Reviewerly/exHarmony huggingface dataset:
Robust Distracter-Resistive Tracker via Learning a Multi-Component Discriminative Dictionary

Discriminative dictionary learning (DDL) provides an appealing paradigm for appearance modeling in visual tracking. However, most existing DDL-based trackers cannot handle drastic appearance changes, especially for scenarios with background cluster and/or similar object interference. One reason is that they often suffer from the loss of subtle visual information, which is critical to distinguish an object from distracters. In this paper, we explore the use of activations from the convolutional layer of a convolutional neural network to improve the object representation and then propose a robust distracter-resistive tracker via learning a multi-component discriminative dictionary. The proposed method exploits both the intra-class and inter-class visual information to learn shared atoms and the class-specific atoms. By imposing several constraints into the objective function, the learned dictionary is reconstructive, compressive, and discriminative, and thus can better distinguish an object from the background. In addition, our convolutional features have structural information for object localization and balance the discriminative power and semantic information of the object. Tracking is carried out within a Bayesian inference framework where a joint decision measure is used to construct the observation model. To alleviate the drift problem, the reliable tracking results obtained online are accumulated to update the dictionary. Both the qualitative and quantitative results on the CVPR2013 benchmark, the VOT2015 data set, and the SPOT data set demonstrate that our tracker achieves substantially better overall performance against the state-of-the-art approaches.
Voxel-based edge bundling through direction-aware kernel smoothing

Relational data with a spatial embedding and depicted as node-link diagram is very common, e.g, in neuroscience, and edge bundling is one way to increase its readability or reveal hidden structures. This article presents a 3D extension to kernel density estimation-based edge bundling that is meant to be used in an interactive immersive analysis setting. This extension adds awareness of the edges’ direction when using kernel smoothing and thus implicitly supports both directed and undirected graphs. The method generates explicit bundles of edges, which can be analyzed and visualized individually and as sufficient as possible for a given application context, while it scales linearly with the input size.
Message Passing for Joint Registration and Tracking in Multistatic Radar

Sensor registration is fundamental in sensor fusion. Inaccuracies in sensor location and rotation can manifest themselves into the measurements used in Multiple Target Tracking (MTT), and dramatically degrade its performance. These registration parameters are often estimated separately to any multitarget estimation, which could lead to increased computational expense, and also to systematic errors. Recent works have shown that MTT algorithms derived from Belief Propagation (BP) are computationally efficient and highly scalable for large tracking scenarios. This work presents a hierarchical Bayesian model inspired by single-cluster methods from the Random Finite Set (RFS) literature, that allow for the registration parameters to be estimated jointly with the multiple target tracking. Simulations are carried out on a multistatic radar network containing two radars with a relative range and azimuth bias between them. Results are presented for a particle-BP MTT algorithm, and it's performance is compared to that of a Sequential Monte Carlo (SMC)-Probability Hypothesis Density (PHD) filter. The results show that the BP algorithm outperforms the PHD implementation in terms of accuracy by around 10%.
Kernel Stability for Model Selection in Kernel-Based Algorithms

Model selection is one of the fundamental problems in kernel-based algorithms, which is commonly done by minimizing an estimation of generalization error. The notion of stability and cross-validation (CV) error of learning machines consists of two widely used tools for analyzing the generalization performance. However, there are some disadvantages to both tools when applied for model selection: 1) the stability of learning machines is not practical due to the difficulty of the estimation of its specific value and 2) the CV-based estimate of generalization error usually has a relatively high variance, so it is prone to overfitting. To overcome these two limitations, we present a novel notion of kernel stability (KS) for deriving the generalization error bounds and variance bounds of CV and provide an effective approach to the application of KS for practical model selection. Unlike the existing notions of stability of the learning machine, KS is defined on the kernel matrix; hence, it can avoid the difficulty of the estimation of its value. We manifest the relationship between the KS and the popular uniform stability of the learning algorithm, and further propose several KS-based generalization error bounds and variance bounds of CV. By minimizing the proposed bounds, we present two novel KS-based criteria that can ensure good performance. Finally, we empirically analyze the performance of the proposed criteria on many benchmark data, which demonstrates that our KS-based criteria are sound and effective.
Dynamic Scaling of Video Analytics for Wide-Area Tracking in Urban Spaces

