Reviewerly/exHarmony huggingface dataset:
Depth First Search in the Semi-streaming Model

Depth first search (DFS) tree is a fundamental data structure for solving various graph problems. The classical algorithm for building a DFS tree requires O(m+n) time for a given undirected graph G having n vertices and m edges. In the streaming model, an algorithm is allowed several passes (preferably single) over the input graph having a restriction on the size of local space used. Now, a DFS tree of a graph can be trivially computed using a single pass if O(m) space is allowed. In the semi-streaming model allowing O(n) space, it can be computed in O(n) passes over the input stream, where each pass adds one vertex to the DFS tree. However, it remains an open problem to compute a DFS tree using o(n) passes using o(m) space even in any relaxed streaming environment.We present the first semi-streaming algorithms that compute a DFS tree of an undirected graph in o(n) passes using o(m) space. We first describe an extremely simple algorithm that requires at most ceil[n/k] passes to compute a DFS tree using O(nk) space, where k is any positive integer. For example using k=sqrt{n}, we can compute a DFS tree in sqrt{n} passes using O(n sqrt{n}) space. We then improve this algorithm by using more involved techniques to reduce the number of passes to ceil[h/k] under similar space constraints, where h is the height of the computed DFS tree. In particular, this algorithm improves the bounds for the case where the computed DFS tree is shallow (having o(n) height). Moreover, this algorithm is presented in form of a framework that allows the flexibility of using any algorithm to maintain a DFS tree of a stored sparser subgraph as a black box, which may be of an independent interest. Both these algorithms essentially demonstrate the existence of a trade-off between the space and number of passes required for computing a DFS tree. Furthermore, we evaluate these algorithms experimentally which reveals their exceptional performance in practice. For both random and real graphs, they require merely a few passes even when allowed just O(n) space.
Riding Through Walls: A journey of physical computing through Google Street View

Over two years, artist and researcher Dr. Megan Smith cycled through image tile after image tile of the Google Street View platform, physically working her way across 6300 km of highwayredefining what the great Canadian road trip can be.Starting out with a goal to network a stationary bike with the internet, along the way she dropped into an other experience: a space of physical glitches, a jaunt through other people's histories, a glimpse into the lives of the people who drive the Google-Mobiles-their pit-stops, lunches, and road kill.It was a digitally enhanced life experience far beyond what the step counter on a fitbit can offer.It was not an 'Everyware,' but instead an intimate glimpse into the layers of preserved digital visual culture along a focused route that revealed human and algorithmic errors hidden deep within the structure of the amazing archive of Google data.Each blurred statue and virtual dead-end hinted at the infrastructure and labour, which has produced a wealth of information hidden beyond the boundaries of the publicly accessible platform.Perpetually triggered by human force, it was an experience of places beyond reach that were nonetheless felt, seen, and discovered through a compulsive drive to push through the Internet.The journey was filled with surprise and discovery, from the sudden appearance of angelic forms on the screen, to messages of affection scrawled on the rocks of the endless Canadian Shield.These moments of excitement punctuated the endless hours of cycling, which required both physical endurance and mental perseverance.While most forays into Google Street View last a few minutes and involve magical teleportation from one landmark to the next, Smith pedalled for a total of 337 hours.This duration almost exactly matches Google's prediction for the over 6300 km journey, demonstrating the real physicality of her virtual endeavour, and the detailed accuracy of the geo-data within the platform.From coast to coast, Smith was joined physically and virtually by supporters who rode alongside or cheered as she achieved milestone after milestone, getting ever nearer to Canada's east coast.The entire adventure was live cast and documented publicly on social media.An archive of the performance can be found across Twitter, Facebook, Instagram and YouTube along with directions to build a DIY networked bike on Instructables.Through a series of events and workshops the project engaged the community and spawned interest in virtual road tripping and exploration.This paper will discuss the sights and experiences encountered along this journey across Canada, and what those experiences say about digitally experiencing geography and culture through a visual archive: the timelessness of experiences frozen in the database, the limitations of human error within a platform that is managed by algorithms, and what it really means to be physically computing. Physical computing. Google Street View. Maker culture
Family History Discovery through Search at Ancestry

At Ancestry, we apply learning to rank algorithms to a new area to assist our customers in better understanding their family history. The foundation of our service is an extensive and unique collection of billions of historical records that we have digitized and indexed. Currently, our content collection includes 20 billion historical records. The record data consists of birth records, death records, marriage records, adoption records, census records, obituary records, among many others types. It is important for us to return relevant records from diversified record types in order to assist our customers to better understand their family history.
PENGARUH PENERAPAN FRAUD EARLY WARNING SYSTEM (FEWS) TERHADAP AKTIVITAS BISNIS PERUSAHAAN

