Reviewerly/exHarmony huggingface dataset:
Network Analytics for Streaming Traffic Analysis

Network traffic analytics has always been an important field for cybersecurity and network operations. The analysis could be done on the stream of data (network traffic) in an on-line mode for real-time network operations or on the captured traffic data in an off-line mode for forensic analysis. In this paper, we aim to shed light into using machine learning based network analytics on traffic flows both for network operations and forensic analysis purposes. In doing so, our goal is to design and develop systems that could assist the human expert in analyzing unknown background traffic.
The Index Thomisticus as a Big Data Project

The Digital Humanities (DH), as Rob Kitchin reminds us, have always been interested in the building of infrastructure for research (Kitchin 2014, Loc. 222 of 6164). Imagining how emerging technologies could first be applied to Humanities problems and then scaled up to infrastructure for others to use has been one of the defining features of the field, by which we mean the field has evolved through projects that experimented with the application of new computing technologies to the difficult problems of the Humanities. Such experimentation began with Father Busais Index Thomisticus (IT) project (Busa 1980; Winter 1999; Busa 1974-1980) which is why many genetic descriptions of the field returns to the Index. The Index Thomisticus (IT) project was not only the first, but also one of the largest Digital Humanities projects of all time, even though the outcome might, by today’s standards be considered “small”. The project lasted 34 years and at its peak (1962) involved a staff of as many as 70 persons all housed in a large ex-textile factory in Gallarate. For that time they were dealing with big data, we might even say really big data, and the infrastructure they had to build was unlike any ever built before. If we want to understand what is involved in scaling up to big infrastructure we should look back to the beginnings of the field and the emergence of big projects like the Index. This paper will therefore look at the Busa’s project as a way to think through big projects by first discussing the historiography of the IT project and DH projects in general. We will ask how can we study projects as bearers of ideas? What resources do we need/have? Then we will look at specific aspects of the project that shed light on DH projects in general. In particular we will look at how the project was communicated, conceived, and the data processing innovations. Finally, we will reflect on what lessons the IT project has for us at a time when big data has become an end in itself. What can we learn from Busa’s attention to data in the face of the temptations of automatically gathered data?
An Autoethnography on the Middle-Aged Female Students’ Desire for Growth and Self-Realization

이 연구는 남존여비의 가부장적 사회 관습과 빈곤으로 인해 배움의 기회를 박탈당했던 중년여성의 끊임없는 성장욕구를 통해 전업주부에서 전문 강사로 전환되는 자기실현의 과정을 고찰하는데 목적을 두었다. 이를 위해 질적 연구 중 하나인 자문화기술지 연구 방법으로 연구를 진행하였다. 연구의 목적을 달성하기 위하여 대화, 회상 자료, 자기 관찰 등의 자료를 수집하였다. 연구 결과는 자료를 코딩하고 분석하여 ‘성장의 지연’, ‘성장의 시작’이라는 두 가지 주제를 도출하였다. 이에 따른 연구 결과는 첫째, 중년 여성 만학도가 배움의 기회를 가질 수 없었던 자신의 환경을 수용하고 그로 인한 배움의 가치와 욕구를 명확히 인지할 수 있었다. 둘째, 중년여성 만학도의 성장욕구를 통해 자기실현 과정을 알 수 있었다. 이 연구는 중년여성 만학도의 배움 동기와 성장욕구를 세밀하고 심층적으로 파악하여 자기실현 과정을 고찰한 것에 의의가 있으며 향후 중년여성 만학도 대상으로 상담하는 장면에서 그들이 좌절하지 않고 자신감을 가지고 학업을 지속할 수 있도록 도우며 성장하고자 하는 자기실현을 위한 의미를 제공하고자한다.The purpose of this study is to examine the process of transition of a middle aged student from a housewife to a professional instructor by learning the desire for continuous growth a women who missed opportunities for learning due to patriarchal social customs and poverty. For this purpose, the research adopted one of the qualitative methods, the autoethnographic research methodology and collected data through conversations, recollections, and self-observations to accomplish the purpose. Th rough coding and analyzing of the data, the research results were defi ned in two themes of ‘delayed growth’ and ‘start of growth’. Th e research results are as follow. First, middle-aged female students came to be able to accepting their environemnts in which they could not have a chance to learn and clearly recognize the value of learning and their desire for it. Second, they were able to learn the self-realization process through the desire for growth of a middle-aged woman student. Th is study is meaningful as it looked deep into the process of self realization of a middle-aged women student by identifying motivation of learning and desire for growth and in the counseling, it helped them to continue with their education with confi dence.
Teaching Rationale Management in Agile Project Courses