Smart City deployments typically have thousands to even hundreds of thousands of Surveillance cameras. Rapid advancements in computer vision techniques due to Deep Neural Networks enable using these camera feeds for performing non-trivial analytics. Tracking a moving object of interest using a large network of cameras, also known as object reidentification, is one such analytic that empowers city administration with capabilities such as finding missing people or prioritizing emergency vehicles. We have built Anveshak, a framework for distributed wide-area tracking. Anveshak fills in the shortcomings of existing Big Data and Deep Learning frameworks by - exposing an intuitive and composable programming model; automating application deployment and orchestration across edge, fog and cloud resources and providing knobs to the user for managing the application performance. The knobs lend the application the ability to scale potentially to thousands of cameras. In this proposal we have designed two representative applications; missing person tracking and priority signalling for emergency vehicles. We empirically verify that the application scales to 1000 cameras on a Cloud-only deployment of 10 Azure VMs with 8 cores and 32GB RAM each. Alternatively, it scales to 500 cameras on a simulated setup of 100 edge, 30 fog, and 1 Cloud VM. We also highlight the effect of the knobs on the application performance. designed two representative applications; missing person tracking and priority signalling for emergency vehicles. We empirically verify that the application scales to 1000 cameras on a Cloud-only deployment of 10 Azure VMs with 8 cores and 32GB RAM each. Alternatively, it scales to 500 cameras on a simulated setup of 100 edge, 30 fog, and 1 Cloud VM. We also highlight the effect of the knobs on the application performance.
Towards an Exhaustive Framework for Online Social Networks User Behaviour Modelling

Since the advent of Web 2.0, Online Social Networks (OSNs) represent a rich opportunity for researchers to collect real user data and to explore OSNs user behaviour. Based on the current challenges and future directions proposed in literature, we aim to investigate how to comprehensively model OSNs user behaviours, by exploiting and combining user data of different nature. We propose to use hypergraphs as a model to easily analyse and combine structural, semantic, and activity-related user information, and to study their evolution over time. This novel user behaviour modelling technique will converge in open, efficient, and scalable libraries, which will be integrated into a modular framework able to handle the data crawling process from several OSNs.
Indoor Scene recognition for Micro Aerial Vehicles Navigation using Enhanced SIFT-ScSPM Descriptors

In this paper, a new scene recognition visual descriptor called Enhanced Scale Invariant Feature Transform-based Sparse coding Spatial Pyramid Matching (Enhanced SIFT-ScSPM) descriptor is proposed by combining a Bag of Words (BOW)-based visual descriptor (SIFT-ScSPM) and Gist-based descriptors (Enhanced Gist-Enhanced multichannel Gist (Enhanced mGist)). Indoor scene classification is carried out by multi-class linear and non-linear Support Vector Machine (SVM) classifiers. Feature extraction methodology and critical review of several visual descriptors used for indoor scene recognition in terms of experimental perspectives have been discussed in this paper. An empirical study is conducted on the Massachusetts Institute of Technology (MIT) 67 indoor scene classification data set and assessed the classification accuracy of state-of-the-art visual descriptors and the proposed Enhanced mGist, Speeded Up Robust Features-Spatial Pyramid Matching (SURF-SPM) and Enhanced SIFT-ScSPM visual descriptors. Experimental results show that the proposed Enhanced SIFT-ScSPM visual descriptor performs better with higher classification rate, precision, recall and area under the Receiver Operating Characteristic (ROC) curve values with respect to the state-of-the-art and the proposed Enhanced mGist and SURF-SPM visual descriptors.
Software Engineering Methodologies for the Evaluation and Monitoring of Projects of Higher Education Students

The change of strategies in teaching in Higher Education motivated the present work. With the application of cooperative and collaborative learning in the classroom, better results were obtained in the subject Research Workshop II, which is taught in Higher Education in Engineering. The work applying cooperative and collaborative learning impacted on the way to evaluate the participants, generating the need to find a way that allowed to have the elements to know who was contributing, which team progressed slower according to the program, as well as their peers in the development of the research project. In recent years in a significant number of companies dedicated to Information Technology and Communications have incorporated agile methodologies to manage different projects in very diverse branches, but especially in Software Engineering. In particular, the methodology used is Scrum. Therefore, Scrum was incorporated to develop students’ projects, but also to evaluate the progress that students are making in the development of projects. With the incorporation of Scrum, better results have been obtained in the projects that the students develop, they and the evaluator are aware of the progress and delays they have in achieving them, in order to take actions that allow the products requested to be completed in a timely manner the curricular program of the subject.
A Sensitivity and Performance Analysis of Word2Vec Applied to Emotion State Classification Using a Deep Neural Architecture