Proposes of this observation is to know how big the effect of application of fraud early warning system to business activity that happen in acompany. Because of that, fraud needs more attention and how can fraud early warning system prohibit fraud happen in some business activity of one company. This observation did with spread the questioner to employees in a company to know how big the effect of this fraud early warning system. Data analysis is using simple regression method where test the quality of qualitative data using validity test and reliability test, hypothesis test using fit model testing and F tests. Based on the result of validity test and reliability test, get the valid and reliable result. Based on the result of fit model testing, get the result that the implementation of fraud early warning system is not effective in this company. This is show from R² value as 0.011, which means independent variable just available to clarify dependent variable as 1.1%. Meanwhile, from result of simultaneous simple regression testing, get that fraud early warning system is not significant affected its business activity. This is show from p-value as 0.996, which higher than alpha 0.05.
DFRWS IoT Forensic Challenge Report 3

In this chapter, we report on the findings of our group’s analysis of the Digital Forensic Research Workshop (DFRWS) IoT Forensic Challenge (2018–2019).
Analysis of Modified Least Significant Bit Polynomial Function Algorithm For Securing Digital Image

Abstract Nowadays, there are many types of information, among which is the digital image. Some information must be secured, so it has to be made confidential. One of the methods to secure data is by steganography, wherein information is hidden inside another media called cover. Least Significant Bit (LSB) is the smallest bit in the binary array from the value of a byte. The polynomial function is the total of the power product in one or more variables with coefficients. The digital image can be used as a cover in steganography. The pixel selection for containing the message may be determined by the polynomial function so that the polynomial function can be used as the embedding and extraction key. The larger the ratio between the dimensions of the cover image to the image message, the better values of PSNR and MSE obtained. It can be concluded that the combination of the LSB method and polynomial function may serve as a modification to the LSB method in steganography.
PLOTS: Procedure Learning from Observations using subTask Structure

In many cases an intelligent agent may want to learn how to mimic a single observed demonstrated trajectory. In this work we consider how to perform such procedural learning from observation, which could help to enable agents to better use the enormous set of video data on observation sequences. Our approach exploits the properties of this setting to incrementally build an open loop action plan that can yield the desired subsequence, and can be used in both Markov and partially observable Markov domains. In addition, procedures commonly involve repeated extended temporal action subsequences. Our method optimistically explores actions to leverage potential repeated structure in the procedure. In comparing to some state-of-the-art approaches we find that our explicit procedural learning from observation method is about 100 times faster than policy-gradient based approaches that learn a stochastic policy and is faster than model based approaches as well. We also find that performing optimistic action selection yields substantial speed ups when latent dynamical structure is present.
Can We Understand Image Semantics from Conventional Neural Networks?

The Convolutional Neural Networks (CNNs) have been employed successfully for object identification, behavior analysis, letters and digits recognition, etc. The researchers in computer vision committee have studied the capacity of this model in two directions. The first one is improving its performance by increasing layers of the network, using learned features, more data, or more computing (GPUs). The second one is theoretical understanding in architecture design, in optimization and in a generalization of deep networks. One of the first researches in limitation of CNNs in understudying the semantics of images has done by Hosseini et al. in 2017. This result puts the researchers in CNNs community to do more researches to keep up with proper understanding and continued advances in the fields. This paper also forces on analysis the CNNs capability in understanding semantics information in images by recognizing images of the same shape and semantics but opposite the intensity. Experimental results were done on MNIST and GTSRB dataset indicates the limitation of CNNs in understanding the image semantic.
Subjective Annotations for Vision-based Attention Level Estimation

Attention level estimation systems have a high potential in many use cases, such as human-robot interaction, driver modeling and smart home systems, since being able to measure a person's attention level opens the possibility to natural interaction between humans and computers. The topic of estimating a human's visual focus of attention has been actively addressed recently in the field of HCI. However, most of these previous works do not consider attention as a subjective, cognitive attentive state. New research within the field also faces the problem of the lack of annotated datasets regarding attention level in a certain context. The novelty of our work is two-fold: First, we introduce a new annotation framework that tackles the subjective nature of attention level and use it to annotate more than 100,000 images with three attention levels and second, we introduce a novel method to estimate attention levels, relying purely on extracted geometric features from RGB and depth images, and evaluate it with a deep learning fusion framework. The system achieves an overall accuracy of 80.02%. Our framework and attention level annotations are made publicly available.
El aprendizaje significativo como base de las metodologías innovadoras

