Reviewerly/exHarmony huggingface dataset:
Circular-aperture Modified Hartmann Mask for quadriwave lateral shearing interferometry

A circular-aperture Modified Hartmann Mask (cMHM) for quadriwave lateral shearing interferometry is proposed. The cMHM features in the hybrid structure consisting of a phase chessboard and a circular-aperture array, which is easy to manufacture. The diffraction properties of cMHM are numerically analyzed. By choosing the radius of the circular aperture to be the first positive root of Bessel function, the intensities of parasitic diffraction orders are largely diminished. In the simulations, the parasitic errors are evaluated from the perspectives of both extraction error and reconstruction error. The parasitic error of cMHM is about only half of the parasitic error of MHM. In the experiments, a compact cMHM-based lateral shearing interferometer has been built. Wavefront measurement results obtained by cMHM-LSI are compared with those obtained by commercial wavefront sensor, and the results correspond with each other well. The adaptability and accuracy of the proposed cMHM are validated.
From click to playback

Responding fluently to user requests is important to keep them immersed. In this paper, we are presenting an extensive dataset to study the response time of YouTube's mobile video streaming service on Android. We illustrate the application of our dataset by studying YouTube's initial delay for a subset of 9 videos in 75 network scenarios. We find that in 41% of the cases, YouTube exceeds the attention span of a typical user, while deep immersion is only reached in 15% of the cases. Our factor analysis implies that the allocation of the initial CDN node is the critical link in this delay chain. Since our dataset includes a large variety of factors, we are describing setup, methodology, and data structure in detail. Our dataset and measurement tools are publicly available at [8].
Combination of modified U‐Net and domain adaptation for road detection

Road detection is one of the crucial tasks for scene understanding in autonomous driving. Recently, methods based on deep learning had rapidly grown and addressed this task excellently, because they can extract more abundant features. In this study, the authors consider the visual road detection problem as a classification for each pixel of the given image, which is road or non-road. There is complex illumination encounter in traffic applications, so that the detection model has poor adaptability. They address this problem by proposing a deep network architecture, which combines the network U-Net-prior and domain adaptation model (DAM). U-Net-prior is a modified segmentation network which integrates location prior and shape prior into U-Net. DAM is a model for reducing the gap between training images and test images, which is optimised in adversarial learning to make the features extracted from different datasets close to each other. They validate the effectiveness of each component of the algorithm, and compare the overall architecture with other state-of-the-art methods, and the results show that the architecture achieves top accuracies with the shortest run time in monocular-vision-based methods, simultaneously, compared with the methods based on other sensors, the architecture also achieves a competitive result.
On the Design of Biorthogonal Halfband Filterbanks With Almost Tight Rational Coefficients

Wavelet filters with rational coefficients are efficiently implemented by using simple adders and register shifts in digital hardware. In this brief, a new halfband polynomial with rational coefficients (RC-HBP) is proposed to design biorthogonal filterbanks. The Bernoulli polynomial is used to construct RC-HBP. This new class of RC-HBP is used in modified L- step lifting scheme to construct the biorthogonal filters with rational coefficients. The predict and update steps of lifting are designed from RC-HBP. The filters are constructed using three and four lifting steps. The properties of the proposed filters are measured and the result shows that the proposed wavelet filters give better symmetry, regularity and almost unity frame bound ratio, i.e., almost tight as compared to the existing methods. The proposed design method is illustrated with different examples.
A Museum Visitors Classification Based On Behavioral and Demographic Features

This paper describes an exploratory study that attempts to classify museum visitors by taking into consideration indoor behavior and demographic features. We discuss different approaches of using such data for improving the user experience in the museum. Moreover, we try to explain user's behavior by creating different user groups using a novel data set. Our findings indicate that knowing user age, education and her museum visits frequency, together with the current visit signals (total standing time and listening to a mobile guide time) can be used for visitors classification that might be useful in designing new intelligent user interfaces that can improve the visitor's indoor experience.
Flowing Bodies

In this paper we investigate human interactions with urban media installations by adopting two scales of analysis: the body scale (micro) and the city scale (macro). This twofold approach allows us to better understand the relationships between the design properties of outdoor installations and the urban spatial layout around them. We conducted in-the-wild studies of two urban media installations, one consisting of fixed components, and the other of movable components, which were deployed in different places and encouraged different types of whole-body interaction. We provide a detailed account of the micro and macro levels of interactions, based on observational and qualitative explorations. Our studies reveal that the urban spatial layout is a key element in defining the interactions and encounters around outdoor interfaces, and therefore it needs to inform the design process from the outset.
Using Robust Data Governance to Mitigate the Impact of Cybercrime

