Reviewerly/exHarmony huggingface dataset:
An Algorithmic Framework for Geo-Distributed Analytics

Large-scale cloud enterprisesKandula, Srikanth operate tens to hundreds of datacenters, running a variety of services that produce enormous amounts of data, suchMenache, Ishai as search clicks and infrastructure operation logs. A recent research direction in both academia and industry is to attempt to process the “big data” in multiple datacenters, as the alternativeNaor, Joseph (Seffi) of centralized processing might be too slow and costly (e.g., due to transferring all the data to a single location). Running such geo-distributed analytics jobs at scale gives riseTimnat, Erez to key resource management decisions: Where should each of the computations take place? Accordingly, which data should be moved to which location, and when? Which network paths should be used for moving the data, etc. These decisions are complicated not only because they involve the scheduling of multiple types of resources (e.g., compute and network), but also due to the complicated internal data flow of the jobs—typically structured as a DAG of tens of stages, each of which with up to thousands of tasks. Recent work [17, 22, 25] has dealt with the resource management problem by abstracting away certain aspects of the problem, such as the physical network connecting the datacenters, the DAG structure of the jobs, and/or the compute capacity constraints at the (possibly heterogeneous) datacenters. In this paper, we provide the first analytical model that includes all aspects of the problem, with the objective of minimizing the makespan of multiple geo-distributed jobs. We provide exact and approximate algorithms for certain practical scenarios and suggest principled heuristics for other scenarios of interest.
The Use of Background Subtraction Method in Model Design of Traffic Light Timer Control System

Abstract Traffic jam caused many problems, one of which, causing time wasting and increasing fuel consumption of user’s vehicle. One of the solutions offered to solve traffic jam is a control system named traffic light timer. It is a traffic management system in each spot of the road done by minimizing the waiting time of the rider during the jam. This research built traffic light timer controller model based on image processing by using Background Subtraction method. The object captured on the road will be processed using Background Subtraction method, the results then show the situation of the road as the parameters of quiet, medium, and heavy roads. The images are then forwarded to the traffic light timer control system to be processed using the hardware and software used in the research. The results show that the Traffic light timer control system is able to classify the heaviness of vehicles queue, to control the timer, and the lights of the traffic light automatically based on the queue of the vehicles with time processing around 2 seconds.
Assessing Accuracy of Ensemble Learning for Facial Expression Recognition with CNNs

Automatic facial expression recognition has recently attracted the interest of researchers in the field of computer vision and deep learning. Convolutional Neural Networks (CNNs) have proved to be an effective solution for feature extraction and classification of emotions from facial images. Further, ensembles of CNNs are typically adopted to boost classification performance. In this paper, we investigate two straightforward strategies adopted to generate error-independent base classifiers in an ensemble: the first strategy varies the seed of the pseudo-random number generator for determining the random components of the networks; the second one combines the seed variation with different transformations of the input images. The comparison between the strategies is performed under two different scenarios, namely, training from scratch an ad-hoc architecture and fine-tuning a state-of-the-art model. As expected, the second strategy, which adopts a higher level of variability, yields to a more effective ensemble for both the scenarios. Furthermore, training from scratch an ad-hoc architecture allows achieving on average a higher classification accuracy than fine-tuning a very deep pretrained model. Finally, we observe that, in our experimental setup, the increase of the ensemble size does not guarantee an accuracy gain.
Prevention of Motion Sickness

There are many and various ways that we can prevent, or at least reduce, the likelihood and severity of motion sickness. We can start by avoiding exposure to motion profiles that have been shown to be particularly provocative and in some cases we can control the duration of exposure. We can also do our best to distract those who are inexperienced travelers so that they are less likely to dwell on the idea that they might become motion sick. As I have heard local sport fishermen say, “If the fish are biting, I don’t get seasick!” Well-controlled increased exposure to provocative motion together with supportive cognitive-behavioural training can go a long way to helping people get their “sea legs” and adapt to other forms of provocative motion on land, sea and in the air; I have been very successful in training people to overcome their motion sickness, whatever the cause.
Communication Media and Digital Literacy as Intervention Tools in Prisons

In Spain, there are a few projects that link communication with social intervention in prisons. These projects have remained invisible to society, and similar initiatives taking place in other prisons were even unknown amongst themselves. It is not common to find radio, programs of digital literacy, magazines, or even television and cinema in prisons. These are activities that remain within the walls and come about from the restlessness of the educators who voluntarily produce them. The work presented comes about the doctoral research work that for 4 years explored these initiatives. Through qualitative methodology, based on the use of in-depth interviews, the authors give voice to 20 prisoners and educators that participate in these projects in Spanish jails. Without claiming to be representative, the results show that these activities can become a powerful tool for social-educative integration and personal transformation.
Workshop on Interactive and Adaptive Learning in an Open World