espanolLa teoria del aprendizaje significativo es todavia una respuesta valida y actual a los problemas del sistema educativo en un contexto en el que a pesar del uso de metodologias innovadoras, la ensenanza sigue estando dirigida a la superacion de distintos test, estimulando asi el aprendizaje mecanico en lugar del aprendizaje significativo. A pesar de ser un constructo utilizado por muchos, parece ser entendido por pocos, ya que se ha producido un apropiamiento superficial del mismo, de forma que hoy todo es aprendizaje significativo y todas las metodologias contribuyen al mismo, aun cuando no se conoce la teoria que lo sustenta. Ante esta situacion, este estudio pretende investigar si los docentes del modulo de y Orientacion (FOL) buscan un aprendizaje significativo en su alumnado, si conocen la teoria que lo sustenta y si utilizan estrategias en el aula que lo propician. Para ello, se ha establecido un marco teorico construido a partir de una revision bibliografica sobre la teoria del aprendizaje significativo en la que se estudia su origen. Para contrastar las hipotesis de partida a la luz del marco teorico se han utilizado cuestionarios dirigidos al profesorado y al alumnado de FOL, que han permitido identificar las estrategias mas utilizadas en el aula de FOL y analizar la percepcion que los estudiantes tienen de este Modulo. Se ha constatado que los docentes buscan el aprendizaje significativo, pero desconocen la teoria que lo sustenta. EnglishThe Ausubel's theory of Meaningful Learning is an effective and up-to-date answer to the current situation of the education system, when despite the numerous innovative methodologies, teaching keeps addressed to rote learning instead meaningful learning. It is an expression used by many but misunderstood by most. There has been an artificial appropriation of this concept so that everything is meaningful learning and every methodology contributes to it, even when the theory under the expression is generally unknown. This is why this study aims to investigate whether the teachers of Formacion y Orientacion Laboral intend a meaningful learning in their classrooms, if they know the theory supporting this concept and if they use strategies that boost it. For this purpose, a theorical framework has been defined starting with the origin of the theory. In order to test the initial hypothesis in the light of theoretical framework, we have used questionnaires addressed to both students and teachers. This survey has allowed us to identify the most common strategies in the classrooms of FOL and to analyze the perception of students about this subject. We have confirmed that teachers do aim meaningful learning though unknown the theory under the concept.
Trade-Book Publicity

The direct response specialist buys space or rents lists, produces brochures, flyers, or coupon ads, uses the mails or media; the advertising manager buys time or space for commercials or paid ads. Publicists, though, have to woo their messengers with words, written or spoken, and hope that these messengers will use their media to pass the words on to potential consumers. The publicist's main tasks were to establish and maintain contact with reviewers, organize publication parties, send out catalog data and review copies, schedule book-and-author luncheon talks, and occasionally arrange an author interview for a newspaper's book page or a guest spot on a local radio or TV program. A publicity wrap-up on the campaign helps the sales and marketing people stay in touch with the total effort, evaluate results, and plan future marketing strategies. Publicity breaks need to be promoted and communicated if they are going to be effective sales producers.
Development of Thematic Learning Materials Based on Scientific Approach on Indahnya Alam Negeriku Subtema in 4th Grade of Public Elementary School 094151 Sipanganbolon

The aims of the study is to find out the development of thematic learning materials based on scientific approach. The data was conducted in the IV grade student of SD Negeri 094151 Sipanganbolon Academic Year 2018/2019, the sample taken from 30 students. The result shows that the feasibility assessment by material experts obtained an average score of 3.27 and 81.94% (Very good), the feasibility assessment by linguists obtained an average score of 3.56 and 89% (Very good), the feasibility assessment by design experts obtained an average score of 3.4 and 85% (Very good).
University and Government Unite to Address Homeland Cybersecurity Issues

There is a direct relationship emerging between cybersecurity skills and quality assurance skills. This article shares the ideas and experiences California Polytechnic State University has created to address high-priority technology and cybersecurity ga..
Summarizing videos into a target language: Methodology, architectures and evaluation

