Reviewerly/exHarmony huggingface dataset:
Interleave Variational Optimization with Monte Carlo Sampling: A Tale of Two Approximate Inference Paradigms

Computing the partition function of a graphical model is a fundamental task in probabilistic inference. Variational bounds and Monte Carlo methods, two important approximate paradigms for this task, each has its respective strengths for solving different types of problems, but it is often nontrivial to decide which one to apply to a particular problem instance without significant prior knowledge and a high level of expertise. In this paper, we propose a general framework that interleaves optimization of variational bounds (via message passing) with Monte Carlo sampling. Our adaptive interleaving policy can automatically balance the computational effort between these two schemes in an instance-dependent way, which provides our framework with the strengths of both schemes, leads to tighter anytime bounds and an unbiased estimate of the partition function, and allows flexible tradeoffs between memory, time, and solution quality. We verify our approach empirically on real-world problems taken from recent UAI inference competitions.
RTL Implementation of image compression techniques in WSN

&lt;p&gt;The Wireless sensor networks have limitations regarding data redundancy, power and require high bandwidth when used for multimedia data. Image compression methods overcome these problems. Non-negative Matrix Factorization (NMF) method is useful in approximating high dimensional data where the data has non-negative components. Another method of the NMF called (PNMF) Projective Nonnegative Matrix Factorization is used for learning spatially localized visual patterns. Simulation results show the comparison between SVD, NMF, PNMF compression schemes. Compressed images are transmitted from base station to cluster head node and received from ordinary nodes. The station takes on the image restoration. Image quality, compression ratio, signal to noise ratio and energy consumption are the essential metrics measured for compression performance. In this paper, the compression methods are designed using Matlab.The parameters like PSNR, the total node energy consumption are calculated. RTL schematic of NMF SVD, PNMF methods is generated by using Verilog HDL.&lt;/p&gt;
Deep learning approach to multimedia traffic classification based on QoS characteristics

With the fast increase of multimedia traffic in Internet of Things (IoT) applications, IoT traffic now requires very different Quality of Service (QoS). By extensive statistical analysis of traffic flow data from a real world network, the authors find that there are some latent features hidden in the multimedia data, which can be useful for accurately differentiating multimedia traffic flows from the QoS perspective. Under limited training data conditions, existing shallow classification methods are limited in performance, and are thus not effective in classifying emerging multimedia traffic types, which have truly entered the era of big data and become very completed in QoS features. This situation inspires us to revisit the multimedia traffic classification problem with a deep learning (DL) approach. In this study, an improved DL-based multimedia traffic classification method is proposed, which considers the inherent structure of QoS features in multimedia data. An improved stacked autoencoder model is employed to learn the relevant QoS features of multimedia traffic. Extensive experimental studies with multimedia datasets captured from a campus network demonstrate the effectiveness of the proposed method over six benchmark schemes.
Combining Adaptive Hierarchical Depth Motion Maps With Skeletal Joints for Human Action Recognition

This paper presents a new framework for human action recognition by fusing human motion with skeletal joints. First, adaptive hierarchical depth motion maps (AH-DMMs) are proposed to capture the shape and motion cues of action sequences. Specifically, AH-DMMs are calculated over adaptive hierarchical windows and Gabor filters are used to encode the texture information of AH-DMMs. Then, spatial distances of skeletal joint positions are computed to characterize the structure information of the human body. Finally, two types of fusion methods including feature-level fusion and decision-level fusion are employed to combine the motion cues and structure information. The experimental results on public benchmark datasets, i.e., MSRAction3D and UTKinect-Action, show the effectiveness of the proposed method.
Enriching Word Embeddings with a Regressor Instead of Labeled Corpora

We propose a novel method for enriching word-embeddings without the need of a labeled corpus. Instead, we show that relying on a regressor – trained with a small lexicon to predict pseudo-labels – significantly improves performance over current techniques that rely on human-derived sentence-level labels for an entire corpora. Our approach enables enrichment for corpora that have no labels (such as Wikipedia). Exploring the utility of this general approach in both sentiment and non-sentiment-focused tasks, we show how enriching embeddings, for both Twitter and Wikipedia-based embeddings, provide notable improvements in performance for: binary sentiment classification, SemEval Tasks, embedding analogy task, and, document classification. Importantly, our approach is notably better and more generalizable than other state-of-the-art approaches for enriching both labeled and unlabeled corpora.
CompareLDA: A Topic Model for Document Comparison

