Reviewerly/exHarmony huggingface dataset:
Discriminative Saliency-pose-attention Covariance for Action Recognition

Most covariance-based representations of actions are focused on the statistical features of poses by empirical averaging weighting. Note that these poses have a variety of saliency levels for different actions. Neglecting pose saliency could degrade the discriminative power of the covariance features, and further reduce the performance of action recognition. In this paper, we propose a novel saliency weighting covariance feature representation, Saliency-Pose-Attention Covariance(SPA-Cov), which reduces the negative effects from the ambiguous pose samples. Specifically, we utilize a discriminative approach to derive probability distribution of action categories for each pose, which is modeled by the uncertainty of information entropy to obtain the salient weighting. Experimental results show that our proposed method efficiently improves the discriminative power of the generated covariance. In some databases, the proposed SPA-Cov outperforms the state-of-the-art variant methods which are based on kernel matrix, Bayesian posterior features, temporal hierarchical features, etc.
Effort Estimation of Back-end Part of Software using Chaotically Modified Genetic Algorithm

The focus of Software Development Effort Estimation (SDEE) is to precisely predict the estimation of effort and time required for successfully developing a software project.From the past few years, data-intensive applications with a huge back-end part are contributing to the overall effort of projects.Therefore, it is becoming more important to add the back-end part to the SDEE process.This paper proposes an Evolutionary Learning (EL) based hybrid artificial neuron termed as dilationerosion perceptron (DEP) framework from the mathematical morphology (MM) having its foundation in complete lattice theory (CLT) for solving the SDEE problem.In this work, we used the DEP (CMGA) model utilizing a chaotically modified genetic algorithm (CMGA) for the construction of DEP parameters.The proposed method uses the ER diagram artifacts such as aggregation, specialization, generalization, semantic integrity constraints, etc. for calculating the SDEE of back-end part of the business software.Furthermore, the proposed method was tested over two different datasets, one is existing and the other one is a self-developed dataset.The performance of the given method is then evaluated by three popular performance metrics, exhibiting better performance of the DEP (CMGA) model for solving the SDEE problems.
Robust long‐term correlation tracking with multiple models

To address the challenge of repetitive target appearance variation and frequent occlusion, existing visual tracking methods either handle corrupted samples or correct the appearance model. In this study, the authors propose a novel framework that successfully combines these two strategies. In their method, the base tracker is an improved discriminative correlation filter-based tracker, in which an independent classifier is employed to alleviate the problem of corrupted samples; the best model is selected for improvement from a group of models, which they call a ‘model colony’. The model colony is composed of models updated via different processes. The correlation output and the peak-to-sidelobe ratio are used to evaluate each model in the model colony. In addition, they propose a novel criterion called the maximum-to-others ratio for superior model selection. Experiments on 80 challenging sequences show that their tracker outperforms state-of-the-art trackers. In addition, experimental results demonstrate that their formulation significantly improves the performance of their base tracker.
Time Windowed Data Structures for Graphs

We present data structures that can answer time windowed queries for a set of timestamped events in a relational event graph. We study the relational event graph as input to solve (a) time windowed decision problems for monotone graph properties, such as disconnectedness and bipartiteness, and (b) time windowed reporting problems such as reporting the minimum spanning tree, the minimum time interval, and the graph edit distance for obtaining spanning forests. We also present results of window queries for counting subgraphs of a given pattern, such as paths of length 2 (in general graphs) and paths of length 3 (in bipartite graphs), quadrangles and complete subgraphs of a fixed order or of all orders l ≥ 3 (i.e., cliques of size l). These query results can be used to compute graph parameters that are important for social network analysis, e.g., clustering coefficients, embeddedness and neighborhood overlapping.
Fusion is great, and interpretable fusion could be exciting for theory generation: Response to Pater

From my perspective, Pater’s (2019) target article does a great service both to researchers who work in generative linguistics and to researchers who utilize neural networks—and especially to researchers who might find themselves wanting to do both by harnessing the insights of each tradition. The fusion of theories of linguistic representation and probabilistic learning techniques has certainly led to many interesting and valuable insights about the nature of both linguistic representation and the language acquisition process. However, I feel that the most exciting aspect of Pater’s article is the increasing interpretability of neural network models, especially when combined with insights from the theoretical framework of generative linguistics. This allows for the possibility that neural networks could be used to actually generate new theories of representation. I describe how I think this theory-generation process might work with interpretable neural networks.
Cheap devices bring quake damage sensing to the masses

