Reviewerly/exHarmony huggingface dataset:
A New Local Transformation Module for Few-Shot Segmentation

Few-shot segmentation segments object regions of new classes with a few of manual annotations. Its key step is to establish the transformation module between support images (annotated images) and query images (unlabeled images), so that the segmentation cues of support images can guide the segmentation of query images. The existing methods form transformation model based on global cues, which however ignores the local cues that are verified in this paper to be very important for the transformation. This paper proposes a new transformation module based on local cues, where the relationship of the local features is used for transformation. To enhance the generalization performance of the network, the relationship matrix is calculated in a high-dimensional metric embedding space based on cosine distance. In addition, to handle the challenging mapping problem from the low-level local relationships to high-level semantic cues, we propose to apply generalized inverse matrix of the annotation matrix of support images to transform the relationship matrix linearly, which is non-parametric and class-agnostic. The result by the matrix transformation can be regarded as an attention map with high-level semantic cues, based on which a transformation module can be built simply. The proposed transformation module is a general module that can be used to replace the transformation module in the existing few-shot segmentation frameworks. We verify the effectiveness of the proposed method on Pascal VOC 2012 dataset. The value of mIoU achieves at 57.0% in 1-shot and 60.6% in 5-shot, which outperforms the state-of-the-art method by 1.6% and 3.5%, respectively.
Personalizing Activity Recognition Models Through Quantifying Different Types of Uncertainty Using Wearable Sensors

Recognizing activities of daily living (ADL) provides vital contextual information that enhances the effectiveness of various mobile health and wellness applications. Development of wearable motion sensors along with machine learning algorithms offer a great opportunity for ADL recognition. However, the performance of the ADL recognition systems may significantly degrade when they are used by a new user due to inter-subject variability. This issue limits the usability of these systems. In this paper, we propose a deep learning assisted personalization framework for ADL recognition with the aim to maximize the personalization performance while minimizing solicitation of inputs or labels from the user to reduce user's burden. The proposed framework consists of unsupervised retraining of automatic feature extraction layers and supervised fine-tuning of classification layers through a novel active learning model based on a given model's uncertainty. We design a Bayesian deep convolutional neural network with stochastic latent variables that allows us to estimate both aleatoric (data-dependent) and epistemic (model-dependent) uncertainties in recognition task. In this study, for the first time, we show how distinguishing between the two aforementioned sources of uncertainty leads to more effective active learning. The experimental results show that our proposed method improves the accuracy of ADL recognition on a new user by 25% on average compared to the case of using a model for a new user with no personalization with an average final accuracy of 89.2%. Moreover, our method achieves higher personalization accuracy while significantly reducing user's burden in terms of soliciting inputs and labels compared to other methods.
A Graph Neural Network Assisted Monte Carlo Tree Search Approach to Traveling Salesman Problem

We tackle the classical traveling salesman problem (TSP) by combining a graph neural network and Monte Carlo Tree Search.We adopt a greedy algorithm framework to derive a promising tour by adding the vertices successively.A graph neural network is trained to capture graph motifs and interactions between vertices, and then to give the prior probability of selecting a vertex at every step.Instead of making decisions directly based on the output of graph neural networks, we combine the graph neural network with Monte Carlo Tree Search to provide a more reliable policy as the output of the latter is the feedback information by fusing the prior probability with the scouting exploration.Without much heuristic designing, our approach outperforms recent state-of-the-art learning-based methods on the TSP.Experimental results demonstrate that the proposed method can be generalized to instances with more vertices than those used during the training.
Trustful Internet of Surveillance Things Based on Deeply Represented Visual Co-Saliency Detection

Trustful Internet of Things (IoT) plays an important role in smart cities. The trust information in surveillance data motivates the analysis of images from numerous IoT devices. Saliency detection is a fundamental step in surveillance data analysis for providing help to the subsequent tasks, but unsuitable to IoT applications owing to the neglect of image similarity and difference from diverse IoT devices. To solve this problem, we enable the co-saliency detection in IoT, which detects the common and salient foreground regions in the group surveillance images. The main contributions include: 1) enable a multistage context perception scheme to efficiently extract the contextual information corresponding to different-size receptive fields in the single image; 2) construct a two-path information propagation to extract the interimage similarity and difference from the high-level image feature representations of the group images; and 3) propose the stage-wise refinement to allocate the label information to different parts of the network for helping the network to learn the enriched semantically common knowledge. The extensive experiments performed on three public data sets can demonstrate the effectiveness of our approach and its superiority to four state-of-the-art co-saliency detection methods.
A Theoretical Model for Fork Analysis in the Bitcoin Network