Next generation machine learning requires stepping away from classical batch learning towards interactive and adaptive learning. This is essential to cope with demanding machine learning applications we have already today. Our workshop at ECCV 2018 in Munich therefore served as a discussion forum for experts in this field and in the following we give a brief overview. Please note that this discussion paper has not been not peer-reviewed and only contains the subjective summary of the workshop organizers.
X-GAN: Improving Generative Adversarial Networks with ConveX Combinations

Recent neural architectures for image generation are capable of producing photo-realistic results but the distributions of real and faked images still differ. While the lack of a structured latent representation for GANs results in mode collapse, VAEs enforce a prior to the latent space that leads to an unnatural representation of the underlying real distribution. We introduce a method that preserves the natural structure of the latent manifold. By utilizing neighboring relations within the set of discrete real samples, we reproduce the full continuous latent manifold. We propose a novel image generation network X-GAN that creates latent input vectors from random convex combinations of adjacent real samples. This way we ensure a structured and natural latent space by not requiring prior assumptions. In our experiments, we show that our model outperforms recent approaches in terms of the missing mode problem while maintaining a high image quality.
A Prediction Method of Solar Power Generator using Machine Learning Techniques

We first purchased a solar power generator, installed it in the right place, and then collected power generation data for the next 12 months. First, we collected weather and power generation data to excel file. For the weather data, the data provided by meteorological agency was secured to ensure the reliability of the information. The collected data can be monitored anytime and anywhere using PC or mobile device. The user interface of these services is planned to be upgraded to be more usable after the accuracy of the service provided. The core development contents are collected data based power generation prediction technique. This is because system failure monitoring can be performed only when a power generation prediction system is configured. Our power generation prediction algorithm was developed based on machine learning. LSTM was used among linear regression and artificial neural network in machine learning. Experiments using the machine learning showed that the accuracy could be increased to 85% when compared with actual power generation.
Smooth Ride: Low-Pass Filtering of Manual Segmentations Improves Consensus

In this paper, we investigate slice-wise manual segmentation of knee anatomy. Due to high inter-rater variability between annotators, often a high number of raters is required to obtain a reliable ground truth consensus. We conducted an extensive study in which cartilage surface was segmented manually by six annotators on three scans of the knee. The slice-wise annotation results in high-frequency artifact that can be reduced by averaging over the segmentations of the annotators. A similar effect can also be obtained by smoothing the surface using low-pass filtering. In our results, we demonstrate that such filtering increases the consistency of the annotation of all raters. Furthermore, due to the smoothness of the cartilage surface, strong filtering produces surfaces that show differences to the ground truth that are in the same order of magnitude as the inter-rater variation. The remaining root mean squared error lies in the range of 0:11 to 0:14 mm. These findings show that appropriate pre-processing techniques result in segmentations close to the consensus of multiple raters, suggesting that in the future fewer annotators are required to achieve a reliable segmentation.
Computational and Corpus Approaches to Chinese Language Learning: An Introduction

In this introductory chapter, we first provide a discussion of the rationale and objectives of the book. We then offer a brief review of the body of corpus linguistics research that intersects with Chinese language pedagogy and acquisition. This is followed by an overview of the state of the art of research in computational linguistics and natural language processing that pertains to Chinese language teaching, learning, and assessment. We conclude with a description of the organization of the book.
Fuzzy Inference System Through Triangular and Hendecagonal Fuzzy Number

A fuzzy inference system works on the basis of fuzzy if-then rules to mimic human intelligence for quantifying the vagueness/uncertainty, which arises in many real-world problems. In this paper, fuzzy inference system is designed using triangular and hendecagonal fuzzy number that represent the value for the linguistic environment. The factors of T2DM mellitus play a critical role in affecting each and every individual health without their knowledge. In this paper, the factor of “Blood Glucose”, medical term known as hyperglycemia, is analyzed through this fuzzy inference system (FIS).
Towards a Two Factor Authentication Method Using Zero-Knowledge Protocol in Online Banking Services

