting neurons and one feedback neuron, which is used to compute the dynamic shift of inputs. The circuits combine arbitrary finite resolution of inputs, high convergence speed to the winner-take-all operation, low computational and hardware implementation complexity, and independence of initial conditions. The circuits are also used for finding elements of input vector with minimal/maximal values to normalize them in the range [0, 1].
An Analysis on Different Document Keyword Extraction Methods

The research document corpses grow with more and more publication in the various field of research. Keywords play an important role in grouping these documents and making it available to users. This paper gives an insight into the new emerging and ongoing models of keyword extraction. Here we have explained four new different ways for keyword extraction, that are TF-IDF, sentence embedding and graph-based models. In which graph-based models for extraction of keywords contain two different ways that are, by collective node weight and by building the graph. And in TF-IDF it shows the comparison of five different combinations of frequency measurement for extraction of keywords. These keyword extraction techniques are unsupervised methods. An unsupervised method does not need any input data other than the document itself for extracting keywords.
A Radar Measurement Model for Extended Object Tracking in Dynamic Scenarios

The radar sensor is an important component in autonomous driving applications. Compared with other sensor types like LiDAR or camera, the radar sensor comes with best weather robustness and ease of integration. Its exclusive capability to measure electromagnetic reflectivity and Doppler-derived radial speeds plays a major role in environment perception applications. However, the processing chain of the radar is more complex and often results in unintuitive measurement effects. In this paper, we explain the technical background of high-resolution radar detections. We give a probabilistic architecture modeling Doppler and Micro-Doppler measurements and their influence on the Monopulse-based azimuth angle, the peak detection and the resulting measurement data. Examples accompany the description of the modeling steps.
Body size illusions influence perceived size of objects: a validation of previous research in virtual reality

Previous research indicates that the size of the own body affects the judgment of objects’ size, depending on the amount of subjective ownership toward the body (Van der Hoort et al. in PLOS ONE 6(5):e20195, 2011). We are the first to transfer this own-body-size effect into a virtual environment. In a series of three experiments, participants (N = 68) had to embody small, medium, and large avatars and judge the size of objects. Body ownership was manipulated using synchronous and asynchronous touch. We also included a new paradigm with an additional change of perspective to induce stronger ownership (Experiment 2). Additionally, we assessed whether the visibility of the body during the judgment phase influenced the results (Experiment 3). In all three experiments, we found an overestimation in a small and an underestimation in a large body compared to a medium body. However, size estimation did not depend on the degree of ownership despite clear differences in self-reported ownership. Our results show that a virtual reality scenario does not require a visuotactile manipulation of ownership in order to evoke the own-body-size effect. Our validation of the effect in a virtual setting may be helpful for the design of clinical applications.
Designing a Sales Prediction Model in Tourism Industry and Hotel Recommendation Based on Hybrid Recommendation

As the trend of e-commerce and online shopping is increasing day by day, we need a method which can be helpful to decide whether to buy the product or not. Here the opinions of the other customers and rating for that product can be used as a considerably good parameter for designing such a system. With the development of new technologies we have a possible solution in the form of recommendation system. Recommendation system gives an important help in providing the necessary information to user based on personalized and practical services. The most vital technique in this field is which plays a crucial role is Collaborative filtering. In this paper we present a recommendations system for tourism and hotel based on enhanced Collaborative filtering approach.
Context-Aware Natural Integration of Advertisement Object

Product placement, also called advertisement embedding, is to place some specific products in an image or a video, which may attract consumers to buy their products. However, adding advertisement objects in images is difficult, because where to add the product and how to fuse the background must be concerned. In this paper, to overcome this issue, we present a novel hierarchical framework with conditional generative adversarial network to add advertisement object in all kinds of scene images. The key point of our framework is leaning the relation between surrounding and products. To generate the products more realistic and to make the detail information such characters and logo more clear, we propose local-global discriminator. Our experiment demonstrates the effectiveness of our proposed method on different scene images. Detail codes and results are available in GitHub. https://github.com/happyyuwei/ProductPlacement.
Changes in perceived usability and aesthetics with repetitive use in the first use session

Abstract This study examines how repetitive use in a short time period influences perceived usability, aesthetics, and user satisfaction. A mixed design was used, considering usability, aesthetics, and exposure time. Sixty‐four users were tested in a controlled experimental setting. According to the results, the negative effect of low usability on perceived usability and user satisfaction decreased as familiarity with the tasks increased. Also, the negative effect of low aesthetics on perceived usability weakened with repetitive use. These results imply that repetitive use can be an important matter for products with bad designs and promoting repetitive use in a short time period can be an effective strategy to reduce negative user perceptions.
Current Trends in the Population-Based Optimization