Word2Vec has become one of the most relevant neural networks to generate word embeddings for NLP applications. Despite that, little has been investigated in terms of its sensitivity to the word vectors’ length (n) and the window size (w). Thus, the present paper performs a sensitivity analysis of Word2Vec when applied to generate word embeddings for a deep neural architecture used to classify emotion states in tweets. Furthermore, we present a computational performance analysis to investigate how the system scales as a function of n and w in different computing environments. The results show that a window size of approximately half the tweet length (8 words) and a value of $$n = 50$$ suffices to find good performances. Also, by increasing these values one may unnecessarily increase the computational cost.
On the Customization of Batch Refactoring

Refactorings are program transformations to improve code structure quality. Developers often apply these transformations in a batch, i.e., transformations in a sequence on a certain program location in order to achieve a specific goal. Either individual or batch refactorings often need to be customized to a particular program context. Otherwise, the developer may not be able to achieve his goal. Existing tools provide limited support for refactoring customization. IDE features do not allow developers to properly create their own custom refactoring. As a consequence, developers often feel reluctant to use these tools, and end up applying their refactorings manually. This position paper proposes an approach for developers to customize program transformations for their refactorings. The approach allows a developer to: (i) compose an individual or batch refactoring according to his context's needs, and (ii) reuse the custom refactorings in similar contexts. This novel approach has the potential to increase the use of automated refactoring, while allowing software companies to better tailor the IDE to their specific refactoring practices. From a research perspective, our approach motivates researchers to investigate better practices for refactoring customization.
Fire risk assessment and factor analysis of buildings based on multi-target decision and fuzzy mathematical model

Reasonable fire risk assessment system can demonstrate the occurrence of fire and ensure the safe evacuation of fire. The selected indicators of the evaluation system play a fundamental role in the establishment of the system. In the evaluation model, the general problem is transformed into a speci fic mathematical model by using the method of fuzzy information processing, which makes the evaluation result more direct and measurable. This paper uses a measure of feature attributes to measure the contribution of clusters, that is, the method of calculating the weight of features. When the value of the equilibrium discriminant function reaches the minimum value, the clustering result under the optimal condition can be obtained. Then, the author analyzes the fire risk assessment and factor analysis of buildings based on multi-target decision and fuzzy mathematical model. The simulation results show that the improved fuzzy model proposed in this paper makes the calculation results more accurate. The fire risk analysis and control system based on the theory of fuzzy information processing can be widely used in various high-rise buildings to ensure safety.
Development of Visual Elements for Accurate Simulation

Virtual reality (VR) is a powerful tool to study human behavior and performance under the controlled conditions of a laboratory simulation. Simulation fidelity, particularly in the visual representation of the virtual environment (VE), continues to improve with ongoing advances in computer graphics technology. Because the software tools used to create these VEs are typically the same as those used by the gaming industry, the pervasiveness of high-fidelity video games raises expectations of quality, detail, and lighting in VR applications. However, high-quality models of equipment and environments are often not readily available, and developers may not be familiar with how to implement the latest techniques like physically based rendering (PBR) and photogrammetry. In addition, creating these assets in a timely manner may require several team members with different skillsets working together, and maintaining a consistent aesthetic can be a challenge. Because visual consistency is critical for maintaining immersion and controlling visual stimuli in research as well as in training, researchers from the National Institute for Occupational Safety and Health (NIOSH) created an asset development workflow. This paper describes the workflow, precision modeling techniques, and review process as well as PBR and photogrammetry techniques. It also details lessons learned throughout the process of creating numerous photorealistic VR assets.
On Anonymous Commenting

In many online services, anonymous commenting is not possible for the users; therefore, the users can not express their critical opinions without disregarding the consequences. As for now, naïve approaches are available for anonymous commenting which cause problems for analytical services on user comments. In this paper, we explore anonymous commenting approaches and their pros and cons. We also propose methods for anonymous commenting where it's possible to protect the user privacy while allowing sentimental analytics for service providers. Our experiments were conducted on a real dataset gathered from Instagram comments which indicate the effectiveness of our proposed methods in privacy protection and sentimental analytics. The proposed methods are independent of a particular website and can be utilized in various domains.
Learning Distributed Representation of Recipe Flow Graphs via Frequent Subgraphs