Blockchain networks which employ Proof-of-Work in their consensus mechanism may face inconsistencies in the form of forks. These forks are usually resolved through the application of block selection rules (such as the Nakamoto consensus). In this paper, we investigate the cause and length of forks for the Bitcoin network. We develop theoretical formulas which model the Bitcoin consensus and network protocols, based on an Erdös-Rényi random graph construction of the overlay network of peers. Our theoretical model addresses the effect of key parameters on the fork occurrence probability, such as block propagation delay, network bandwidth, and block size. We also leverage this model to estimate the weight of fork branches. Our model is implemented using the network simulator OMNET++ and validated by historical Bitcoin data. We show that under current conditions, Bitcoin will not benefit from increasing the number of connections per node.
A genetic Artificial Bee Colony algorithm for signal reconstruction based big data optimization

In recent years, the researchers have witnessed the changes or transformations driven by the existence of the big data on the definitions, complexities and future directions of the real world optimization problems. Analyzing the capabilities of the previously introduced techniques, determining possible drawbacks of them and developing new methods by taking into consideration of the unique properties related with the big data are nowadays in urgent demands. Artificial Bee Colony (ABC) algorithm inspired by the clever foraging behaviors of the real honey bees is one of the most successful swarm intelligence based optimization algorithms. In this study, a novel ABC algorithm based big data optimization technique was proposed. For exploring the solving abilities of the proposed technique, a set of experimental studies has been carried out by using different signal decomposition based big data optimization problems presented at the Congress on Evolutionary Computation (CEC) 2015 Big Data Optimization Competition. The results obtained from the experimental studies first were compared with the well-known variants of the standard ABC algorithm named gbest-guided ABC (GABC), ABC/best/1, ABC/best/2, crossover ABC (CABC), converge-onlookers ABC (COABC) and quick ABC (qABC). The results of the proposed ABC algorithm were also compared with the Differential Evolution (DE) algorithm, Genetic algorithm (GA), Firefly algorithm (FA), Fireworks algorithm (FW), Phase Base Optimization (PBO) algorithm, Particle Swarm Optimization (PSO) algorithm and Dragonfly algorithm (DA) based big data optimization techniques. From the experimental studies, it was understood that the newly introduced ABC algorithm based technique is capable of producing better or at least promising results compared to the mentioned big data optimization techniques for all of the benchmark instances.
Deep Neural Network Acceleration Based on Low-Rank Approximated Channel Pruning

Acceleration and compression on deep Convolutional Neural Networks (CNNs) have become a critical problem to develop intelligence on resource-constrained devices. Previous channel pruning can be easily deployed and accelerated without specialized hardware and software. However, weight-level redundancy is not well explored in channel pruning, which results in a relatively low compression ratio. In this work, we propose a Low-rank Approximated channel Pruning (LAP) framework to tackle this problem with two targeted steps. First, we utilize low-rank approximation to eliminate the redundancy within filter. This step achieves acceleration, especially in shallow layers, and also converts filters into smaller compact ones. Then, we apply channel pruning on the approximated network in a global way and obtain further benefits, especially in deep layers. In addition, we propose a spectral norm based indicator to coordinate these two steps better. Moreover, inspired by the integral idea adopted in video coding, we propose an evaluator based on Integral of Decay Curve (IDC) to judge the efficiency of various acceleration and compression algorithms. Ablation experiments and IDC evaluator prove that LAP can significantly improve channel pruning. To further demonstrate the hardware compatibility, the network produced by LAP obtains impressive speedup efficiency on the FPGA.
The Employment of Unmanned Aerial Vehicles for Analyzing and Mitigating Disaster Risks in Industrial Sites

Effective safety procedures play a fundamental role in high-risk industrial contexts, because the occurrence of an accident can cause environmental catastrophes, severe damages to the structures, and fatal injuries to the workers. In particular, maintaining detailed and up-to-date information about potential accident areas is a crucial issue to prevent the occurrence of disasters and to safely plan the rescue process. In such contexts, periodic safety inspections are therefore performed on a regular basis, although they may expose the inspection personnel to harsh or hazardous environments. The employment of unmanned aerial vehicles (UAVs) in such situations can provide unprecedented opportunities for predisaster analyses and site management operations. According to the current regulations, however, industrial contexts are classified as “critical,” which substantially limits the possibilities of performing UAV operations in such places. The recent introduction of a simplified regulatory system for inoffensive UAVs has opened a wide landscape of new business opportunities. The article discusses the application of such technology for site analysis in industrial contexts, and illustrates the mandatory risk assessment procedure prescribed by the regulation. The results obtained show that although the technical advantages and economic convenience of employing this technology is generally recognized, at the dawn of the fourth industrial revolution, the market spread is still poor due to fragmented regulations, cultural barriers, and technical issues. In particular, the compliance with the ATEX directives still constitutes a significant technological problem substantially limiting the spread of innovative industrial services. Such issues are discussed in the article referring to a case study, in order to provide a reference guideline and to highlight the boundaries of application of innovative approaches to the prevention and management of industrial disasters based on UAV technology. In addition, the article identifies some important directives for further research and development of UAV systems capable to operate in high-risk industrial contexts, which constitutes an emerging business area with a vast potential market.
Text Summarization Method Based on Double Attention Pointer Network

