Reviewerly/exHarmony huggingface dataset:
The Price of Free Illegal Live Streaming Services

As Internet streaming of live content has gained on traditional cable TV viewership, we have also seen significant growth of free live streaming services which illegally provide free access to copyrighted content over the Internet. Some of these services draw millions of viewers each month. Moreover, this viewership has continued to increase, despite the consistent coupling of this free content with deceptive advertisements and user-hostile tracking. In this paper, we explore the ecosystem of free illegal live streaming services by collecting and examining the behavior of a large corpus of illegal sports streaming websites. We explore and quantify evidence of user tracking via third-party HTTP requests, cookies, and fingerprinting techniques on more than $27,303$ unique video streams provided by $467$ unique illegal live streaming domains. We compare the behavior of illegal live streaming services with legitimate services and find that the illegal services go to much greater lengths to track users than most legitimate services, and use more obscure tracking services. Similarly, we find that moderated sites that aggregate links to illegal live streaming content fail to moderate out sites that go to significant lengths to track users. In addition, we perform several case studies which highlight deceptive behavior and modern techniques used by some domains to avoid detection, monetize traffic, or otherwise exploit their viewers. Overall, we find that despite recent improvements in mechanisms for detecting malicious browser extensions, ad-blocking, and browser warnings, users of free illegal live streaming services are still exposed to deceptive ads, malicious browser extensions, scams, and extensive tracking. We conclude with insights into the ecosystem and recommendations for addressing the challenges highlighted by this study.
Gamified Automation in Immersive Media for Education and Research

The potential of using video games as well as gaming engines for educational and research purposes is promising, especially with the current progress of Industry 4.0 technologies such as augmented and virtual reality devices. However, it is important to be aware of the barriers of these technologies. Integrating additional software libraries into current developer environments would not only increase accessibility for the general public, but would gamify the development process through multiplayer functionality. In this paper we will briefly discuss a couple case studies demonstrating the usefulness of using Unreal Engine 4 for collaborative research purposes. In addition, we present several ideas on how to extend such development environments to further assist researchers and students to easily create prototypes for the purpose of gamified scientific creativity and inquiry.
Invariant-Equivariant Representation Learning for Multi-Class Data

Representations learnt through deep neural networks tend to be highly informative, but opaque in terms of what information they learn to encode. We introduce an approach to probabilistic modelling that learns to represent data with two separate deep representations: an invariant representation that encodes the information of the class from which the data belongs, and an equivariant representation that encodes the symmetry transformation defining the particular data point within the class manifold (equivariant in the sense that the representation varies naturally with symmetry transformations). This approach is based primarily on the strategic routing of data through the two latent variables, and thus is conceptually transparent, easy to implement, and in-principle generally applicable to any data comprised of discrete classes of continuous distributions (e.g. objects in images, topics in language, individuals in behavioural data). We demonstrate qualitatively compelling representation learning and competitive quantitative performance, in both supervised and semi-supervised settings, versus comparable modelling approaches in the literature with little fine tuning.
ISO 9001, 2015 Quality Manual Template for Service Industry

This paper describes a blue print for the Quality Management System (QMS) for a company that is pursuing ISO 9001: 2015 certification.The Quality Manual (QM) proposed in this study could be used as a template to demonstrate an example to follow.The presented example was implemented in a high profile administrative work such as tax administration or medical claim administration.The significant changes in the new ISO 9001 are; risk-based thinking, top management commitment, establishing the context of the organization.Quality procedures are the activities and processes that are the building blocks of the QMS.These activities and processes are intended to fulfil the requirements of the QMS.A medical claim administration, organization will be presented here as an example for a blueprint for QM.This template will ensure that relevant documented quality procedures are available, utilized and adhered to by all its employees in all processes and activities that are relevant to the Quality System.
Review of Conventional Explanations

Abstract This chapter is aimed at objectively assessing various possible reasons for Africa’s poverty and underdevelopment. It reviews the conventional or popular explanations for Africa’s prolonged predicament, as well as other factors possibly negatively impacting Africa. The topics addressed are colonial legacies, ethnicism and neo-patrimonialism; institutions, governance and democracy, the role of government; natural conditions like climate and geography, and other factors including corruption and globalization. The author sizes up the existing views or discourses from critical perspective and, in the process, sheds new lights on what are commonly misunderstood.
Audio and Video Streaming in Telepresence Application Using WebRTC for Healthcare System