Rationale management is beneficial since it supports decision-making and prevents knowledge vaporization. To apply rationale management, developers need to know how to systematically capture rationale and how to exploit the documentation. We believe that teaching these skills to students further integrates rationale management into the daily work of developers and has positive effects on both the software development process and on the quality of the software. In this paper, we report on a lecture on teaching rationale management to students. In this lecture, students are introduced to a rationale model, to capture and exploitation methods, and tool support for rationale management. The goal is to motivate them to apply rationale management. We present the students' results as well as their attitude and feedback towards the applied methods. Further, we sketch how rationale management will be applied during the semester.
Optimizing Adaptive Tile-Based Virtual Reality Video Streaming

The increasing popularity of head-mounted displays and 360° video cameras has encouraged content providers to provide virtual reality video streaming over the Internet, using HTTP adaptive streaming to deliver a two-dimensional representation of the immersive content. However, since only a limited part of the video (i.e., the viewport) is watched by the user, the available bandwidth is not optimally used. Recently, adaptive tile-based video streaming has been proposed; rather than sending the whole 360° video at once, the video is cut into temporal segments and spatial tiles. Each tile can be requested at a different quality level, giving priority to content within the viewport. This results in higher video quality and an increased bandwidth utilization. In this paper, we address three open research questions, concerning viewport prediction, tile-based rate adaptation, and application layer optimizations. First, we present a content-agnostic viewport prediction scheme based on spherical walks. Second, we introduce a new rate adaptation heuristic for tile-based video, which prioritizes tiles according to the great-circle distance between the viewport’s and the tile’s center. Third, we investigate the advantages of using HTTP/2 server push. We show that the proposed optimizations result in significant improvements in terms of viewport prediction error and video quality, compared to state-of-the-art solutions.
Sparsity Normalization: Stabilizing the Expected Outputs of Deep Networks.

The learning of deep models, in which a numerous of parameters aresuperimposed, is known to be a fairly sensitive process and should be carefullydone through a combination of several techniques that can help to stabilize it.We introduce an additional challenge that has never been explicitly studied:the heterogeneity of sparsity at the instance level due to missing values orthe innate nature of the input distribution. We confirm experimentally on thewidely used benchmark datasets that this variable sparsity problem makes theoutput statistics of neurons unstable and makes the learning process moredifficult by saturating non-linearities. We also provide the analysis of thisphenomenon, and based on our analysis, we present a simple technique to preventthis issue, referred to as Sparsity Normalization (SN). Finally, we show thatthe performance can be significantly improved with SN on certain popularbenchmark datasets, or that similar performance can be achieved with lowercapacity. Especially focusing on the collaborative filtering problem where thevariable sparsity issue has been completely ignored, we achieve newstate-of-the-art results on Movielens 100k and 1M datasets, by simply applyingSparsity Normalization (SN).
‘You’ll need help from your adult assistant’: Readership accommodation in children’s recipes

Abstract This article investigates how children’s recipes are adapted structurally and linguistically for their young readership. It is based on comparative data, using 24 children’s and 24 adults’ versions of the same recipes, and additional online and printed children’s recipes as reference. Even though recipes have been researched extensively, research on recipes for children in particular is rare. Based on general research on facilitation methods in recipe writing, the article analyzes how children, as the target readership, are accommodated in the analyzed recipes and how they differ from recipes intended for adults. Findings suggest that children’s recipes often have an appealing title, are more detailed and clearer, and use visual support. There is a tendency to reduce presuppositions and include additional advice. However, they seem less consistent than adult recipes and, in contrast to the general trend for accommodation, often use technical terminology without further explanations. The request for an adult helper distinguishes children’s from adults’ recipes and serves as a differentiating feature. These findings indicate that children’s recipes are adapted to the particular context and needs of a young and inexperienced audience.
Fast and virtually exact quantum gate generation in <i>U</i>(<i>n</i>) via iterative Lyapunov methods

A new approach called Reference Input Generation Algorithm (RIGA) is introduced for quantum gate generation. Let X¯ℓ−1(t) be the trajectory that is obtained in step ℓ−1, with X¯ℓ−1(t)=I. In step ℓ, X¯ℓ−1 is right-translated in order to displace X¯ℓ−1(Tf) to Xgoal. The translated trajectory is used as a reference for a Lyapunov-based tracking control law, generating X¯ℓ(T), and so on. A proof of the exponential convergence of X¯ℓ−1(Tf) to zero is provided for Tf large enough. Two examples present numerical experiments regarding N coupled qubits. The first example considers N = 3 with a known minimum time T∗. It presents excellent results for Tf=T∗. The second example is a benchmark for the comparison between RIGA and GRAPE, considering a Hadamard gate for the systems with N = 2, 3, …, 10 qubits. The runtime of RIGA could be improved, and GRAPE was implemented in a faster CPU. However, RIGA presents results that are similar to GRAPE, with faster runtime in some cases, showing that the RIGA is indeed a promising algorithm.
Automatic Determination Number of Cluster for Multi Kernel NMKFCM Algorithm on Image Segmentation

