Reviewerly/exHarmony huggingface dataset:
Learning to solve the credit assignment problem

Backpropagation is driving today's artificial neural networks (ANNs). However, despite extensive research, it remains unclear if the brain implements this algorithm. Among neuroscientists, reinforcement learning (RL) algorithms are often seen as a realistic alternative: neurons can randomly introduce change, and use unspecific feedback signals to observe their effect on the cost and thus approximate their gradient. However, the convergence rate of such learning scales poorly with the number of involved neurons. Here we propose a hybrid learning approach. Each neuron uses an RL-type strategy to learn how to approximate the gradients that backpropagation would provide. We provide proof that our approach converges to the true gradient for certain classes of networks. In both feedforward and convolutional networks, we empirically show that our approach learns to approximate the gradient, and can match the performance of gradient-based learning. Learning feedback weights provides a biologically plausible mechanism of achieving good performance, without the need for precise, pre-specified learning rules.
SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models

The standard variational lower bounds used to train latent variable models produce biased estimates of most quantities of interest. We introduce an unbiased estimator of the log marginal likelihood and its gradients for latent variable models based on randomized truncation of infinite series. If parameterized by an encoder-decoder architecture, the parameters of the encoder can be optimized to minimize its variance of this estimator. We show that models trained using our estimator give better test-set likelihoods than a standard importance-sampling based approach for the same average computational cost. This estimator also allows use of latent variable models for tasks where unbiased estimators, rather than marginal likelihood lower bounds, are preferred, such as minimizing reverse KL divergences and estimating score functions.
Spatial-Temporal Gaussian Scale Mixture Modeling for Foreground Estimation

Subtracting the backgrounds from the video frames is an important step for many video analysis applications. Assuming that the backgrounds are low-rank and the foregrounds are sparse, the robust principle component analysis (RPCA)-based methods have shown promising results. However, the RPCA-based methods suffered from the scale issue, i.e., the ℓ1-sparsity regularizer fails to model the varying sparsity of the moving objects. While several efforts have been made to address this issue with advanced sparse models, previous methods cannot fully exploit the spatial-temporal correlations among the foregrounds. In this paper, we proposed a novel spatial-temporal Gaussian scale mixture (STGSM) model for foreground estimation. In the proposed STGSM model, a temporal consistent constraint is imposed over the estimated foregrounds through nonzero-means Gaussian models. Specifically, the estimates of the foregrounds obtained in the previous frame are used as the prior for these of the current frame, and nonzero means Gaussian scale mixture models (GSM) are developed. To better characterize the temporal correlations, the optical flow has been used to model the correspondences between foreground pixels in adjacent frames. The spatial correlations have also been exploited by considering that local correlated pixels should be characterized by the same STGSM model, leading to further performance improvements. Experimental results on real video datasets show that the proposed method performs comparably or even better than current state-of-the-art background subtraction methods.
Towards Improved Trust in Threat Intelligence Sharing using Blockchain and Trusted Computing

Threat intelligence sharing is posited as an important aid to help counter cybersecurity attacks and a number of threat intelligence sharing communities exist. There is a general consensus that many challenges remain to be overcome to achieve fully effective sharing, including concerns about privacy, negative publicity, policy/legal issues and expense of sharing, amongst others. One recent trend undertaken to address this is the use of decentralized blockchain based sharing architectures. However while these platforms can help increase sharing effectiveness they do not fully address all of the above challenges. In particular, issues around trust are not satisfactorily solved by current approaches. In this paper, we describe a novel trust enhancement framework -TITAN- for decentralized sharing based on the use of P2P reputation systems to address open trust issues. Our design uses blockchain and Trusted Execution Environment technologies to ensure security, integrity and privacy in the operation of the threat intelligence sharing reputation system.
User Profile System based on Sentiment Analysis for Mobile Edge Computing

Emotions of users do not converge in a single application but are scattered across diverse applications. Mobile devices are the closest media for handling user data and these devices have the advantage of integrating priv... | Find, read and cite all the research you need on Tech Science Press
Blockchain for IoT-Based Digital Supply Chain: A Survey

This exploratory investigation aims to discuss current network environment of digital supply chain system and security issues, especially from the Internet world, of digital supply chain management system with applying some advanced information technologies, such as Internet of Things and blockchain, for improving various system performance and properties. This paper introduces the general histories and backgrounds, in terms of information science, of the supply chain and relevant technologies which have been applied or are potential to be applied on supply chain with purpose of lowering cost, facilitating its security and convenience. It provides a comprehensive review of current relative research work and industrial cases from several famous companies. It also illustrates the IoT enablement and security issues of current digital supply chain system, and existing blockchain’s role in this kind of digital system. Finally, this paper concludes several potential or existing security issues and challenges which supply chain management is facing.
Harmony Search Method with Global Sharing Factor Based on Natural Number Coding for Vehicle Routing Problem