Currently, health care in Lower- and Middle-Income Countries (LMICs) is suffering from shortage of trained physicians in rural areas. The development of certain technologies, particularly in sensors has yielded the birth of mHealth. The main objective of this paper is to develop a telepresence application for a mobile platform. The functionality of the system will include the ability for the doctor to connect with a doctor/patient from a remote location using mobile or web application, and the ability to diagnose patient disease remotely and recommend follow-up care. There is a requirement for low price, well suited, and easy-to-use video communication system. The web real-time communication (WebRTC) permits browsers to set up a peer connection to deliver information and media with real-time conferencing capabilities through simple JavaScript APIs.
Application of Double Variable Regression to Determine SNPs that Affect High School / Academic Achievement

The evaluation step in school accreditation process includes eight components of national education standard (SNP). The result of accreditation from the evaluation is believed to explicate the academic achievement of student, in this case is National Examination (UN). Thus, it is necessary to further observe the relation between the accreditation results and the score of national examination. One of the analysis that can be used is regression analysis, it is used to observe the relation between the accreditation result and the sroce of national examination also to identify the SNP components that affect the national examination score. However, since the study was conducted at senior high school level where the national examination score for this level covers six subjects, the analysis used is no longer a simple regression but a multiple variable regression. It is because of the relationship between the score of the national examination that characterizes an academic achievement. The application result of multiple variable regression method shows that there is a relation between SNP and national examination score.
Better Guider Predicts Future Better: Difference Guided Generative Adversarial Networks

Predicting the future is a fantasy but practicality work. It is the key component to intelligent agents, such as self-driving vehicles, medical monitoring devices and robotics. In this work, we consider generating unseen future frames from previous obeservations, which is notoriously hard due to the uncertainty in frame dynamics. While recent works based on generative adversarial networks (GANs) made remarkable progress, there is still an obstacle for making accurate and realistic predictions. In this paper, we propose a novel GAN based on inter-frame difference to circumvent the difficulties. More specifically, our model is a multi-stage generative network, which is named the Difference Guided Generative Adversarial Netwok (DGGAN). The DGGAN learns to explicitly enforce future-frame predictions that is guided by synthetic inter-frame difference. Given a sequence of frames, DGGAN first uses dual paths to generate meta information. One path, called Coarse Frame Generator, predicts the coarse details about future frames, and the other path, called Difference Guide Generator, generates the difference image which include complementary fine details. Then our coarse details will then be refined via guidance of difference image under the support of GANs. With this model and novel architecture, we achieve state-of-the-art performance for future video prediction on UCF-101, KITTI.
Detecting Text in the Wild with Deep Character Embedding Network

Most text detection methods hypothesize texts are horizontal or multi-oriented and thus define quadrangles as the basic detection unit. However, text in the wild is usually perspectively distorted or curved, which can not be easily tackled by existing approaches. In this paper, we propose a deep character embedding network (CENet) which simultaneously predicts the bounding boxes of characters and their embedding vectors, thus making text detection a simple clustering task in the character embedding space. The proposed method does not require strong assumptions of forming a straight line on general text detection, which provides flexibility on arbitrarily curved or perspectively distorted text. For character detection task, a dense prediction subnetwork is designed to obtain the confidence score and bounding boxes of characters. For character embedding task, a subnet is trained with contrastive loss to project detected characters into embedding space. The two tasks share a backbone CNN from which the multi-scale feature maps are extracted. The final text regions can be easily achieved by a thresholding process on character confidence and embedding distance of character pairs. We evaluated our method on ICDAR13, ICDAR15, MSRA-TD500, and Total-Text. The proposed method achieves state-of-the-art or comparable performance on all these datasets, and shows substantial improvement in the irregular-text datasets, i.e. Total-Text.
Two-Sided Estimates for Order Statistics of Log-Concave Random Vectors

We establish two-sided bounds for expectations of order statistics ($k$-th maxima) of moduli of coordinates of centered log-concave random vectors with uncorrelated coordinates. Our bounds are exact up to multiplicative universal constants in the unconditional case for all $k$ and in the isotropic case for $k \leq n-cn^{5/6}$. We also derive two-sided estimates for expectations of sums of $k$ largest moduli of coordinates for some classes of random vectors.
RSS Stance Against The Floodgate of Startling Revelations: Prevarication – South Asia Citizens Web

Prevarication is dodging facing the truth. Prevaricating in matters of national interest where the unity and integrity of the country is involved is even worse treachery. Rashtriya Swayamsevak…
Approximate-Closed-Itemset Mining for Streaming Data Under Resource Constraint