The main objective of our work is to explore the applicability of cryptographic authentication techniques in two factor/step authentication techniques for online banking systems. In particular, we are investigating zero-knowledge protocol as the second step authentication in the aforementioned systems. Many of the existing two-factor authentication schemes involves the third party in their authentication scheme and/or send user information such as passwords over the network. We have proposed a model which utilizes zero-knowledge proof for second step authentication. The proposed system does not involve the third party or require user passwords to be sent over the network. We also have analyzed and discussed some of the security aspects such as key logging, shoulder surfing and eavesdropping which existing one-factor username password-based systems are not immune to.
A Software Framework for Adaptive Signal Analytics based on Autonomic Service Components

The design of platforms for analyzing signals/data requires encoding domain knowledge, defining programming language abstractions and creating an adaptive runtime environment which interfaces with the environment, to enable meaningful inferences. The platform needs to be data driven and context aware. An appropriate computational tool in the platform should enable an automated selection of algorithms and data for making the right inferences with a specified performance. Towards designing a software platform for analyzing data, deriving data driven models, selection of algorithms and enabling feedback mechanisms for adaptation, we propose a service oriented software architecture based on autonomic service components. This approach enables goal relevant decision making and data driven adaptation. The use of Service Component Ensemble Language (SCEL) is highlighted for signal pre-processing based on data driven models. A service broker architecture is proposed to aid in composing services to enable enable algorithm selection and execution for signal preprocessing on biomedical signals for clinical inferences
Lesson concert "Yakov Dubravin to children: "Good afternoon!"

Methodical development of an action was created in connection with small amount of information in social nets about works of the composer Ya.I. Dubravin - the honored worker of arts of Russia, professor of St. Petersburg State University of culture and arts. Its primary activity is works for chorus. In this lesson concert of a song are presented by soloists-vocalists-children under convenient piano accompaniment, and under soundtracks. Song material is various: from the songs devoted to the Great Patriotic War to the works written in jazz style. It is a lot of game in which it is possible to work over acting skills of children. Everything speaks about complexity of the identity of Ya.I. Dubravin. All lesson concert is impregnated with feeling of friendship and a unification, love for the country and to life. Pupils of a class of solo singing act as leaders who narrate about the history of writing of songs, and illustrators of vocal material. In an action video selection from Archive of
Making Your Personas Live

Your persona project is well underway, and the descriptions are used for: ideation, supporting dialogue, recruiting users for product tests, and communicating with the users etc. You now have to consider how to, in the long run, get the method to maintain focus, what should happen if you obtain new knowledge about the users, when to update information, and how to communicate the personas to new employees.
MappGuru, A Universal Addressing System for the Unstructured Areas

An address system is an essential infrastructure which paves the way for social and economic development. It allows people to connect, improves emergency response, increases access to utilities and facilitates postal services and the delivery of goods. On the other hand a digital address infrastructure is undeniably essential for many other processes such as opening bank accounts, the creation of national ID Cards, voting and representing sets of information in data collection and analysis, as well as in marketing and in population census. Designing an addressing system suitable for all countries is challenging. This paper presents a solution by providing a comprehensive universal addressing system—the MappGuru—designed to function as a standardized addressing system with the ability to provide postcode and addresses to any properties. The MappGuru addressing system suggests a unique process to assign addresses based on an existing postcode. The MappGuru addresses are easy for human to understand and can inherently be integrated with information technology systems. To illustrate the MappGuru addressing system, a dedicated section consisting of a case study to assign addresses to an unstructured area in Kigali/Rwanda is described.
How Bad is the Freedom to Flood-It?

Fixed-Flood-It and Free-Flood-It are combinatorial problems on graphs that generalize a very popular puzzle called Flood-It.Both problems consist of recoloring moves whose goal is to produce a monochromatic ("flooded") graph as quickly as possible.Their difference is that in Free-Flood-It the player has the additional freedom of choosing the vertex to play in each move.In this paper, we investigate how this freedom affects the complexity of the problem.It turns out that the freedom is bad in some sense.We show that some cases trivially solvable for Fixed-Flood-It become intractable for Free-Flood-It.We also show that some tractable cases for Fixed-Flood-It are still tractable for Free-Flood-It but need considerably more involved arguments.We finally present some combinatorial properties connecting or separating the two problems.In particular, we show that the length of an optimal solution for Fixed-Flood-It is always at most twice that of Free-Flood-It, and this is tight.
String Matching in Case of Periodicity in the Pattern