In image analysis, image segmentation performed an essential role to get detail information about image. Image segmentation is suitable in many applications like medicinal, face recognition, pattern recognition, machine vision, computer vision, video surveillance, crop infection detection and geographical entity detection in map. FCM is famous method used in fuzzy clustering to improve result of image segmentation. FCM doesn’t work properly in noisy and nonlinear separable image, to overcome this drawback, Multi kernel function is used to convert nonlinear separable data into linear separable data and high dimensional data and then apply FCM on this data. NMKFCM method incorporates neighborhood pixel information into objective function and improves result of image segmentation. New proposed method used RBF kernel function into objective function. RBF function is used for similarity measure. New proposed algorithm is effective and efficient than other fuzzy clustering algorithms and it has better performance in noisy and noiseless images. In noisy image, find automatically required number of cluster with the help of Hill-climbing algorithm.
Continual learning improves Internet video streaming

We describe the results of a randomized controlled trial of video-streaming algorithms for bitrate selection and network prediction. Over the last eight months, we have streamed 14.2 years of video to 56,000 users across the Internet. Sessions are randomized in blinded fashion among algorithms, and client telemetry is recorded for analysis. We found that in this real-world setting, it is difficult for sophisticated or machine-learned control schemes to outperform a simple scheme (buffer-based control), notwithstanding good performance in network emulators or simulators. We performed a statistical analysis and found that the variability and heavy-tailed nature of network and algorithm behavior create hurdles for robust learned algorithms in this area. We developed an ABR algorithm that robustly outperforms other schemes in practice, by combining classical control with a learned network predictor, trained with supervised learning in situ on data from the real deployment environment. To support further investigation, we are publishing an archive of traces and results each day, and will open our ongoing study to the community. We welcome other researchers to use this platform to develop and validate new algorithms for bitrate selection, network prediction, and congestion control.
Pendampingan Pembelajaran Bahasa Inggris Dasar Pada Eks-TKI Di Dusun Sumberwaru Desa Tamanagung Kecamatan Cluring Kabupaten Banyuwangi

English is an international language that is used as the language of world communication. Indonesian migrant workers and eks-migrant workers need the ability to communicate in English because it can affect their level of success while working in other countries. This community service activity aims to provide basic English language learning assistance to eks-TKI in sub village of Sumberwaru village, Tamanagung sub-district, Cluring, Bbanyuwangi district which is 30 kilometers from the center of the district administration. The training was attended by 12 people with four face-to-face meetings each with a duration of 2 hours. Face-to-face meetings are conducted with lecture and demonstration methods, followed by practice / practice. The subject presented was divided into three discussion materials, namely Part of speech, Tenses, and Sentences. The obstacles faced in the activity were relatively short training times and varied material capture of the participants. In general, this activity went well and received enthusiastic welcome from the participants who participated in the training completely.
Dynamics of corruption and cronyism in India

This chapter discusses the impact of corruption and cronyism on societies around the world, and presents the crucial differences between the related concepts of corruption and cronyism. It explores how corruption and cronyism have affected business and society in India and how the economic liberalization and its resultant growth can help reduce corruption and cronyism. The fundamental distinction between cronyism and corruption is that cronyism is based on a tie or a connection or relationship between actors and involves implicit, unspecified, and reciprocal transactions with no stipulation of a time period during which favours must be returned. Influence market corruption deals in access to decision-makers and policy implementers within strong state institutions; often politicians serve as middlemen, trading connections for contributions both legal and otherwise. The administrative systems and social, economic, and political institutions that exist in India show the deep imprint of the Congress Party’s philosophy and policies.
How to quantify a dynamical resource

We show that the generalization of the relative entropy of a resource from states to channels is not unique, and there are at least six such generalizations. We then show that two of these generalizations are asymptotically continuous, satisfy a version of the asymptotic equipartition property, and their regularizations appear in the power exponent of channel versions of the quantum Stein's Lemma. To obtain our results, we use a new type of that can be applied to functions of channels (with no state analog). We call it liberal smoothing as it allows for more spread in the optimization. Along the way, we show that the diamond norm can be expressed as a D_max distance to the set of quantum channels, and prove a variety of properties of all six generalizations of the relative entropy of a resource.
ORGANIZATION OF STUDENT-CENTERED TRAINING IN THE SYSTEM OF ADDITIONAL EDUCATION OF ADULTS