The Deloitte's report (2015) on cybersecurity trends states that emerging technologies, coupled with a shifting threat profile, are challenging organizations to deal more and more with sophisticated "bad actors" that are motivated, skilled, and adaptable [1]. There is little doubt that cybercrime is growing more rapidly than cybersecurity measures are able to deal with and businesses and governments have never been more at risk from cyberattacks. In this paper, we ask the question, 'what are some of the robust data governance practices that can be put in place to forestall cybercrime?' We also asked the question, 'are there some research papers that have been published to address this concern? We then took a deep into extant literature to find out. Furthermore, we proposed a cybercrime mitigation framework using robust data governance. Our literature synthesis revealed that there is little research that investigated the concept of data governance and cybercrime together, which leads us to propose research hypotheses for a future quantitative research.
Double speech act: Negotiating inter-cultural beliefs and intra-cultural hate speech

This study investigates attitudes towards discriminatory speech among a sample of Greek Cypriot youth and aims to identify motivators/factors that might produce hateful attitudes. Questionnaires and face-to-face interviews are explored with corpus linguistics tools, and discourse analysis is used to evaluate the sample's intra-cultural attitude towards hate speech as well as the differences and commonalities in beliefs and attitudes towards the Other and the Self. We focus on the respective roles of salience (Giora, 2003) and common ground (Kecskes & Zhang, 2009) to explain the internal dialogue that is witnessed during the interviews. Intra-cultural beliefs and attitudes that incline to rejecting out-groups seem to be confronted with the pressure of external calls for acceptance that characterize the European discourse. This negotiated positioning of the participants is externalized in what we have called 'a double act stance'.
A Deep Learning Framework for Automatic Airplane Detection in Remote Sensing Satellite Images

Automated object detection in high-resolution remote sensing satellite images(HRRSSI) is a proper solution for this task rather than manual detection using professional specialists. However, it is more complex due to the varying size, type, orientation, and complex background of the objects to detect. Utilizing artificial intelligence using deep learning is the state of the art technique to achieve this task. The number of labeled satellite images is limited for training a deep neural network therefore; transfer learning techniques were adopted for this task. This paper proposes a framework for airplane detection based on Convolution Neural network (CNN). Faster Region Based CNN (Faster R-CNN) framework is used to perform automatic airplane detection through transfer learning. Inception v2 is added to the network for feature extraction to enhance detection accuracy. The problem of information reduction of the objects due to the resizing of large size satellite image in test phase has been solved by adding a split layer before the input layer, together with a mosaic layer after detection output layer. Dataset is used to build and test the model is collected from Google Earth. Experimental results prove that the proposed developed model is extremely accurate for satellite images object detection.
Improved Tail Bounds for Missing Mass and Confidence Intervals for Good-Turing Estimator

The missing mass of a sequence is defined as the total probability of the elements that have not appeared or occurred in the sequence. The popular Good-Turing estimator for missing mass has been used extensively in language modeling and ecological studies. Exponential tail bounds have been known for missing mass, and improving them results in better confidence in estimation. In this work, we first show that missing mass is sub-Gamma on the right tail with the best-possible variance parameter under the Poisson and multinomial sampling models. This results in a right tail bound that beats the previously best known tail bound for deviation from mean up to about 0.2785. Further, we show that the sub-Gaussian approach cannot result in any improvement in the right tail bound for Poisson sampling. We derive confidence intervals for the Good-Turing estimator with better confidence levels and narrower width when compared to existing ones. Our results are worst case over all distributions.
A customised grammar framework for query classification

In real-life classification problems, prior information about the problem and expert knowledge about the domain are often used to obtain reliable and consistent solutions. This is especially true in fields where the data is ambiguous, such as text, in which the same words can be used in seemingly similar texts, but have a different meaning. A promising avenue for text classification is machine learning, which has been shown to perform well in a variety of applications including query classification and sentiment analysis. Many of the proposed approaches rely on the bag-of-words representation, which loses the information about the structure of the text. In this paper, we propose a Customised Grammar Framework for text classification, which exploits domain-related information and a new way to represent text as a series of syntactic categories forming syntactic patterns. The framework employs a formal grammar approach for transforming the text into the syntactic patterns representation. We applied the framework for the query classification problem and our results show that our approach outperforms previous ones in terms of classification performance.
Combinatorial Separable Convolutional Dictionaries