With technology borrowed from cellphones, even modest structures can be automatically scanned for hidden damage.
Bootstrapping Accountability and Privacy to IPv6 Internet without Starting from Scratch

Accountability and privacy are considered valuable but conflicting properties in the Internet, which at present does not provide native support for either. Past efforts to balance accountability and privacy in the Internet have unsatisfactory deployability due to the introduction of new communication identifiers, and because of large-scale modifications to fully deployed infrastructures and protocols. The IPv6 is being deployed around the world and this trend will accelerate. In this paper, we propose a private and accountable proposal based on IPv6 called PAVI that seeks to bootstrap accountability and privacy to the IPv6 Internet without introducing new communication identifiers and large-scale modifications to the deployed base. A dedicated quantitative analysis shows that the proposed PAVI achieves satisfactory levels of accountability and privacy. The results of evaluation of a PAVI prototype show that it incurs little performance overhead, and is widely deployable.
Faster queries for longest substring palindrome after block edit

Palindromes are important objects in strings which have been extensively studied from combinatorial, algorithmic, and bioinformatics points of views. Manacher [J. ACM 1975] proposed a seminal algorithm that computes the longest substring palindromes (LSPals) of a given string in O(n) time, where n is the length of the string. In this paper, we consider the problem of finding the LSPal after the string is edited. We present an algorithm that uses O(n) time and space for preprocessing, and answers the length of the LSPals in O(l + log log n) time, after a substring in T is replaced by a string of arbitrary length l. This outperforms the query algorithm proposed in our previous work [CPM 2018] that uses O(l + log n) time for each query.
The Relationship Between Financial Development and Innovation: Empirical Evidence from Selected Asian Countries

This study extends research on the relationship between financial development (FD) and innovation by dividing FD into bank-based FD and stock-market-based FD and investigating their effects on innovation. The number of patent applications is used as a proxy for innovative activities, and the financial development of a country is measured by the development of the banking sector and the stock market. The study employs the two-step Generalized Method of Moments (GMM) to test the relationship using a panel data set in 25 various Asian countries in the period from 2000 to 2015. The development of the banking sector and the stock market increase the number of patent applications, but their interaction calculated by financial structure is not related to innovation. The finding indicates that the development of a financial system encourages innovation regardless of the difference in the development level between its components. The study provides more insights into the impact of financial market development on the domestically economic activities.
Non-native writers and the use of appraisal resources in research article introductions

Writing an introduction section of a research article usually requires the interpersonal voice and arguments for building up a sense of persuasiveness that will entice readers. A quality research article introduction can be achieved by using appraisal resources to represent the writers’ ideas and propositions effectively. Using twenty introduction sections of research articles written in English by Indonesian and Chinese writers as non-native writers, this study examines the use of appraisal resources and compares their distributions. By employing textual analysis and using Appraisal resources theory drawn from the work of Martin and White (2005), the findings reveal that there are both similarities and differences in the distribution of appraisal resources between Indonesian and Chinese writers in the introduction sections of research articles. In terms of similarities, both Indonesian and Chinese writers use attitude (appreciation), expand (engagement), and force (graduation) as the most used appraisal resources in their writing. What is different, however, is that the Indonesian writers seem more likely than Chinese writers to use appraisal resources overall, except for graduation resources, which were used more often by the Chinese writers. This article discusses some of the pedagogical implications for those who are teaching students of English as a foreign language and want to improve and strengthen their voice and arguments in the writing of research article introductions.
Circular Non-uniform Sampling Patch Inputs for CNN Applied to Multiple Sclerosis Lesion Segmentation

Convolutional Neural Networks (CNN) have been obtaining successful results in the task of image segmentation in recent years. These methods use as input the sampling obtained using square uniform patches centered on each voxel of the image, which could not be the optimal approach since there is a very limited use of global context. In this work we present a new construction method for the patches by means of a circular non-uniform sampling of the neighborhood of the voxels. This allows a greater global context with a radial extension with respect to the central voxel. This approach was applied on the 2015 Longitudinal MS Lesion Segmentation Challenge dataset, obtaining better results than approaches using square uniform and non-uniform patches with the same computational cost of the CNN models.
Special issue on machine learning in medical imaging

