Reviewerly/exHarmony huggingface dataset:
Online single and multiple analysis dictionary learning-based approach for visual object tracking

In recent years, various synthesis dictionary learning-based techniques have been applied for the task of visual object tracking. However, there are challenges in the representation ability of the target and the background using a synthesis dictionary. Owing to such challenges, the target in a given video sequence is not tracked very efficiently. We propose an online analysis dictionary learning framework for visual tracking. Our main motivation stems from the fact that analysis dictionary is able to capture significantly more variability in the data as compared to a synthesis dictionary of the same dimensions. We make use of single and multiple analysis dictionaries in our work. In order to leverage the advantage of multiple dictionaries, we adaptively fuse them. To the best of our knowledge, this is the first work that uses analysis dictionary for visual tracking. We use the entire OTB-50 dataset to evaluate the performance of proposed trackers. Our experiments show a significant improvement over existing synthesis dictionary learning approaches. This makes our approach much more preferable over the conventional approaches that use synthesis dictionary learning model for visual tracking.
Segmentation and Classification of CT Renal Images Using Deep Networks

The study of deep learning (LeCun et al. in Nature 521(7553):436, 2015 [1]) models, in particular, the convolutional neural networks (CNN), is playing a key role for various applications in medical domain since last decade. It has successfully demonstrated interesting results with higher accuracy which motivates sophisticated diagnosis tools in the Healthcare domain. We have done a study on using CNN models such as U-net and Alexnet on renal dataset for segmentation and classification of renal images. Data preprocessing on kidney images has been carried out using U-net architecture (Ronneberger et al. in U-net: convolutional networks for biomedical image segmentation. Springer, Berlin, pp. 234–241, 2015 [2]). A detailed study on fine tuning the hyper parameters that governs the model performance and test accuracy has been carried out. We achieved a dice coefficient of 83% in creating masks for renal data using U-net. We performed experiments on AlexNet and the best accuracy achieved is 94.75%. Finally, we have visualized the convolutional layers using saliency maps.
On Biases in Information Retrieval Models and Evaluation

The advent of the modern information technology has benefited society as the digitization of content increased over the last half-century. While the processing capability of our species has remained unchanged, the information available to us has been notably increasing. In this overload of information, Information Retrieval (IR) has been playing a prominent role by developing systems capable of separating relevant information from the rest. This separation, however, is a difficult task rooted in the complexity of understanding of what is and what is not relevant. To manage this complexity, IR has developed a strong empirical nature, which has led to the development of grounded retrieval models, resulting in the development of retrieval systems empirically designed to be biased towards relevant information. However, other biases have been observed, which counteract retrieval performance. In this thesis, the reduction of retrieval systems to filters of information, or sampling processes, has allowed us to systematically investigate these biases.
Ordinal Label Proportions

In Machine Learning, it is common to distinguish different degrees of supervision, ranging from fully supervised to completely unsupervised scenarios. However, lying in between those, the Learning from Label Proportions (LLP) setting [19] assumes the training data is provided in the form of bags, and the only supervision comes through the proportion of each class in each bag. In this paper, we present a novel version of the LLP paradigm where the relationship among the classes is ordinal. While this is a highly relevant scenario (e.g. customer surveys where the results can be divided into various degrees of satisfaction), it is as yet unexplored in the literature. We refer to this setting as Ordinal Label Proportions (OLP). We formally define the scenario and introduce an efficient algorithm to tackle it. We test our algorithm on synthetic and benchmark datasets. Additionally, we present a case study examining a dataset gathered from the Research Excellence Framework that assesses the quality of research in the United Kingdom.
Spin squeezing in Dicke-class of states with non-orthogonal spinors

The celebrated Majorana representation is exploited to investigate spin squeezing in different classes of pure symmetric states of N qubits with two distinct spinors, namely the Dicke-class of states. On obtaining a general expression for spin squeezing parameter, the variation of squeezing for different configurations is studied in detail.It is shown that the states in the Dicke-class, characterized by two-distinct non-orthogonal spinors, exhibit squeezing.
LiteRef: A software system for managing the knowledge of research literature