A number of real-world applications require comparison of entities based on their textual representations. In this work, we develop a topic model supervised by pairwise comparisons of documents. Such a model seeks to yield topics that help to differentiate entities along some dimension of interest, which may vary from one application to another. While previous supervised topic models consider document labels in an independent and pointwise manner, our proposed Comparative Latent Dirichlet Allocation (CompareLDA) learns predictive topic distributions that comply with the pairwise comparison observations. To fit the model, we derive a maximum likelihood estimation method via augmented variational approximation algorithm. Evaluation on several public datasets underscores the strengths of CompareLDA in modelling document comparisons.
Multi-Agent Discussion Mechanism for Natural Language Generation

We introduce the discussion mechanism into the multiagent communicating encoder-decoder architecture for Natural Language Generation (NLG) tasks and prove that by applying the discussion mechanism, the communication between agents becomes more effective. Generally speaking, an encoder-decoder architecture predicts target-sequence word by word in several time steps. At each time step of prediction, agents with the discussion mechanism predict the target word after several discussion steps. In the first step of discussion, agents make their choice independently and express their decision to other agents. In the next discussion step, agents collect other agents’ decision to update their own decisions, then express the updated decisions to others again. After several iterations, the agents make their final decision based on a well-communicated situation. The benefit of the discussion mechanism is that multiple encoders can be designed as different structures to fit the specified input or to fetch different representations of inputs.We train and evaluate the discussion mechanism on Table to Text Generation, Text Summarization and Image Caption tasks, respectively. Our empirical results demonstrate that the proposed multi-agent discussion mechanism is helpful for maximizing the utility of the communication between agents.
Impacto de la red social Facebook en el proceso educativo superior de las matemáticas considerando la ciencia de datos

This mixed research aims to analyze the use of the social network Facebook in Computational Mathematics and Basic Applied Statistics during the 2017 school year through data science (decision tree technique). The sample is composed of 54 students of the Bachelor of Administration, Information Technology, Marketing and Commerce. The decision tree technique allows the identification of 8 predictive models about the interaction and communication of students in the social network Facebook during the teaching and learning process. Likewise, the qualitative approach analyzes the use of this social network considering the variables on the meeting point, distribution of contents, motivation, support tool in learning and satisfaction. The results indicate that Facebook is a tool that facilitates the teaching and learning process in the field of mathematics. In fact, the linear regression indicates that the publication of tasks, sending of messages among students, creation of comments on the wall and consultation of videos in this social network positively influence the interaction and communication of students during the teaching and learning process. Therefore, this qualitative and quantitative study recommends the incorporation of the social network Facebook in school activities with the purpose of innovating educational practices.
Supporting software documentation with source code summarization

Source code summarization is a process of generating summaries that describe software code, the majority of source code summarization usually generated manually, where the summaries are written by software developers.Recently, new automated approaches are becoming more useful.These approaches have been found to be effective in some cases.The main weaknesses of these approaches are that they never exploit code dependencies and summarize either the software classes or methods but not both.This paper proposes a source code summarization approach (Suncode) that produces a short description for each class and method in the software system.To validate the approach, it has been applied to several case studies.Moreover, the generated summaries are compared to summaries that written by human experts and to summaries that written by a state-of-theart solution.Results of this paper found that Suncode summaries provide better information about code dependencies comparing with other studies.In addition, Suncode summaries can improve and support the current software documentation.The results found that manually written summaries were more precise and short as well.
Spatial Mixture Models with Learnable Deep Priors for Perceptual Grouping

Humans perceive the seemingly chaotic world in a structured and compositional way with the prerequisite of being able to segregate conceptual entities from the complex visual scenes. The mechanism of grouping basic visual elements of scenes into conceptual entities is termed as perceptual grouping. In this work, we propose a new type of spatial mixture models with learnable priors for perceptual grouping. Different from existing methods, the proposed method disentangles the representation of an object into “shape” and “appearance” which are modeled separately by the mixture weights and the conditional probability distributions. More specifically, each object in the visual scene is modeled by one mixture component, whose mixture weights and the parameter of the conditional probability distribution are generated by two neural networks, respectively. The mixture weights focus on modeling spatial dependencies (i.e., shape) and the conditional probability distributions deal with intra-object variations (i.e., appearance). In addition, the background is separately modeled as a special component complementary to the foreground objects. Our extensive empirical tests on two perceptual grouping datasets demonstrate that the proposed method outperforms the stateof-the-art methods under most experimental configurations. The learned conceptual entities are generalizable to novel visual scenes and insensitive to the diversity of objects.
Detection of immovable objects on visually impaired people walking aids