Recent works have considered the use of a linear combination of separable filters to approximate a non-separable filter bank (FB) to obtain computational advantages in CNNs and convolutional sparse representations/coding (CSR/CSC). However, it has been recently shown that there are advantages to directly solving the convolutional dictionary learning (CDL) problem considering a separable FB. A separable filter bank of M 2-d filters is typically constructed from a paired set of M horizontal filters and M vertical filters. In contrast, here we propose an outer product construction involving all possible combinations of vertical and horizontal filters, so that M vertical and M horizontal filters generate M <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> 2-d filters. Our computational experiments show that this alternative form results in a reduction in computation time of 10% and 80% for the CDL and CSC problems respectively, while matching the reconstruction performance of the typical separable FB approach for the same cardinality.
Detecting Abnormal Massive Crowd Flows: Characterizing Fleeing En Masse by Analyzing the Acceleration of Object Vectors

Surveillance systems play an important role in mitigating various types of misconduct. However, observing a vast number of surveillance feeds is an arduous task that can be solved with a computerized system. A motion-based estimation method is proposed to measure the weighted temporal difference between consecutive frames by using spatial mean-sigma observations. It calculates the temporal frame differences that indicate the object vectors (OVs), of which statistical characteristics are evaluated periodically to identify any crowd abnormality. The proposed method is able to accurately detect the unusual event at the earliest scene, based on original and public data sets.
Contextual Fact Ranking and Its Applications in Table Synthesis and Compression

Modern search engines increasingly incorporate tabular content, which consists of a set of entities each augmented with a small set of facts. The facts can be obtained from multiple sources: an entity's knowledge base entry, the infobox on its Wikipedia page, or its row within a WebTable. Crucially, the informativeness of a fact depends not only on the entity but also the specific context(e.g., the query).To the best of our knowledge, this paper is the first to study the problem of contextual fact ranking: given some entities and a context (i.e., succinct natural language description), identify the most informative facts for the entities collectively within the context.We propose to contextually rank the facts by exploiting deep learning techniques. In particular, we develop pointwise and pair-wise ranking models, using textual and statistical information for the given entities and context derived from their sources. We enhance the models by incorporating entity type information from an IsA (hypernym) database. We demonstrate that our approaches achieve better performance than state-of-the-art baselines in terms of MAP, NDCG, and recall. We further conduct user studies for two specific applications of contextual fact ranking-table synthesis and table compression-and show that our models can identify more informative facts than the baselines.
Time Split Based Pre-processing with a Data-Driven Approach for Malicious URL Detection

Malicious uniform resource locator (URL) host unsolicited content and are a serious threat and are used to commit cyber crime. Malicious URL’s are responsible for various cyber attacks like spamming, identity theft, financial fraud, etc. The internet growth has also resulted in increase of fraudulent activities in the web. The classical methods like blacklisting is ineffective in detecting newly generated malicious URL’s. So there arises a need to develop an effective algorithm to detect and classify the malicious URL’s. At the same time the recent advancement in the field of machine learning had shown promising results in areas like image processing, Natural language processing (NLP) and other domains. This motivates us to move in the direction of machine learning based techniques for detecting and classifying URL’s. However, there are significant challenges in detecting malicious URL’s that needs to be answered. First and foremost any available data used in detecting malicious URL’s is outdated. This makes the model difficult to be deployed in real time scenario. Secondly the inability to capture semantic and sequential information affects the generalization to the test data. In order to overcome these shortcomings we introduce the concept of time split and random split on the training data. Random split will randomly split the data for training and testing. Whereas time split will split the data based on time information of the URL’s. This in turn is followed by different representation of the data. These representation are passed to the classical machine learning and deep learning techniques to evaluate the performance. The analysis for data set from Sophos Machine Learning building blocks tutorial shows better performance for time split based grouping of data with decision tree classifier and an accuracy of 88.5%. Additionally, highly scalable framework is designed to collect data from various data sources in a passive way inside an Ethernet LAN. The proposed framework can collect data in real time and process in a distributed way to provide situational awareness. The proposed framework can be easily extended to handle vary large amount of cyber events by adding additional resources to the existing system.
Evaluation of shared resource allocation using SAND for ABR streaming