Here, we present a novel algorithm for frequent itemset mining for streaming data (FIM-SD). For the past decade, various FIM-SD methods in one-pass approximation settings have been developed to approximate the frequency of each itemset. These approaches can be categorized into two approximation types: parameter-constrained (PC) mining and resource-constrained (RC) mining. PC methods control the maximum error that can be included in the frequency based on a pre-defined parameter. In contrast, RC methods limit the maximum memory consumption based on resource constraints. However, the existing PC methods can exponentially increase the memory consumption, while the existing RC methods can rapidly increase the maximum error. In this study, we address this problem by introducing the notion of a condensed representation, called a $Δ$-covered set, to the RC approximation. This notion is regarded as an extension of the closedness compression and when $Δ= 0$, the solution corresponds to an ordinary closed itemset. The algorithm searches for such approximate closed itemsets that can restore the frequent itemsets and their frequencies under resource constraint while the maximum error is bounded by an integer, $Δ$. We first propose a one-pass approximation algorithm to find the condensed solution. Then, we improve the basic algorithm by introducing a unified PC-RC approximation approach. Finally, we empirically demonstrate that the proposed algorithm significantly outperforms the state-of-the-art PC and RC methods for FIM-SD.
Introduction to MapReduce

This chapter introduces you to MapReduce programming. You will see how functional abstraction lead to real-life implementation. There are two key technical solutions that enable the use of map and reduce functions in practice for parallel processing of big data. First of all, a distributed file system, like Hadoop Distributed File System (HDFS), which ensures delivery of unique subsets of the whole dataset to each map instance.
Extreme Relative Pose Estimation for RGB-D Scans via Scene Completion

Estimating the relative rigid pose between two RGB-D scans of the same underlying environment is a fundamental problem in computer vision, robotics, and computer graphics. Most existing approaches allow only limited maximum relative pose changes since they require considerable overlap between the input scans. We introduce a novel deep neural network that extends the scope to extreme relative poses, with little or even no overlap between the input scans. The key idea is to infer more complete scene information about the underlying environment and match on the completed scans. In particular, instead of only performing scene completion from each individual scan, our approach alternates between relative pose estimation and scene completion. This allows us to perform scene completion by utilizing information from both input scans at late iterations, resulting in better results for both scene completion and relative pose estimation. Experimental results on benchmark datasets show that our approach leads to considerable improvements over state-of-the-art approaches for relative pose estimation. In particular, our approach provides encouraging relative pose estimates even between non-overlapping scans.
Mini-Workshop: Deep Learning and Inverse Problems

Machine learning and in particular deep learning offer several data-driven methods to amend the typical shortcomings of purely analytical approaches. The mathematical research on these combined models is presently exploding on the experimental side but still lacking on the theoretical point of view. This workshop addresses the challenge of developing a solid mathematical theory for analyzing deep neural networks for inverse problems.
Interest Point Detection based on Adaptive Ternary Coding

In this paper, an adaptive pixel ternary coding mechanism is proposed and a contrast invariant and noise resistant interest point detector is developed on the basis of this mechanism. Every pixel in a local region is adaptively encoded into one of the three statuses: bright, uncertain and dark. The blob significance of the local region is measured by the spatial distribution of the bright and dark pixels. Interest points are extracted from this blob significance measurement. By labeling the statuses of ternary bright, uncertain, and dark, the proposed detector shows more robustness to image noise and quantization errors. Moreover, the adaptive strategy for the ternary cording, which relies on two thresholds that automatically converge to the median of the local region in measurement, enables this coding to be insensitive to the image local contrast. As a result, the proposed detector is invariant to illumination changes. The state-of-the-art results are achieved on the standard datasets, and also in the face recognition application.
On the Interaction Effects Between Prediction and Clustering

Machine learning systems increasingly depend on pipelines of multiple algorithms to provide high quality and well structured predictions. This paper argues interaction effects between clustering and prediction (e.g. classification, regression) algorithms can cause subtle adverse behaviors during cross-validation that may not be initially apparent. In particular, we focus on the problem of estimating the out-of-cluster (OOC) prediction loss given an approximate clustering with probabilistic error rate p_0. Traditional cross-validation techniques exhibit significant empirical bias in this setting, and the few attempts to estimate and correct for these effects are intractable on larger datasets. Further, no previous work has been able to characterize the conditions under which these empirical effects occur, and if they do, what properties they have. We precisely answer these questions by providing theoretical properties which hold in various settings, and prove that expected out-of-cluster loss behavior rapidly decays with even minor clustering errors. Fortunately, we are able to leverage these same properties to construct hypothesis tests and scalable estimators necessary for correcting the problem. Empirical results on benchmark datasets validate our theoretical results and demonstrate how scaling techniques provide solutions to new classes of problems.
Review on Good Documentation practice in pharmaceutical manufacturing unit as per European Union GMP Chapter-4 on Documentation