This paper proposes an improved Harmony Search algorithm, and gives the definition of the Global Sharing Factor of the Harmony Search (HS) algorithm. In the definition, the number of creations of the HS algorithm is applied to the sharing factor and calculated. In this algorithm, the natural harmony encoding method is used to encode the initial harmony, and the total path length of all vehicles is taken as the optimization objective function. A new harmony generation strategy is proposed as follows: each tone component in an evolution is calculated separately using the new learning strategy and update strategy. In the calculation process, the tone component is judged by whether it needs to be adjusted according to the adjustment strategy. In this way, the problems of singularity and randomness of the new harmony generation strategy of basic HS are solved to improve the diversity of algorithm solutions. Then, a new Harmony Search method with Global Sharing Factor based on natural number coding and decoding for the Vehicle Routing Problem (GSF-HS-VRP) is proposed. The improved Global Sharing Factor-Harmony Search-Vehicle Routing Problem (GSF-HS-VRP) algorithm is applied to capacity-limited vehicle path optimization problems compared with the HS, Improved Harmony Search (IHS), Global-best Harmony Search (GHS), and Self-adaptive Global Best Harmony Search (SGHS) algorithms. The small-scale data and Solomon examples were adopted as the experimental data. Compared with the other four algorithms, the GSF-HS-VRP algorithm has the shortest running time, more rapid convergence speed, and higher efficiency. In the multi-vehicle test, with the increase of the number of vehicles, the optimized path of the vehicle is more satisfied in relation to the actual needs of customers. The results showed that this method could effectively improve the optimization performance of the capacity-limited vehicle routing problem.
Networked Disinformation and the Lifecycle of Online Conspiracy Theories

This chapter aims to navigate through the form and function of online network topologies in the context of a rising tide of misinformation and disinformation campaigns. It explains how network effects regulate the lifecycle of conspiracy theories. The E.U. membership referendum in the UK and the election of Donald Trump as the 45th president of the USA, both in 2016, put the debates about the ‘real-world’ impact of Web-mediated disinformation campaigns in the limelight. The topology of the online meta-network turns it into a fitting environment for the global spread of rumours, falsehoods and conspiracy theories. A concise social network analysis of the ‘pizzagate’ conspiracy theory evidences a clear distribution of positions and roles within the whole network across the three sampled windows of observation. Powered by online technical and social structures, echo-chamber dynamics find far-reaching avenues of expression and new audiences to captivate – showing a capacity of adaptation that can go through phases of entrenchment, encroachment and retrenchment.
Influence of sampling accuracy on augmented reality for laparoscopic image-guided surgery

This study aims to evaluate the accuracy of point-based registration (PBR) when used for augmented reality (AR) in laparoscopic liver resection surgery.The study was conducted in three different scenarios in which the accuracy of sampling targets for PBR decreases: using an assessment phantom with machined divot holes, a patient-specific liver phantom with markers visible in computed tomography (CT) scans and in vivo, relying on the surgeon's anatomical understanding to perform annotations. Target registration error (TRE) and fiducial registration error (FRE) were computed using five randomly selected positions for image-to-patient registration.AR with intra-operative CT scanning showed a mean TRE of 6.9 mm for the machined phantom, 7.9 mm for the patient-specific phantom and 13.4 mm in the in vivo study.AR showed an increase in both TRE and FRE throughout the experimental studies, proving that AR is not robust to the sampling accuracy of the targets used to compute image-to-patient registration. Moreover, an influence of the size of the volume to be register was observed. Hence, it is advisable to reduce both errors due to annotations and the size of registration volumes, which can cause large errors in AR systems.
Developing a Complete Dialogue System Using Long Short-Term Memory

As technologies of natural language understanding and generation improve, the human interest towards human-computer interaction increases. The technologies can be applied for various applications of customer services. Most works related to this field are emphasizing on single sentence and speaker turn. Meanwhile, a conversation sometimes has its own context according to the previous one. Designing this kind of conversational system is challenging. Most conversational agents are built based on knowledge-based and rule based systems. This paper discusses a development of a complete dialogue system to understand the intent of a text and give response based on the dialogue state. The dialogue model is implemented using the combination of rule-based and data-driven approach by utilizing a long short-term memory (LSTM). Some experiments show that the developed system give a high performance. A detail observation informs that some errors come from the intent classifier that fails to classify some sentences not in the corpus. This system can be improved by increasing the performance of the intent classifier and incorporating an additional named entity recognition module.
TRAINING AS A MAJOR TOOL OF TEACHER PROFESSIONALISM ENHANCEMENT

T. Micheeva, E. Murugova, Y. Morozova, V. DemchenkoDon State Technical University (RUSSIAN FEDERATION)
The Shapley Value of Tuples in Query Answering.