Conventional computer-aided detection systems (CADs) for colonoscopic images utilize shape, texture, or temporal information to detect polyps, so they have limited sensitivity and specificity. This study proposes a method to extract possible polyp features automatically using convolutional neural networks (CNNs). The objective of this work aims at building up a light-weight dual encoder-decoder model structure for polyp detection in colonoscopy Images. This proposed model, though with a relatively shallow structure, is expected to have the capability of a similar performance to the methods with much deeper structures. The proposed CAD model consists of two sequential encoder-decoder networks that consist of several CNN layers and full connection layers. The front end of the model is a hetero-associator (also known as hetero-encoder) that uses backpropagation learning to generate a set of reliably corrupted labeled images with a certain degree of similarity to a ground truth image, which eliminates the need for a large amount of training data that is usually required for medical images tasks. This dual CNN architecture generates a set of noisy images that are similar to the labeled data to train its counterpart, the auto-associator (also known as auto-encoder), in order to increase the successor's discriminative power in classification. The auto-encoder is also equipped with CNNs to simultaneously capture the features of the labeled images that contain noise. The proposed method uses features that are learned from open medical datasets and the dataset of Zhejiang University (ZJU), which contains around one thousand images. The performance of the proposed architecture is compared with a state-of-the-art detection model in terms of the metrics of the Jaccard index, the DICE similarity score, and two other geometric measures. The improvements in the performance of the proposed model are attributed to the effective reduction in false positives in the auto-encoder and the generation of noisy candidate images by the hetero-encoder.
iWatch: A Fall and Activity Recognition System Using Smart Devices

Recent reports show that the average life expectancy is increasing worldwide, posing significant overhead on healthcare systems and increasing demands on long-term care facilities.One of the grand challenges directly related to growing ageing societies is the implications of falling.Many elderly people live alone, especially those in Western countries who cannot afford living in a senior house or retirement facility.In such cases, not only falling is a major concern, but also daily activities must be continuously monitored and analyzed to provide immediate support when needed.Vital signs and environment context are also crucial conditions for pre-and post-event assessments.Thanks to technology advancements and widespread adoption of the Internet of Things which enables us to provide smart and ubiquitous healthcare services.In this paper, we propose iWatch, a smart and flexible system for fall detection and activity recognition using common smart devices, a smartwatch and a smartphone.Machine learning techniques are used to build efficient and highly accurate activity recognition classifiers.iWatch also provides health risk analysis using threshold-based models and leverages visualization tools to better communicate with the user.iWatch is a promising technology that provides a small step in a giant leap to revolutionize healthcare services, especially for those who needs extra care.
Improvement in the Efficiency of a Distributed Multi-Label Text Classification Algorithm Using Infrastructure and Task-Related Data

Distributed computing technologies allow a wide variety of tasks that use large amounts of data to be solved. Various paradigms and technologies are already widely used, but many of them are lacking when it comes to the optimization of resource usage. The aim of this paper is to present the optimization methods used to increase the efficiency of distributed implementations of a text-mining model utilizing information about the text-mining task extracted from the data and information about the current state of the distributed environment obtained from a computational node, and to improve the distribution of the task on the distributed infrastructure. Two optimization solutions are developed and implemented, both based on the prediction of the expected task duration on the existing infrastructure. The solutions are experimentally evaluated in a scenario where a distributed tree-based multi-label classifier is built based on two standard text data collections.
Clustering and Classification of Breathing Activities by Depth Image from Kinect

This paper describes a new approach of the non-contact capturing method of breathing activities using the Kinect depth sensor. To process the data, we utilized feature extraction on time series of mean depth value and optional feature reduction step. The next process implemented a machine learning algorithm to execute clustering on the resulted data. The classification had been realized on four different subjects and then, continued to use 10-fold cross-validation and Support Vector Machine (SVM) classifier. The most efficient classifier is SVM radial with the grid reached the best accuracy for all of the subjects.
Optimizing over a Restricted Policy Class in MDPs

We address the problem of finding an optimal policy in a Markov decision process (MDP) under a restricted policy class defined by the convex hull of a set of base policies. This problem is of great interest in applications in which a number of reasonably good (or safe) policies are already known and we are interested in optimizing in their convex hull. We first prove that solving this problem is NP-hard. We then propose an efficient algorithm that finds a policy whose performance is almost as good as that of the best convex combination of the base policies, under the assumption that the occupancy measures of the base policies have a large overlap. The running time of the proposed algorithm is linear in the number of states and polynomial in the number of base policies. A distinct advantage of the proposed algorithm is that, apart from the computation of the occupancy measures of the base policies, it does not need to interact with the environment during the optimization process. This is especially important (i) in problems that due to concerns such as safety, we are restricted in interacting with the environment only through the (safe) base policies, and (ii) in complex systems where estimating the value of a policy can be a time consuming process.
The Remote Experimentation as the Practical-Oriented Basis of Inclusive Engineering Education