One consequence of a visually impaired (blind) person is a lack of ability in the activities related to the orientation and mobility. Blind person uses a stick as a tool to know the objects that surround him/her.The objective of this research is to develop a tool for blind person which is able to recognize what object in front of him/her when he/she is walking. An attached camera will obtain an image of an object which is then processed using template matching method to identify and trace the image of the object. After getting the image of the object, furthermore calculate and compare it with the data training. The output is produced in the form of sound that in accordance with the object. The result of this research is that the best slope and distance for the template matching method to properly detect silent objects is 90 degrees and 2 meters.
Data-Adaptive Metric Learning with Scale Alignment

The central problem for most existing metric learning methods is to find a suitable projection matrix on the differences of all pairs of data points. However, a single unified projection matrix can hardly characterize all data similarities accurately as the practical data are usually very complicated, and simply adopting one global projection matrix might ignore important local patterns hidden in the dataset. To address this issue, this paper proposes a novel method dubbed “Data-Adaptive Metric Learning” (DAML), which constructs a data-adaptive projection matrix for each data pair by selectively combining a set of learned candidate matrices. As a result, every data pair can obtain a specific projection matrix, enabling the proposed DAML to flexibly fit the training data and produce discriminative projection results. The model of DAML is formulated as an optimization problem which jointly learns candidate projection matrices and their sparse combination for every data pair. Nevertheless, the over-fitting problem may occur due to the large amount of parameters to be learned. To tackle this issue, we adopt the Total Variation (TV) regularizer to align the scales of data embedding produced by all candidate projection matrices, and thus the generated metrics of these learned candidates are generally comparable. Furthermore, we extend the basic linear DAML model to the kernerlized version (denoted “KDAML”) to handle the non-linear cases, and the Iterative Shrinkage-Thresholding Algorithm (ISTA) is employed to solve the optimization model. Intensive experimental results on various applications including retrieval, classification, and verification clearly demonstrate the superiority of our algorithm to other state-of-the-art metric learning methodologies.
Adaptive Sample Weight for Machine Learning Computer Vision Algorithms in V2X Systems

In machine learning, training sample set management has an important impact on the performance of visual detection and tracking algorithms, as corrupted training samples degrade the tracking performance, especially in practical scenarios such as vehicular networks. However, how to evaluate and remove the corrupted training samples still remains a challenging topic. In this paper, we propose a novel scheme to remove the corrupted training samples in visual tracking, which will improve the tracking performance dramatically. In the proposed scheme, a novel training sample set management method based on the adaptive sample weight is presented. Specifically, similarity learning is first utilized to evaluate the quality of training samples with similarity score. Then, if the similarity score is below a certain threshold, the training sample is deemed as the corrupted one and is removed from the training sample set. The experimental results show that the proposed scheme obtains superior performances on visual tracking benchmarks and vehicular scenarios.
Mineralogy, ore chemistry, and fluid inclusion studies in Gushfil Pb-Zn deposit, Irankuh mining district, SW Isfahan

Mineralogy, ore chemistry, and fluid inclusion studies in Gushfil Pb-Zn deposit, Irankuh mining district, SW Isfahan
Adaptive Estimators Show Information Compression in Deep Neural Networks.