We investigate the application of the Shapley value to quantifying the contribution of a tuple to a query answer. The Shapley value is a widely known numerical measure in cooperative game theory and in many applications of game theory for assessing the contribution of a player to a coalition game. It has been established already in the 1950s, and is theoretically justified by being the very single wealth-distribution measure that satisfies some natural axioms. While this value has been investigated in several areas, it received little attention in data management. We study this measure in the context of conjunctive and aggregate queries by defining corresponding coalition games. We provide algorithmic and complexity-theoretic results on the computation of Shapley-based contributions to query answers; and for the hard cases we present approximation algorithms.
Generalized equation for real-world image enhancement by Milano Retinex family

Milano Retinexes are spatial color algorithms grounded on the Retinex theory and widely applied to enhance the visual content of real-world color images. In this framework, they process the color channels of the input image independently and re-scale channel by channel the intensity of each pixel <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>p</mml:mi> </mml:math> by the so-called local reference white, i.e., a strictly positive value, computed by reworking a set of features sampled around <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>p</mml:mi> </mml:math> . The neighborhood of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>p</mml:mi> </mml:math> to be sampled, its sampling, the features to be processed, as well as the mathematical model for the computation of the local reference white vary from algorithm to algorithm, determining different levels of enhancement. Based on the analysis of a group of Milano Retinexes, this work proves that the Milano Retinex local reference whites can be expressed by a generalized equation whose parameters model specific aspects of the Milano Retinex spatial color processing. In particular, tuning these parameters leads to different Milano Retinex implementations. This study contributes to a better understanding of the similarities and differences among the members of the Milano Retinex family, and provides new taxonomic schemes of them based on their own mathematical properties.
Predicting Hidden Danger Quantity in Coal Mines Based on Gray Neural Network

The hidden danger is the direct cause of coal mine accidents, and the number of hidden dangers in a certain area not only reflects the current safety situation, but also determines the development trend of safety production in this area to a large extent. By analyzing the formation and development law of the hidden dangers and hidden danger accident-induced mechanism in coal mines, it is concluded that there are some objective laws in the process of occurrence, development, weakening, and even stabilization of hidden dangers in a certain area. The development of the number of hidden dangers for a coal mine generally presents the law of similar normal distribution curve, with a certain degree of partial symmetry. Many years of hidden danger elimination in coal mines will accumulate large-scale hidden danger data. In this paper, by using the average value of hidden danger quantity in consecutive months to weaken the oscillation of hidden danger quantity sequence, and combining with gray model (1,1) and the neural network of extreme learning machine, and employing big data of hidden dangers available, a hidden danger quantity prediction model based on the gray neural network was established, and the experimental analysis and verification carried out. The results show that the model can achieve good prediction effect on the number of hidden dangers in a coal mine, which not only reflects the complex gray system behavior of hidden dangers of a coal mine, but also can predict dynamically. The safety management efficiency and emergency capacity of the coal mine enterprise will be greatly improved.
Deep Learning-Based Multi-Chatbot Broker for Q&amp;A Improvement of Video Tutoring Assistant

Chatbot is software for conversations where the opponent is a program instead of a human. Marketing, business, education, healthcare, and other fields are using chatbots for the convenience of users. The functionality of the chatbot is like a virtual assistant for users to help in purchasing and idea generations, resulting in better services and faster response. This paper presents the fundamental components and working principle of a multi-chatbot broker for video tutoring assistant for e-learning as an online video lecture assistant. The proposed broker implements a deep learning approach for a single video having multiple chatbots to select relative answers for a different perspective of each user, thereby improving the responsiveness of feedback and its quality. This means reducing the responding time of video tutoring assistant and increasing the quality of Q&A data by adding multiple chatbots from different viewpoints for a single video.
Probing Text Models for Common Ground with Visual Representations