Population-based methods are used to deal with computationally difficult optimization problems. The outcome of the current research effort in the field of the population-based optimization can be broadly categorized as a new, stand-alone, P-B metaheuristics, ensemble P-B metaheuristics including multi-population and multi-agent approaches, new hybrid approaches involving P-B metaheuristics, and improvements and successful modifications of the earlier known P-B metaheuristics. Research results obtained during the last few years in each of the above categories are briefly discussed. The last part of the paper includes comments on directions of future research in the population-based optimization.
Hand Gesture Recognition for Collaborative Workstations: A Smart Command System Prototype

Human-machine collaboration is a key aspect in modern industries, which must be compliant to the Industry 4.0 paradigm. Although the collaboration can be achieved using a Collaborative Robot in a purposely designed workstation, this solution is not always neither feasible nor affordable for the specific task to be carried out in the workstation. On the other hand, using a smart HMI to make an industrial robot a "smart" robot can be a better and affordable solution depending on the task. In this work we present the preliminary development and characteristics of an experimental HMI for smart manufacturing developed in MATLAB and ROS Industrial. The collaboration between humans and robots is achieved by leveraging the Faster R-CNN Object Detector to robustly detect and recognize the hand gestures performed in real-time. The system is based on a state machine to carry out simple tasks such as the repeated movement of the robot following a given trajectory and a pick and place task where the robot interactively reaches a given point and a jog modality.
Quality Assessment for Natural and Screen Visual Contents

Quality assessment (QA) of screen visual contents (SVCs) has gained more and more popularity. SVCs are very different from natural visual contents (NVCs) which have been dealt with by most researchers in the literature. QA methods specifically designed for NVCs also can be used to evaluate the quality of SVCs. Yet, their performances are unsatisfactory. This may due to the statistical differences of SVCs and NVCs. In the thesis, SVCs and NVCs QA methods in the literature are being compared and studied for both SVCs and NVCs benchmarked databases. It is found out that methods that incorporate gradient features work well for both SVCs and NVCs. Thus, gradient feature is utilized in designing QA method that works for both SVCs and NVCs simultaneously.QA methods will be designed first for natural and screen content images. Then, the methods will be further enhanced for video QA purposes. This is the first attempt in formulating QA methods that work for SVCs and NVCs simultaneously.
Beg, borrow, and steal: Formal and informal access to the scholarly literature at U.S. master's universities

This study investigates the methods by which faculty obtain scholarly articles, books, and chapters. It focuses on full-text retrieval rather than discovery, drawing on a survey of 529 full-time faculty at U.S. colleges and universities in the Carnegie master's—large and master's—medium categories. When seeking articles, faculty rely mainly on their home-institution library collections, freely accessible online resources, and interlibrary loan. The situation is different for books, however; faculty most often purchase the books they need. Despite the continuing importance of formal access mechanisms (home-institution library collections and interlibrary loan), faculty rely on other sources of full text—informal access mechanisms—for 50% of the articles and 66% of the books they use. Nearly 25% get more articles from the open web than from any other source, and substantial minorities report heavy reliance on other sources. In particular, faculty sometimes use other libraries, often relying on current or past affiliations (e.g., part-time teaching) or on the user accounts of family, friends, and colleagues. Many are critical of their university library collections, but most are satisfied with freely accessible online resources and interlibrary loan.
Diffusion self-triggered square-root cubature information filter for nonlinear non-Gaussian systems and its application to the optic-electric sensor network

This article focuses on the problem of the distributed estimation for a class of discrete-time nonlinear non-Gaussian systems. In this paper, the non-Gaussian noises are approximated by the Gaussian mixture model. A novel method to merge the Gaussian components has been proposed in this article to improve its numerical stability. A diffusion Gaussian mixture square-root cubature information filter has been proposed in this paper. To ease the internode communication cost further, we design a self-triggered mechanism in this article. With the proposed mechanism, each sensor node transmits its information to its neighbors only when it’s necessary. The effectiveness of the proposed algorithm is verified through its application to an optic-electric sensor network.
Similarity-Based LSTM Architecture for Energy-Efficient Edge-Level Speech Recognition

Targeting the resource-limited edge devices, we present a novel processing architecture of long short-term memory (LSTM) networks for low-power speech recognition. The proposed scheme newly defines the similarity score between two inputs of adjacent LSTM cells, and then the processing mode of the current LSTM cell is dynamically determined to reduce the energy while providing the accurate recognition. If the similarity is high, more precisely, the current cell is disabled and the outputs are directly copied from the prior vectors, totally eliminating complex LSTM operations. To maximize the skipping ratio without degrading the accuracy, for the first time, we analyze the effects of skipping the consecutive cells and set the upper limit of the number of consecutive skips. When two adjacent inputs are weakly similar, in addition, we modify the concept of the previous delta-computing, which approximately activate the LSTM cell with low computational resolution, further reducing the energy consumption. Compared to the previous state-of-the-art solutions, as a result, the proposed LSTM architecture remarkably saves the energy consumed for the accurate speech recognition, which is suitable to the resource-limited embedded edges.
Enhancing subject matter assessments utilizing augmented reality and serious game techniques