The string matching problem is an extensively studied computational problem having applications in many areas such as text editors, compilers, data compression, plagiarism detection, pattern recognition, etc. Dozens of algorithms have been designed to solve it. The finite automata method, Knuth-Morris-Pratt and Boyer-Moore algorithms are the ones of particular importance among them. The string matching in these algorithms is preceded by pattern preprocessing during which some structure supporting the string matching process is created, such as the finite automaton in the finite automata method and the table of prefix function in the Knuth-Morris-Pratt algorithm. The periodicity in pattern implies regularity in the supporting structure, which one can use to reduce the preprocessing time. In this paper, the problem of making the preprocessing phase more efficient in case of a periodic pattern is investigated for the finite automata method and the Knuth-Morris-Pratt algorithm. It is proved that the construction of supporting structure for the entire pattern in these cases can be reduced to its construction merely for the period of that pattern without affecting the processing of the text.
Designing Interactive Public Displays as a New Form of Social Intervention in Nursing Homes

Positive peer relationship in nursing homes is an important component of residents’ life quality. However, social interaction between residents is found to be limited, and their relationships are difficult to establish. In past decades, conventional social interventions have changed very little and had limitations. Although many studies have proved that interactive public displays (IPD) can socially connect people in one community, designing for nursing homes is challenging due to the lack of supported knowledge. This report outlines the ongoing research that investigates how to design appropriate IPD to enhance residents’ social interaction and wellbeing. Research-through-Design is adopted to generate knowledge through an iterative process of developing experimental systems in a real-life setting. The preliminary findings confirmed our hypothesis that IPD could positively influence residents’ social behaviour and feelings. The key is not to push the complexity of technology, but to comply with their current habits, preferences and reduce related barriers.
Variation of Stability Factor of MSERs for Text Detection and Localization in Natural Scene Image Using Naive Bayes Classifier

The process of extracting textual regions from the scene images is a significant matter in the field of image processing & computer vision. It is very challenging due to different fonts, variable font size, illumination conditions and complex background etc. In last decade, image segmentation using Maximal Stable Extremal Regions (MSERs) played an important role in this area due to its various advantages. The generation of MSERs is controlled by variation of stability factor delta in deciding the promising stable areas. The aim of this paper is to study the effect of parameter delta and calculate the optimal delta on the different versions of MSER for detection and localization of text in scene images. Four different features Stroke Width Heterogeneity, Perpetual Color Contrast, Histogram of Oriented Gradients at Edges, Occupy Rate are used to evaluate the probability of text using naive Bayes Model for each version of MSERs. The Training is accomplished on the ICDAR 2013 training dataset and experiments for testing our method are carried out on ICDAR datasets to show the importance of delta (optimal value) parameter of MSER in providing the optimum results expressed as f-measure, recall and precision.
A Method to Bound the Number of Active S-Boxes for a Kind of AES-Like Structure

Abstract Due to the strong security and high performance of the AES block cipher, many hash functions take AES-like structures as building blocks. To evaluate the security of these AES-like structures against differential cryptanalysis, giving the lower bounds on the number of active S-boxes in a differential trail, is an important perspective. However, the original ‘wide-trail strategy’ for AES becomes less effective to get tight bounds for these AES-like structures, because of the different state dimensions (M×M2, instead of M×M) and different round functions from AES. In this paper, we focus on a kind of AES-like structure with state dimensions M×M2, diffusion-optimal permutations and MixColumns transformations using MDS matrices. Inspired by the ‘wide-trail strategy’, we propose a theoretical method to count active S-boxes, by which we prove that there are at least rBd(Bd−1) active S-boxes in any 2r(r≥3) rounds of such an AES-like structure, where Bd is the differential branch number of the MixColumns transformation and equals to M+1. What’s more, this lower bound can be achieved by some diffusion layers. As examples, we apply our method to the LANE hash function and 3D block cipher, optimal lower bounds are both got.
Understanding Challenges to Adoption of the Protection Poker Software Security Game

Currently, security requirements are often neglected in agile projects. Despite many approaches to agile security requirements engineering in literature, there is little empirical research available on why there is limited adoption of these techniques. In this paper we describe a case study on challenges facing adoption of the Protection Poker game; a collaborative and lightweight software security risk estimation technique that is particularly suited for agile teams. Results show that Protection Poker has the potential to be adopted by agile teams. Key benefits identified include good discussions on security and the development project, increased knowledge and awareness of security, and contributions to security requirements. Challenges include managing discussions and the time it takes to play, ensuring confidence in the results from playing the game, and integrating results in a way that improves security of the end-product.