Vision, as a central component of human perception, plays a fundamental role in shaping natural language. To better understand how text models are connected to our visual perceptions, we propose a method for examining the similarities between neural representations extracted from words in text and objects in images. Our approach uses a lightweight probing model that learns to map language representations of concrete words to the visual domain. We find that representations from models trained on purely textual data, such as BERT, can be nontrivially mapped to those of a vision model. Such mappings generalize to object categories that were never seen by the probe during training, unlike mappings learned from permuted or random representations. Moreover, we find that the context surrounding objects in sentences greatly impacts performance. Finally, we show that humans significantly outperform all examined models, suggesting considerable room for improvement in representation learning and grounding.
Novel Pure-Pursuit Trajectory Following Approaches and their Practical Applications

Pure-pursuit algorithm is a popular trajectory tracking algorithm, widely used in mobile robotics and vehicular control for numerous reasons. The operation is simple and straightforward as it depends only on the kinematic model of the target mechanical system. The algorithm can be tuned by choosing look-ahead distance of points of the reference trajectory. On the other hand, pure-pursuit suffers from weaknesses highly attributed to improper selection of look-ahead distance, resulting in poor tracking performance. Recent developments focused on overcoming this drawback by dynamical change of this parameter. In this paper, we propose three enhancements to the original pure-pursuit algorithm, aiming to handle dynamic selection of look-ahead distance by selecting multiple goals, modifying look-ahead distance according to the curvatures and adjusting lateral deviation. Our work was motivated by an ongoing autonomous vehicle research at our university.
A Speaker Verification Backend for Improved Calibration Performance across Varying Conditions

In a recent work, we presented a discriminative backend for speaker verification that achieved good out-of-the-box calibration performance on most tested conditions containing varying levels of mismatch to the training conditions. This backend mimics the standard PLDA-based backend process used in most current speaker verification systems, including the calibration stage. All parameters of the backend are jointly trained to optimize the binary cross-entropy for the speaker verification task. Calibration robustness is achieved by making the parameters of the calibration stage a function of vectors representing the conditions of the signal, which are extracted using a model trained to predict condition labels. In this work, we propose a simplified version of this backend where the vectors used to compute the calibration parameters are estimated within the backend, without the need for a condition prediction model. We show that this simplified method provides similar performance to the previously proposed method while being simpler to implement, and having less requirements on the training data. Further, we provide an analysis of different aspects of the method including the effect of initialization, the nature of the vectors used to compute the calibration parameters, and the effect that the random seed and the number of training epochs has on performance. We also compare the proposed method with the trial-based calibration (TBC) method that, to our knowledge, was the state-of-the-art for achieving good calibration across varying conditions. We show that the proposed method outperforms TBC while also being several orders of magnitude faster to run, comparable to the standard PLDA baseline.
SympleGraph: distributed graph processing with precise loop-carried dependency guarantee

Graph analytics is an important way to understand relationships in real-world applications. At the age of big data, graphs have grown to billions of edges. This motivates distributed graph processing. Graph processing frameworks ask programmers to specify graph computations in user- defined functions (UDFs) of graph-oriented programming model. Due to the nature of distributed execution, current frameworks cannot precisely enforce the semantics of UDFs, leading to unnecessary computation and communication. In essence, there exists a gap between programming model and runtime execution. This paper proposes SympleGraph, a novel distributed graph processing framework that precisely enforces loop-carried dependency, i.e., when a condition is satisfied by a neighbor, all following neighbors can be skipped. SympleGraph instruments the UDFs to express the loop-carried dependency, then the distributed execution framework enforces the precise semantics by performing dependency propagation dynamically. Enforcing loop-carried dependency requires the sequential processing of the neighbors of each vertex distributed in different nodes. Therefore, the major challenge is to enable sufficient parallelism to achieve high performance. We propose to use circulant scheduling in the framework to allow different machines to process disjoint sets of edges/vertices in parallel while satisfying the sequential requirement. It achieves a good trade-off between precise semantics and parallelism. The significant speedups in most graphs and algorithms indicate that the benefits of eliminating unnecessary computation and communication overshadow the reduced parallelism. Communication efficiency is further optimized by 1) selectively propagating dependency for large-degree vertices to increase net benefits; 2) double buffering to hide communication latency. In a 16-node cluster, SympleGraph outperforms the state-of-the-art system Gemini and D-Galois on average by 1.42× and 3.30×, and up to 2.30× and 7.76×, respectively. The communication reduction compared to Gemini is 40.95% on average and up to 67.48%.
QoS-aware Energy-efficient Task Scheduling on HPC Cloud Infrastructures Using Swarm-intelligence Meta-heuristics

Cloud computing infrastructure has been evolving as a cost-effective platform for providing computational resources in the form of high-performance computing as a service (HPCaaS) to users for executing HPC applications. ... | Find, read and cite all the research you need on Tech Science Press