In this paper, we utilize the Microsoft HoloLens, a wearable augmented-reality (AR) device, to investigate how well an AR-based assessment tool measures a student's comprehension of, skill in, and aptitude for a given subject matter. We added assessment capabilities to a serious game prototype built in collaboration with Construction Management faculty for their Occupational Safety and Health Administration (OSHA) safety course. In an effort to verify if these assessment elements are effective, we hosted a trial with sixteen university students who were enrolled in the OSHA safety course.
Mixture variational autoencoders

Variational autoencoders (VAEs) combine a generative model and a recognition model, and jointly train them to maximize a variational lower bound. VAEs play an important role in unsupervised learning and representation learning. But the isotropic generative model in VAEs cannot sufficiently utilize the latent representative space. In this paper, we present mixture variational autoencoders (MVAEs) which suppose observed data is generated from mixture models. We use a continuous variable with prior of Normal distribution as the latent representation, and introduce a discrete variable with prior of Multinomial distribution as latent indicator for mixture models. Two latent variables are both approximated by recognition models computed from neural networks. Furthermore, we combine the reparameterization trick and stick-breaking parameterization to realize stochastic gradient variational Bayes (SGVB) estimator on our variational objective. The MVAEs improve the generative performance by enlarging the latent representative space. Finally, we demonstrate the performance of MVAEs compared with the state-of-the-art models on MNIST, OMNIGLOT, and Fashion-MNIST.
Secure Energy Market against Cyber Attacks using Blockchain

In the recent trend of smart grids, most of the systems are interconnected with cyber systems which lead to various security and adversary issues. To protect the systems from serious threats of cyber-attacks, one of the solutions is to exclude a central authority by making the system decentralized. This issue of the system security can be addressed using blockchain which is a distributed ledger storing data securely via cryptography. In the proposed framework, i.e. the inclusion of smart contract in blockchain for energy trading between Electric Vehicles (EV) and Charging Stations (CS) eliminates the need of an intermediary which minimizes the misleading of information between both the parties and enhances the security and privacy of the system. The cost of charging is calculated and validated by smart contracts via consensus of the CSs. If the EVs have multiple options to choose a particular CS, then the one proposing minimum charging cost is chosen. Furthermore, cost and other details are stored in blockchain in the form of blocks via consensus, which are connected cryptographically to the previous blocks, thus data is immutable and secured.
Unifying Explicit and Implicit Feedback for Rating Prediction and Ranking Recommendation Tasks

The two main tasks addressed by collaborative filtering approaches are rating prediction and ranking. Rating prediction models leverage explicit feedback (e.g. ratings), and aim to estimate the rating a user would assign to an unseen item. In contrast, ranking models leverage implicit feedback (e.g. clicks) in order to provide the user with a personalized ranked list of recommended items. Several previous approaches have been proposed that learn from both explicit and implicit feedback to optimize the task of ranking or rating prediction at the level of recommendation algorithm. Yet we argue that these two tasks are not completely separate, but are part of a unified process: a user first interacts with a set of items and then might decide to provide explicit feedback on a subset of items. We propose to bridge the gap between the tasks of rating prediction and ranking through the use of a novel weak supervision approach that unifies both explicit and implicit feedback datasets. The key aspects of the proposed model is that (1) it is applied at the level of data pre-processing and (2) it increases the representation of less popular items in recommendations while maintaining reasonable recommendation performance. Our experimental results - on six datasets covering different types of heterogeneous user's interactions and using a wide range of evaluation metrics - show that, our proposed approach can effectively combine explicit and implicit feedback and improve the effectiveness of the baseline explicit model on the ranking task by covering a broader range of long-tail items.
In-depth comparison of the Berlekamp–Massey–Sakata and the Scalar-FGLM algorithms: The adaptive variants

The Berlekamp–Massey–Sakata algorithm and the Scalar-FGLM algorithm both compute the ideal of relations of a multidimensional linear recurrent sequence. Whenever quering a single sequence element is prohibitive, the bottleneck of these algorithms becomes the computation of all the needed sequence terms. As such, having adaptive variants of these algorithms, reducing the number of sequence queries, becomes mandatory. A native adaptive variant of the Scalar-FGLM algorithm was presented by its authors, the so-called Adaptive Scalar-FGLM algorithm. In this paper, our first contribution is to make the Berlekamp–Massey–Sakata algorithm more efficient by making it adaptive to avoid some useless relation testings. This variant allows us to divide by four in dimension 2 and by seven in dimension 3 the number of basic operations performed on some sequence family. Then, we compare the two adaptive algorithms. We show that their behaviors differ in a way that it is not possible to tweak one of the algorithms in order to mimic exactly the behavior of the other. We detail precisely the differences and the similarities of both algorithms and conclude that in general the Adaptive Scalar-FGLM algorithm needs fewer queries and performs fewer basic operations than the Adaptive Berlekamp–Massey–Sakata algorithm. We also show that these variants are always more efficient than the original algorithms.