A good document summary should summarize the core content of the text. Research on automatic text summarization attempts to solve this problem. The encoder-decoder model is widely used in text summarization research. Soft attention is used to obtain the required contextual semantic information during decoding. However, due to the lack of access to the key features, the generated summary deviates from the core content. In this paper, we proposed an encoder-decoder model based on a double attention pointer network (DAPT). In DAPT, the self-attention mechanism collects key information from the encoder, the soft attention and the pointer network generate more coherent core content, and the fusion of both generates accurate and coherent summaries. In addition, the improved coverage mechanism is used to address the repetition problem and improve the quality of the generated summaries. Simultaneously, scheduled sampling and reinforcement learning (RL) are combined to generate new training methods to optimize the model. Experiments on the CNN/Daily Mail dataset and the LCSTS dataset show that our model performs as well as many state-of-the-art models. The experimental analysis shows that our model achieves higher summarization performance and reduces the occurrence of repetition.
Fringe harmonics elimination in multi-frequency phase-shifting fringe projection profilometry

In fringe projection profilometry, the purpose of using two- or multi-frequency fringe patterns is to unwrap the measured phase maps temporally. Using the same patterns, this paper presents a least squares algorithm for, simultaneously with phase-unwrapping, eliminating the influences of fringe harmonics induced by various adverse factors. It is demonstrated that, for most of the points over the measured surface, projecting two sequences of phase-shifting fringe patterns having different frequencies enables providing sufficiently many equations for determining the coefficient of a high order fringe harmonic. As a result, solving these equations in the least squares sense results in a phase map having higher accuracy than that depending only on the fringe patterns of a single frequency. For the other few points which have special phases related to the two frequencies, this system of equations becomes under-determined. For coping with this case, this paper suggests an interpolation-based solution which has a low sensitivity to the variations of reflectivity and slope of the measured surface. Simulation and experimental results verify that the proposed method significantly suppresses the ripple-like artifacts in phase maps induced by fringe harmonics without capturing extra many fringe patterns or correcting the non-sinusoidal profiles of fringes. In addition, this method involves a quasi-pointwise operation, enabling correcting position-dependent phase errors and being helpful for protecting the edges and details of the measurement results from being blurred.
Embedding individualized machine learning prediction models for energy efficient VM consolidation within Cloud data centers

The fast growth in demand for utility-based IT services has lead to the formation of large scale Cloud data centers. The electrical energy consumption of these data centers results in high operational costs and carbon dioxide emissions. Cloud data centers benefit from the use of virtualization technology to reduce their energy consumption. This technology enables a Cloud data center to allocate its physical resources (CPU, memory, hard disk, network bandwidth) on demand and balance loads between their physical hosts by live migration of Virtual Machines (VMs). However, the migration of VMs can result in Service Level Agreement Violations (SLAVs) and consequently low Quality of Service (QoS). Hence, in this paper, we propose an energy aware VM consolidation algorithm that minimizes SLAVs. Dynamic VM consolidation has three stages: a) Detecting over- and under-utilized hosts; b) Selecting one or more VMs for migration from those hosts; c) Finding destination hosts for the selected VMs. Therefore, the proposed VM consolidation algorithm contains different models for each stage. For the first stage, we developed different fine-tuned Machine Learning (ML) prediction models for individual VMs to predict the best time to trigger migrations from hosts. For the second stage, we lexicographically consider migration time and host CPU usage when selecting VMs to migrate. Finally, a new method based on the Best Fit Decreasing (BFD) algorithm was developed to select a destination host for the VMs being migrated. Our algorithm was compared to a baseline VM consolidation algorithm that used Local Regression for detecting over-utilized hosts, minimum migration time for the VM selection stage and power-aware best fit for the host selection stage. The comparison demonstrated that our VM consolidation algorithm improved energy consumption and SLAVs by 26% and 50%, respectively.
Gender Bias in Chatbot Design