Good documentation constitutes an essential part of the quality assurance system and is key to operating in compliance with GMP requirements. Documentation requirements of maintaining complete, accurate, truthful and verifiable data in all cGXP documents that are needed to be maintained as per regulatory requirements and various Governmental regulations, laws, rules and statutes/acts. Documentation may exist in a variety of forms, including paper-based, electronic or photographic media. Ensure that the Document should be free from error and during any point if error identify then rectify with proper reason for correcting including sign and date. A system should be in place to indicate special observations and any changes to critical data. GMP Document must have predefined retention period and document must be stored in secure and easy to retrieve or easily available as and when required. Batch Processing and Packaging Instruction must be in place and Contemporaneous entry provision must be available. Instruction and procedure for using equipment; instrument must be clear and specific. Specification with authorization should be available for analysis of Raw Material and Packing Material, Intermediate and Bulk Product, Finished Product.
Curriculum Model Adaptation with Synthetic and Real Data for Semantic Foggy Scene Understanding

This work addresses the problem of semantic scene understanding under fog. Although marked progress has been made in semantic scene understanding, it is mainly concentrated on clear-weather scenes. Extending semantic segmentation methods to adverse weather conditions such as fog is crucial for outdoor applications. In this paper, we propose a novel method, named Curriculum Model Adaptation (CMAda), which gradually adapts a semantic segmentation model from light synthetic fog to dense real fog in multiple steps, using both labeled synthetic foggy data and unlabeled real foggy data. The method is based on the fact that the results of semantic segmentation in moderately adverse conditions (light fog) can be bootstrapped to solve the same problem in highly adverse conditions (dense fog). CMAda is extensible to other adverse conditions and provides a new paradigm for learning with synthetic data and unlabeled real data. In addition, we present three other main stand-alone contributions: 1) a novel method to add synthetic fog to real, clear-weather scenes using semantic input; 2) a new fog density estimator; 3) a novel fog densification method to densify the fog in real foggy scenes without using depth; and 4) the Foggy Zurich dataset comprising 3808 real foggy images, with pixel-level semantic annotations for 40 images under dense fog. Our experiments show that 1) our fog simulation and fog density estimator outperform their state-of-the-art counterparts with respect to the task of semantic foggy scene understanding (SFSU); 2) CMAda improves the performance of state-of-the-art models for SFSU significantly, benefiting both from our synthetic and real foggy data. The datasets and code are available at the project website.
Two-Factor-Based Public Data Protection Scheme in Smart Ocean Management

Nowadays, two-factor data security protection has become a research hotspot in smart ocean management. With the increasing popularity of smart ocean management, how to achieve the two-factor protection of public data resources in smart ocean management is a serious problem to be tackled. Furthermore, how to achieve both security and revocation is also a challenge for two-factor protection. In this paper, we propose a two-factor-based protection scheme with factor revocation in smart ocean management. The proposed scheme allows data owners (DOs) to send encrypted messages to users through a shipboard server (SS). The DOs are required to formulate access policy and perform attribute-based encryption on messages. In order to decrypt, the users need to possess two factors. The first factor is the user's secret key. The second factor is security equipment, which is a sensor card in smart ocean system. The ciphertext can be decrypted if and only if the user gathers the key and the security equipment at the same time. What is more, once the security equipment is lost, the equipment can be revoked and a new one is redistributed to the users. The theoretical analysis and experiment results indeed indicate the security, efficiency, and practicality of our scheme.
Semi-Grundy function, an hereditary approach to Grundy function

Grundy functions have found many applications in a wide variety of games, in solving relevant problems in Game Theory. Many authors have been working on this topic for over many years. Since the existence of a Grundy function on a digraph implies that it must have a kernel, the problem of deciding if a digraph has a Grundy function is NP-complete, and how to calculate one is not clearly answered. In this paper, we introduce the concept: Semi-Grundy function, which arises naturally from the connection between kernel and semi-kernel and the connection between kernel and Grundy function. We explore the relationship of this concept with the Grundy function, proving that for digraphs with a defining hereditary property is sufficient to get a semi-grundy function to obtain a Grundy function. Then we prove sufficient and necessary conditions for some products of digraphs to have a semi-Grundy function. Also, it is shown a relationship between the size of the semi-Grundy function obtained for the Cartesian Product and the size of the semi-Grundy functions of the factors. This size is an upper bound of the chromatic number. We present a family of digraphs with the following property: for each natural number $n\geq 2$, there is a digraph $R_n$ that has two Grundy functions such that the difference between their maximum values is equal to n. Then it is important to have bounds for the Grundy or semi-Grundy functions.