To improve how neural networks function it is crucial to understand their learning process. The information bottleneck theory of deep learning proposes that neural networks achieve good generalization by compressing their representations to disregard information that is not relevant to the task. However, empirical evidence for this theory is conflicting, as compression was only observed when networks used saturating activation functions. In contrast, networks with non-saturating activation functions achieved comparable levels of task performance but did not show compression. In this paper we developed more robust mutual information estimation techniques, that adapt to hidden activity of neural networks and produce more sensitive measurements of activations from all functions, especially unbounded functions. Using these adaptive estimation techniques, we explored compression in networks with a range of different activation functions. With two improved methods of estimation, firstly, we show that saturation of the activation function is not required for compression, and the amount of compression varies between different activation functions. We also find that there is a large amount of variation in compression between different network initializations. Secondary, we see that L2 regularization leads to significantly increased compression, while preventing overfitting. Finally, we show that only compression of the last layer is positively correlated with generalization.
Partial Permutation Decoding for Several Families of Linear and <inline-formula> <tex-math notation="LaTeX">${\mathbb{Z}}_4$ </tex-math> </inline-formula>-Linear Codes

A general criterion to obtain s-PD-sets of minimum size s + 1 for partial permutation decoding, which enable correction up to s errors, for systematic codes over a finite field Fq and Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> -linear codes is provided. We show how this technique can be easily applied to linear cyclic codes over F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> , Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> -linear codes which are the Gray map image of a quaternary linear cyclic code, and some related codes such as quasi-cyclic codes. Furthermore, specific results for some linear and nonlinear binary codes, including simplex, Kerdock, Delsarte-Goethals, and extended dualized Kerdock codes are given. Finally, applying this technique, new s-PD-sets of size s + 1 for Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> -linear Hadamard codes of type 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">γ</sup> 4 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">δ</sup> , for all δ ≥ 4 and 1 <; s <; 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">δ</sup> - 3; and for Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> -linear simplex codes of type 4 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> , for all m ≥ 2 and 1 <; s ≤ 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m+1</sup> - 3, are also provided.
Concatenated <i>k</i>-Path Covers

Given a directed graph G(V,E), a k-(Shortest) Path Cover is a subset C of the nodes V such that every simple (or shortest) path in G consisting of k nodes contains at least one node from C. In this paper, we extend the notion of k-Path Covers such that the objects to be covered don't have to be single paths but can be concatenations of up to p simple (or shortest) paths. For the generalized problem of computing concatenated k-(Shortest) Path Covers, we present theoretical results regarding the VC-dimension of the concatenated path set in dependency of p as well as (approximation) algorithms. Subsequently, we study interesting special cases of concatenated k-Path Covers, in particular, covers for piecewise shortest paths, round tours and trees. For those, we show how the pruning algorithm for k-Path Cover computation can be abstracted and modified in order to also solve concatenated k-Path Cover problems. An extensive experimental study on different graph types proves the applicability and efficiency of our approaches.
Document recommendation based on the analysis of group trust and user weightings

Collaborative filtering (CF) has been applied in various domains to resolve problems related to information overload. In a knowledge-intensive environment, most works are processed through teamwork. A user on a team can reference task-related documents from other trusted members to support work on the task. However, the traditional personalised recommender systems no longer meet the demand of teams or groups. Therefore, this work proposes a novel document recommendation method based on a group-based trust model. Our method will analyse the degrees of trust among users in a group and then identify the trustworthy users. The proposed group trust consists of a hybrid personal trust (HPT) model and users’ importance (i.e. users’ activity, similarity and reputation) in a group. Group-based trust is then integrated with the user-based CF to recommend documents to users. The experiments demonstrate that the proposed method can provide better performance than other trust-based recommendation methods; it not only obtains reliable trust values to increase the accuracy of predictions but also enhances the recommendation quality.
A Survey of Stereoscopic 3D Just Noticeable Difference Models

Just noticeable difference (JND) for stereoscopic 3D content reflects the maximum tolerable distortion; it corresponds to the visibility threshold of the asymmetric distortions in the left and right contents. The 3D-JND models can be used to improve the efficiency of the 3D compression or the 3D quality assessment. Compared to 2D-JND models, the 3D-JND models appeared recently and the related literature is rather limited. In this paper, we give a deep and comprehensive study of the pixel-based 3D-JND models. To our best knowledge, this is the first review on 3D-JND models. Each model is briefly described by giving its rationale and main components in addition to providing exhaustive information about the targeted application, the pros, and cons. Moreover, we present the characteristics of the human visual system presented in these models. In addition, we analyze and compare the 3D-JND models thoroughly using qualitative and quantitative performance evaluation based on Middlebury stereo datasets. Besides, we measure the JND thresholds of the asymmetric distortion based on psychophysical experiments and compare these experimental results to the estimates from the 3D-JND models in order to evaluate the accuracy of each model.