The article presents one of the modern trends in the development of the education system - the organization of student-centered training. Extrapolation of this type of training into the system of additional education of adults will allow to prepare a specialist with modern thinking, focused on innovation, continuous self-development. The purpose of the research is to explore the possibilities of implementing student-centered training tools into the practice of realization retraining programs for educational specialists. Research methods: a theoretical analysis of the essence of the concept of “student-centered training”, analysis of the characteristics of adult education, experimental work on testing methods of student-centered training in the system of additional education of adults. The main results of the research: methods of student-centered training of adults have been adapted to the specifics of organizing retraining programs, conditions for organizing student-centered training in institutions of additional education of adults have been identified, methodical recommendations have been developed. The use of these recommendations will optimize the implementation of retraining programs for education specialists.
Methods of Moving Target Detection and Behavior Recognition in Intelligent Vision Monitoring

With the rapid development of society, people have higher and higher requirements for the monitoring system and expect personalized and intelligent services. Although the unmanned monitoring regions such as Banks, crossroads, parking lot, has been widely installed surveillance cameras, monitoring to the image is not clear, however, especially in the case of multiple moving targets at the same time appears failure to recognize the detail of a moving target, and often need to monitor the researchers obtained data monitor screen or video for video information, which makes the corresponding judgment, it is to monitor the real-time and judge the accuracy of the great difficulties. Intelligent visual surveillance technology differs from the main characteristics of the previous monitoring technology, is using the method of active vision, in almost without human intervention, by automatically adjusting the rotation of the camera haeundae to locate the region of interest and choice, reach the purpose of tracking and identification of moving targets, and on the basis of further analysis, judgment target behavior, do it can not only in the event of unusual circumstances timely alarm and complete daily management work. Intelligent monitoring system can monitor the moving target and its local details in real time. It is a new generation of highly intelligent monitoring technology. In this paper, from the perspective of theory and practical application, some new explorations are made on the motion behavior recognition with video as input, and effective solutions are sought on the basis of the research on a variety of common tracking and matching algorithms, so as to realize the motion target tracking and matching algorithm with high precision, high robustness and high real-time. In this paper, people or vehicles are taken as the detection objects. Under the condition of fixed cameras, moving targets in the complex background of visual monitoring system are detected and tracked, and moving targets are classified to achieve the purpose of intelligence.
MM-GM-PHD Filter-Based Maneuvering Target Tracking in the presence of Doppler Blind Zone

The existence of the Doppler Blind Zone (DBZ) is one of the main challenges in the field of Doppler radar Multi-target Tracking (MTT). The situation would become more complicated when maneuvering target falls into Doppler Blind Zone. Aiming at this problem, the paper proposes a multiple maneuvering target tracking filter based on multi-model Gaussian mixture probability hypothesis density (MM-GM-PHD) in the presence of DBZ. Since DBZ only changes the detection probability of target, it is unrelated with the prediction step of MM-PHD. Therefore, we first substitute the detection probability model with minimum detectable speed (MDV) into the MM-PHD update step formula, then use the Doppler information to sequentially update intensity, and give its Gaussian mixture (GM) form implementation. Numerical analysis shows that the proposed filter can effectively track the maneuvering target in the presence of DBZ.
Survey for Interlinking of DNA Models with Aadhaar Real-Time Records for Enhanced Authentication

Aadhaar is a unique identification issued by the Indian government to every resident of India. The Unique Identification Authority of India (UIDAI), which functions under the planning commission of India, is responsible for managing Aadhaar identification cards. The Aadhaar project was initiated as an attempt to have a unique identification document which would capture all the details including the demographic and biometric (fingerprints and iris scan) information. In some cases Biometric verification can even be faked remotely, with no product or equipment hack. Fingerprints can be duplicated from an assortment of surfaces and used to make a fake finger. Also, iris picture could be skimmed from photos and supplanted on a simulated eye-like protest. DNA (deoxyribonucleic acid), is the hereditary material and is unique in every person. As the DNA can’t be duplicated by any morphing techniques it will be more useful if we interlink the DNA samples to Aadhaar cards as an identification criteria.
On Quasi-Infinitely Divisible Random Measures

Quasi-infinitely divisible (QID) distributions have been recently introduced. A random variable X is QID if and only if there exist two infinitely divisible random variables Y and Z s.t. X+Y=dZ and Y is independent of X. In this work, we introduce QID completely random measures (CRMs) and show that a certain family of QID CRMs is dense in the space of all CRMs with respect to convergence in distribution. We further demonstrate that the elements of this family possess a Lévy-Khintchine formulation and that there exists a one to one correspondence between their law and certain characteristic pairs. We prove the same results also for the class of point processes with independent increments. In the second part of the paper, we show the relevance of these results in a general Bayesian nonparametric framework suitable for topic modelling, and provide a truncation error analysis.