A recent UNESCO report reveals that most popular voice-based conversational agents are designed to be female. In addition, it outlines the potentially harmful effects this can have on society. However, the report focuses primarily on voice-based conversational agents and the analysis did not include chatbots (i.e., text-based conversational agents). Since chatbots can also be gendered in their design, we used an automated gender analysis approach to investigate three gender-specific cues in the design of 1,375 chatbots listed on the platform chatbots.org. We leveraged two gender APIs to identify the gender of the name, a face recognition API to identify the gender of the avatar, and a text mining approach to analyze gender-specific pronouns in the chatbot’s description. Our results suggest that gender-specific cues are commonly used in the design of chatbots and that most chatbots are – explicitly or implicitly – designed to convey a specific gender. More specifically, most of the chatbots have female names, female-looking avatars, and are described as female chatbots. This is particularly evident in three application domains (i.e., branded conversations, customer service, and sales). Therefore, we find evidence that there is a tendency to prefer one gender (i.e., female) over another (i.e., male). Thus, we argue that there is a gender bias in the design of chatbots in the wild. Based on these findings, we formulate propositions as a starting point for future discussions and research to mitigate the gender bias in the design of chatbots.
Ontology-based approach to extract product's design features from online customers’ reviews

Online customer reviews provide new potential customers with relevant information about a product or service. It has been empirically shown that the type of reviews (positive or negative) a product receives significantly impacts its future sales. In this paper, the online customers’ reviews analysis for the identification of key product attributes to be used in the conceptual design phase of a product is outlined. Our goal is to bring a value-added link between the Middle of Life phase to the Beginning of Life phase in the closed-loop product lifecycle management (PLM) by developing an ontology-based reasoning system to provide information that represents the customers’ opinions for the product's conceptual design. The main contributions of the proposed approach are the integration between the ontology and the natural language processing system in extracting the customers’ reviews data in the overall framework. The utility of the proposed approach is shown through the application on the digital camera product review dataset from Amazon.
VRIA: A Web-Based Framework for Creating Immersive Analytics Experiences

We present <VRIA>, a Web-based framework for creating Immersive Analytics (IA) experiences in Virtual Reality.<VRIA> is built upon WebVR, A-Frame, React and D3.js, and offers a visualization creation workflow which enables users, of different levels of expertise, to rapidly develop Immersive Analytics experiences for the Web.The use of these open-standards Web-based technologies allows us to implement VR experiences in a browser and offers strong synergies with popular visualization libraries, through the HTML Document Object Model (DOM).This makes <VRIA> ubiquitous and platform-independent.Moreover, by using WebVR's progressive enhancement, the experiences <VRIA> creates are accessible on a plethora of devices.We elaborate on our motivation for focusing on open-standards Web technologies, present the <VRIA> creation workflow and detail the underlying mechanics of our framework.We also report on techniques and optimizations necessary for implementing Immersive Analytics experiences on the Web, discuss scalability implications of our framework, and present a series of use case applications to demonstrate the various features of <VRIA>.Finally, we discuss current limitations of our framework, the lessons learned from its development, and outline further extensions.
A Hybrid Deep Model Using Deep Learning and Dense Optical Flow Approaches for Human Activity Recognition

Human activity recognition is a challenging problem with many applications including visual surveillance, human-computer interactions, autonomous driving and entertainment. In this study, we propose a hybrid deep model to understand and interpret videos focusing on human activity recognition. The proposed architecture is constructed combining dense optical flow approach and auxiliary movement information in video datasets using deep learning methodologies. To the best of our knowledge, this is the first study based on a novel combination of 3D-convolutional neural networks (3D-CNNs) fed by optical flow and long short-term memory networks (LSTM) fed by auxiliary information over video frames for the purpose of human activity recognition. The contributions of this paper are sixfold. First, a 3D-CNN, also called multiple frames is employed to determine the motion vectors. With the same purpose, the 3D-CNN is secondly used for dense optical flow, which is the distribution of apparent velocities of movement in captured imagery data in video frames. Third, the LSTM is employed as auxiliary information in video to recognize hand-tracking and objects. Fourth, the support vector machine algorithm is utilized for the task of classification of videos. Fifth, a wide range of comparative experiments are conducted on two newly generated chess datasets, namely the magnetic wall chess board video dataset (MCDS), and standard chess board video dataset (CDS) to demonstrate the contributions of the proposed study. Finally, the experimental results reveal that the proposed hybrid deep model exhibits remarkable performance compared to the state-of-the-art studies.