Recent rapid increase in health awareness is producing a large amount of user generated cooking recipes in online community sites. For the effective use of such cooking recipes, it is necessary not only to understand their meaning but also to extract certain structures among them, by paying attention to cooking steps in detail. One of the most precise representations of cooking procedure is the recipe flow graph that is a directed acyclic graph having recipe terms in vertices and their relations in edges. In this paper, as a preliminary attempt for acquiring a new vector representation reflecting various aspects of cooking procedures, we propose a simple method to learn a distributed representation of recipe flow graphs using frequent fragments of cooking procedures. Experiments using real world dataset are conducted to compare the distributed representation of recipe flow graphs and that of recipe texts. As a result, we confirm that the proposed representation can capture the difference among recipes well, and it is suitable for the classification tasks.
Distributed Anomaly Detection Method in Wireless Sensor Networks Based on Temporal-Spatial QSSVM

In Wireless Sensor Networks (WSNs), abnormal sensing data is easily generated due to factors such as the harsh working environment, sensor faults and external events. In order to enhance the detection rate of abnormal data and reduce the false positive rate, we propose a distributed anomaly detection method using one-class quarter-sphere support vector machine (QSSVM) based on temporal-spatial fusion in WSNs. Firstly, according to the synthetic data, the temporal-spatial QSSVM model is trained to determine the relevant parameters. Secondly, the trained QSSVM model is used to classify the streaming data in WSNs, and the abnormal data types are classified into noise, faults and events. Finally, the method decides whether to update the classification model based on whether the new sample has an effect on the boundary of the hypersphere. The experimental results illustrate that the proposed method has a detection rate of 96% compared with other three methods, and the false positive rate is only 14%.
Exploring the Relationship Between Web Presence and Web Usability in Peruvian Universities

Currently the use of the internet is an essential requirement for any business that aspires to reach many customers, sharing the information of their products, their achievements, their day to day, etc. This leads to the generation of a quantity of information year after year by these companies, which allows them to gain presence in the network. This reality also applies to the education sector, so this work will focus on Peruvian universities. Universities generate a large amount of content, and therefore it is necessary to be easy to find and read. In this way they will not be at a disadvantage compared to other universities that compete to attract more applicants. Our motivation is based on a study conducted in Turkey whose results show that the universities with the greatest web presence in that country have better usability. With this motivation, it was decided to do this work that, repeating the Turkish experience, verifies if there is a relationship in the Peruvian universities with greater presence, according to a ranking of universities called Webometrics. This evaluation takes as reference other usability studies, among them those that use tests with users and questionnaires, to obtain a ranking of web usability and to be able to contrast it with the web presence ranking.
rapoptosis: Renatusu via Apoptosis - Prototyping Using Clothes

To redefine the way artifacts are inexhaustively produced, consumed, and discarded, the authors define a new form of death for “things” and propose a system in which artifacts circulate and transform themselves in our society. This system is inspired by the characteristics of organisms, such as self-death and regeneration, in nature. We redefine the death of artifacts as the time when their intrinsic existence value ceases to exist in the relationship between the object and the user, rather than when it ceases to function (self-death: apoptosis), and we propose a method to allow artifacts to judge their intrinsic existence value themselves and survive by having them change users autonomously (rebirth: renatusu). We propose to call this system “rapoptosis” (renatusu via apoptosis). In this paper, we report the results of the rapoptosis prototyping using clothes and investigate the current general methods, timing, and potential requirements of methods to dispose of clothes.
Uncertainty based model selection for fast semantic segmentation

Semantic segmentation approaches can largely be divided into two categories. One with accurate results but slow inference, and another one with real-time inference but sacrificing some performance for speed. In this paper, we try to exploit the benefits of both categories, i.e. accuracy and speed, through the use of model selection techniques. Using the uncertainty, calculated from the entropy map, as our selection criterion, we leverage the speed of the fast, but not so accurate, model for regions with high certainty, that comprise the majority of the input image, while for a few, carefully selected regions with low certainty we employ an accurate, yet expensive, model, to predict the semantic labels. Our experimental results show that our method greatly boosts the performance of the baseline model, while retaining reasonable inference speeds.