Adaptive bitrate (ABR) media streaming clients adjust the quality of media content depending on the current network conditions. The Shared Resource Allocation (SRA) feature defined in MPEG-SAND (Server and network assisted DASH), allows servers to allocate bandwidth to streaming clients. This enables coordination and prioritization of clients that are connected to the same network bottleneck, e.g. to maximize the number of clients that can play back a stream fluently. In this paper we evaluate different bandwidth limitation strategies and analyze the effects on the clients. For this purpose, a testbed using multiple Raspberry Pis was created. The results show that in various scenarios, SRA improves the fairness and the QoE of streaming sessions. Solely allocating a maximum quality level to the client is not sufficient in some cases. Therefore, additional means for SRA are evaluated.
SmokeOut: An Approach for Testing Clustering Implementations

Clustering is a key Machine Learning technique, used in many high-stakes domains from medicine to self-driving cars. Many clustering algorithms have been proposed, and these algorithms have been implemented in many toolkits. Clustering users assume that clustering implementations are correct, reliable, and for a given algorithm, interchangeable. We challenge these assumptions. We introduce SmokeOut, an approach and tool that pits clustering implementations against each other (and against themselves) while controlling for algorithm and dataset, to find datasets where clustering outcomes differ when they shouldn't, and measure this difference. We ran SmokeOut on 7 clustering algorithms (3 deterministic and 4 nondeterministic) implemented in 7 widely-used toolkits, and run in a variety of scenarios on the Penn Machine Learning Benchmark (162 datasets). SmokeOut has revealed that clustering implementations are fragile: on a given input dataset and using a given clustering algorithm, clustering outcomes and accuracy vary widely between (1) successive runs of the same toolkit; (2) different input parameters for that tool; (3) different toolkits.
A Verified Implementation of the Berlekamp–Zassenhaus Factorization Algorithm

We formally verify the Berlekamp-Zassenhaus algorithm for factoring square-free integer polynomials in Isabelle/HOL. We further adapt an existing formalization of Yun's square-free factorization algorithm to integer polynomials, and thus provide an efficient and certified factorization algorithm for arbitrary univariate polynomials. The algorithm first performs factorization in the prime field GF(p) and then performs computations in the ring of integers modulo pk , where both p and k are determined at runtime. Since a natural modeling of these structures via dependent types is not possible in Isabelle/HOL, we formalize the whole algorithm using locales and local type definitions. Through experiments we verify that our algorithm factors polynomials of degree up to 500 within seconds.
An FPGA-Based Computing Infrastructure Tailored to Efficiently Scaffold Genome Sequences

In the current years broad access to genomic data is leading to improve the understanding and prevention of human diseases as never before. De-novo genome assembly, represents a main obstacle to perform the analysis on a large scale, as it is one of the most time-consuming phases of the genome analysis. In this paper, we present a scalable, high performance and energy efficient architecture for the alignment step of SSPACE, a state of the art tool used to perform scaffolding also in case of de-novo assembly. The final architecture is able to achieve up to 9.83x speedup in performance when compared to the software version of Bowtie, a state of the art tool used by SSPACE to perform the alignment.
Comparing Topic Modeling and Named Entity Recognition Techniques for the Semantic Indexing of a Landscape Architecture Textbook

The task of manually annotating text is often tedious and error-prone. There is a strong need to digitize landscape history because a scalable, relational database with refined texts simply does not exist, ultimately limiting the pedagogical extent of this rich field. The data for the study conducted is a comprehensive textbook (544 pages) titled, “Landscape Design: A History of Landscape Architecture,” by Elizabeth Rogers. The Landscape Studies Initiative and Data Science Institute at the University of Virginia have partnered together to construct a SQL aided application (Flask) that will assist in deep annotation of scholarly texts. Our goal was to utilize machine learning techniques, specifically named entity recognition models (NER) and topic models (TM), not only to optimize the annotation process, but also to provide a fresh perspective on the text through a new index. In this paper, we will look at the training system, design, and architecture of several different NER models, including Python's spaCy, Stanford's Named Entity Recognizer, and IBM Bluemix's Natural Language Understanding tool, and compare their accuracies. Additionally, this paper aims to explore topic modeling from different tools and techniques, such as the Python libraries Gensim and Mallet in order to compare and contrast the relevance of those models to our dataset. The impact that these techniques have on the humanities fields can be astoundingly influential, but severely limited by the availability, size, and domain of the training dataset. Entity Recognition and Topic Modeling, as a result, are far from solved tasks: we will address some of the fundamental challenges that can prevent these systems from being robust and accurate.