The aim of the work is to report the results of the Chist-Era project AMIS (Access Multilingual Information opinionS). The purpose of AMIS is to answer the following question: How to make the information in a foreign language accessible for everyone? This issue is not limited to translate a source video into a target language video since the objective is to provide only the main idea of an Arabic video in English. This objective necessitates developing research in several areas that are not, all arrived at a maturity state: Video summarization, Speech recognition, Machine translation, Audio summarization and Speech segmentation. In this article we present several possible architectures to achieve our objective, yet we focus on only one of them. The scientific locks are be presented, and we explain how to deal with them. One of the big challenges of this work is to conceive a way to evaluate objectively a system composed of several components knowing that each of them has its limits and can propagate errors through the first component. Also, a subjective evaluation procedure is proposed in which several annotators have been mobilized to test the quality of the achieved summaries.
Implementasi Metode User Centered Design Pada Sistem Pendukung Keputusan Peramalan Penjualan Ikan Hias

Strategies in sales are needed to increase sales of goods and increase company profits. One of them is sales forecasting that predicts future sales, to avoid mistakes in the supply of goods sold. This study aims to help shop owners make decisions in determining the supply of ornamental fish stocks. The implementation of the user-centered design method for decision support systems used to forecast the sale of ornamental fish by processing ornamental fish sales data in the previous period to obtain sales predictions in the next period. The method implemented is a user-centered design method wherein the process of making a decision support system always involves the user, in this case, the shop owner as of the application user. The user-centered design approach helps in designing technology-based information systems that are user-centered and the goal of user-centered design is not only to create systems /applications that are useful for the user. Data collection was used to create a decision support system using literature studies, interviews with business owners and questionnaires. The results of research on decision support systems are forecasting the sale of ornamental fish with a decision support system using two methods, namely the trend moment method to help shop owners determine the fish stock and user-centered design methods to design a decision support system that suits the needs and wishes of the user
Review of Systematic Software Innovation Using TRIZ

This paper attempts to review the use of TRIZ, Theory of Inventive Problem Solving, in the field of software innovation. TRIZ finds widespread applications in many fields of engineering such as mechanical, electrical, electronics, chemical, materials, industrial engineering, etc. Even, TRIZ has its applications in management and strategies. However, the applications of TRIZ in the field of software engineering to solve problems that arise during phases such as software design, development, coding, testing, and maintenance seems to be in its very initial phase. The primary objectives of this paper are to review and consolidate the current state of the art in the area of TRIZ for software related problems by a literature review. The current review will help academicians and industry experts to understand the current state and to visualize a possible future direction.
Internet of Knowledge

Accurate nuclei segmentation, as an indispensable basis and core link for multi-cell cervical image analysis, plays an important role in automatic pre-cancer detection. However, poor image quality due to the uneven staining, complex backgrounds and overlapped cell clusters poses a great challenge in nuclei segmentation. In this paper, we propose a new Selective-Edge-Enhancement-based Nuclei Segmentation method (SEENS). In the proposed method, selective search is integrated with mathematical operators to segment whole slide cervical images into small regions of interest (ROI) while automatically avoiding repeated segmentation as well as eliminating non-nuclei regions. In addition, an edge enhancement method based on the canny operator and mathematical morphology is presented to extract edge information as a weight to enhance the nucleus edge selectively. As a result, the enhanced ROI is then segmented by the Chan–Vese model with a higher accuracy. We evaluate our method with 18 whole slide images for a total of 395 cell nuclei. Experimental results demonstrate that SEENS achieves higher accuracy in cervical nuclei segmentation. Notably our method performs particularly better in low-contrast scenarios than baselines.
Sentiment Analysis of Film Reviews Based on Semantic Rules and Extended Domain Sentiment Dictionary

The accuracy of the film review sentiment analysis is influenced by the complexity and diversity of the film review on social networks. To solve this problem, this paper proposes a film review sentiment analysis method (SRSD) based on the semantic rules of key sentences and extended domain sentiment dictionary. Firstly, by analyzing the structural characteristics of key sentences, the semantic rules of key sentences are proposed, and the algorithm is used to extract key sentences. Then, the new login sentiment words are extended by the semantic similarity of HowNet words in film review field, and relevant dictionaries are constructed such as degree adverb dictionary and negative word dictionary. Finally, through the semantic rules of key sentences and the extended sentiment dictionary, the sentiment values of all key sentences are weighted and summed to classify the sentiment of film review. The experimental results show that the method effectively improves the efficiency and accuracy of the sentiment analysis.
orientation of the driverless car at loss of a satellite signal

The problem of the driverless car orientation at loss of satellite signal is considered. The software for calculation of the Euler angles is created, based on previously detected points (the vanished points). The method is tested on experimental data. The solution of the ill-posed problem is analyzed at a small number of selected segments of lines on the image.