The challenges and solutions for inclusive engineering education are discussed in this paper. We propose remote experimentation as the practical-oriented basis to train engineers with disabilities in the fields of Computer Science and Information Technologies. The structure and the functionality of international GOLDi network that unites partner universities from Germany, Australia, Ukraine, Armenia and Georgia is given. The possibilities of REIoT complex for studying the features of embedded systems design and Internet of Things technologies as well as an overview of ISRT laboratory for embedded software development and testing are given. The presented Remote Laboratories are successfully used to improve educational services quality and accessibility as well as to strengthen the practical component of the learning process.
Repositioning privacy concerns: Web servers controlling URL metadata

Uniform Resource Locators reveal a significant amount of metadata about user actions, in ways that inherently violate our natural expectations of privacy. Adequately protecting this information is an important issue tackled (sometimes partially) in areas such as secure transport protocols, terminal encryption and caching strategies. A different (and complementary) approach would be to design the application namespace to minimise privacy leakage. Our goal is to develop a different practical concept of this approach, where service providers enforce fully transient URL namespaces that intentionally conceal data through encryption. We aim to determine what would be the design challenges and required compromises to make this a feasible technique to protect data privacy. For starters, we gather requirements from the constraints of URLs in general and compatibility issues seen in web applications, and propose a mapping process for a namespace of encrypted URLs. We implement this approach over an existing web development framework, and analyse the outcome workload from different popular websites to measure its impact in various conditions. Based on our results, we discuss critical design and implementation choices, consider deployment issues that were encountered, and what compromises can be made to address them, if the web service providers want to embed user privacy in their services. Based on this analysis it can be concluded that this type of privacy approach is expensive, with a significant impact in performance and deployment costs that increases with the expected degree of privacy, but there is also room for improvement in various areas. Furthermore, privacy implemented in this way is not a replacement for other types of privacy solutions, but rather a complementary or even conflicting approach, driven by entirely different motives.
Assessment of Fire Hazard on the Readymade Garment Industry in Chittagong City, Bangladesh

The Readymade Garments (RMG) industries are in Bangladesh is the largest contributor to the national export earnings and second largest in GDP of the country. Starting from the late Seventies as minor as well as non-traditional sector with a negligible of exporting, the RMG sector has dramatically grown up a geometric progression over the few decades. Although the country has a glorious history of textile, cloths and clothing’s from the ancient period of time. At present, about 75% of the total exports of the country having from this sector and one of the principle key points of employment. Over the preceding decade, fire and fire hazard is the continual problem in the country’s readymade garments industries. Only in 2012 about 169 people died and huge people are injured because of fire hazards in RMGs. Considering this as a crucial problem, the study was carried out a geo-spatial analysis on Chittagong Export Processing Zone (CEPZ) and Baizid Industrial hub of Chittagong city, Bangladesh. The study was mainly done by primary field survey while secondary sources were also used in comparing and basement of analysis. There are two types of parameters (Hard and Soft Parameters) generally used to assess the fire hazard of readymade garments. The research has been done by adopting the soft parameter, as exit door locked, emergency exit in the factory, fire extinguisher etc. The Fire Risk Index has been developed on the basis of 19 soft parameters. In Chittagong EPZ area 25 garments factories have been surveyed instantly to observe the current status of fire risk for the lack of 19 parameters. Results show that in CEPZ area for 19 parameters the mean safety is 90.45% or 9.55% deviation from the standard. On the contrary, the average condition of 19 parameters is 80.29% fluctuation from the benchmark in the Baizid Industrial area though it is considered as good condition but nearest to the average. Electric short circuit is the prime cause of fire in CEPZ area and also in Baizid industrial hub. At the eleventh hour, it is found from the study that about 80% garments in Baizid Industrial area under in good condition which is (10-20) % deviation from ideal case. On the other hand in CEPZ area about 80% garments are fallen excellent condition, it is only 8% seen in Baizid area. The study also reveals that there is no garments in both area which is in poor or average condition or vulnerable to fire hazards but in Baizid it is about 20% garments are fallen average condition or nearly vulnerable. Before conducting the study, it is seemed that it will be found the garments will be more vulnerable to fire hazards but at the end of the day it is proved as erroneous, for the recurrence of fire hazards in the last few years they have raised the magnitude of fire safety in almost every cases and now the garments are more secured in case of fire hazards.