To acquire knowledge of a research area, a researcher needs to understand many papers and their interrelations. The existing reference management systems help the researcher maintain and use a database of research papers, but do little to help understand the research area as a whole. The paper presents LiteRef, a novel reference management system that addresses this need by effectively integrating tools for (1) organizing knowledge about both individual papers and connections between the papers, (2) visualizing whole research directions/areas and (3) producing well-formed surveys to share this knowledge in a variety of formats. After describing LiteRef’s design and features, the paper shows an example of how LiteRef helps further the author’s own research. Extensive documentation with a video demo for each major feature supplements the paper.
Increasing the Spatial Resolution of BTF Measurement with Scheimpflug Imaging

Abstract We present an improved way of acquiring spatially varying surface reflectance represented by a bidirectional texture function (BTF). Planar BTF samples are measured as images at several inclination angles which puts constraints on the minimum depth of field of cameras used in the measurement instrument. For standard perspective imaging, we show that the size of a sample measured and the achievable spatial resolution are strongly interdependent and limited by diffraction at the lens' aperture. We provide a formula for this relationship. We overcome the issue of the required depth of field by using Scheimpflug imaging further enhanced by an anamorphic attachment. The proposed optics doubles the spatial resolution of images compared to standard perspective imaging optics. We built an instrument prototype with the proposed optics that is portable and allows for measurement on site. We show rendered images using the visual appearance measured by the instrument prototype.
Web Service based Intelligent Search on Legal Documents

Web services such as RESTful APIs provide high flexibility for knowledge base systems in different domains. To apply it to the legal aspect, we can obtain much data of the case law efficiently enabling us to relate one case to another easily and even to compare the details of a plenty of the case laws simultaneously. Having said that, to ensure the performance of the web services and the accuracy of the data sourcing from them is onerous in the consideration of the backend system for the web services and relevant search engine.
A Framework to Create a Virtual Cyber Battlefield for Cyber Maneuvers and Impact Assessment

One of the best ways to recognize cyber threats is to perform cyber maneuvers. Performing cyber maneuvers in the operating environment has many challenges. The proposed approach is to perform cyber maneuvers and security analyses on a virtual environment that is quite similar to the physical environment. This challenge has been solved by modeling and simulating cyber battlefield. In this model, by gathering detailed data of cyberspace elements including knowledge repository of vulnerability, tangible and intangible elements of cyberspace and the relationships between them, cyber maneuvers, penetration testing, cyber-attacks injection, attack tracking, visualization, cyber-attacks impact assessment and risk assessment can be provided and executed. This research provides a model and tool for identifying the risks in the network and helping to prioritize actions to eliminate threats. For evaluation, the results were compared with the output of the security tools of the Iran fuel smart card project system and the results were reviewed in the focus group.
Towards practical homomorphic email filtering

A secure version of the naïve Bayesian filter (NBF) is proposed utilizing partially homomorphic encryption (PHE) scheme. SNBF can be implemented with only the additive homomorphism from the Paillier system, and we derive new techniques to reduce the computational cost of PHE-based SNBF. In the experiment, we implemented SNBF both in software and hardware. Compared to the best existing PHE scheme, we achieved 1,200x (resp., 398,840x) runtime reduction in the CPU (resp., ASIC) implementations, with additional 1,919x power reduction on the designated hardware multiplier. Our hardware implementation is able to classify an average-length email in 0.5 s, making it one of the most practical NBF schemes to date.
Detection and tracking of bubbles in two‐phase air water flow for non‐convergent sinusoidal channel

Bubble detection and tracking, which is essential for the enhancement of gas–liquid two-phase flow applications, is difficult due to optical noise surrounding bubble boundaries. Two-phase flow applications involving unconventional wavy channels further complicate these tasks by introducing non-linearity. In this work, the authors apply a path-based approach to transform a sinusoidal channel into a straightened horizontal channel for bubble detection and tracking. Segmented morphological operations are applied to the linear channel to identify each bubble as a single entity and to eliminate noise caused due to illumination conditions. The bubbles are detected through blob analysis and are associated across different frames based on motion, estimated by Kalman filter in both sinusoidal and path-based approach to the sinusoidal channel. The results show that the proposed path-based straightened channel gives more accurate results for the benchmark parameters of bubble count and bubble velocities than the sinusoidal channel with a percentage error reduction of 31.61% for bubble count and 27.36% for velocity.
Urban Sensing for Anomalous Event Detection:

Sensors deployed in different parts of a city continuously record traffic data, such as vehicle flows and pedestrian counts. We define an unexpected change in the traffic counts as an anomalous local event. Reliable discovery of such events is very important in real-world applications such as real-time crash detection or traffic congestion detection. One of the main challenges to detecting anomalous local events is to distinguish them from legitimate global traffic changes, which happen due to seasonal effects, weather and holidays. Existing anomaly detection techniques often raise many false alarms for these legitimate traffic changes, making such techniques less reliable. To address this issue, we introduce an unsupervised anomaly detection system that represents relationships between different locations in a city. Our method uses training data to estimate the traffic count at each sensor location given the traffic counts at the other locations. The estimation error is then used to calculate the anomaly score at any given time and location in the network. We test our method on two real traffic datasets collected in the city of Melbourne, Australia, for detecting anomalous local events. Empirical results show the greater robustness of our method to legitimate global changes in traffic count than four benchmark anomaly detection methods examined in this paper. Data related to this paper are available at: https://vicroadsopendata-vicroadsmaps.opendata.arcgis.com/datasets/147696bb47544a209e0a5e79e165d1b0_0 .
Learning to Cite: Transfer Learning for Digital Archives

We consider the problem of automatically creating citations for digital archives. We focus on the learning to cite framework that allows us to create citations without users or experts in the loop. In this work, we study the possibility of learning a citation model on one archive and then applying the model to another archive that has never been seen before by the system.
Introduction – Urban Mobility Design

Profound changes in society, such as types of energy, city densification, automated technology and progressive social inclusion, are impacting upon the traditional forms of urban mobility. Much existing land transport infrastructure – trains, buses, personal transport – is predicated upon old frameworks and patterns of use, such as cars facilitating suburban sprawl, vehicular shapes and forms built around the internal combustion engine, inaccessible public transport to persons of reduced mobility and transport planning dogma that sees moving from one place to another as a 'dis-utility'. While Design has always been a key arbiter of the visual expression of mobility, especially in the automotive industry as a driver of style and personal aspiration, public transport on the other hand has for the much part been hitherto relegated to dry unappealing bland functionality. The book takes the reader through the emerging landscape of urban mobility design and examines how design is addressing the issues surrounding driverless vehicles, share systems, universal access, ubiquitous information and user experience among other 'disrupters'. This work reflects current research but also sets the agenda for future speculations in the field.
Deeply Coupled Graph Structured Autoencoder for Domain Adaptation

This work introduced graph regularization to the framework of deep coupled autoencoders. The ensuing domain adaptation tool has been used on a variety of application areas -- image super-resolution, translingual document retrieval and multimodal information retrieval. In each case, the proposed method outperforms other coupled representation learning formulations and application specific approaches.
Linking Real Geographies and Virtual Realties with Immersive Geospatial Technologies

Due to the quantity of information available in the early twenty-first century, big data has become a frequently emphasized factor, and particularly so in the geographical research agenda. However, more data and information do not necessarily result in improved awareness of spatial problems: Communication of geographical knowledge (e.g., on environmental issues) is also complicated by geographical, temporal, and psychological distance between cause and effects, leading users to perceive spatial problems as taking place “later” or “elsewhere”. In this text, we discuss how immersive virtual reality (VR) technologies can help to overcome these different kinds of distances. Two broad topics are addressed: The first, covered in Sect. 4.2, explores the idea of immersion from a technological perspective and also discusses the main differences between immersive and current desktop-based geovisualization VR systems. The second, presented in Sect. 4.3, concerns the potential of immersive VR from the perspective of the user and introduces the concept of spatial presence. Against the theoretical background of Sects. 4.2 and 4.3, a working method defining how geospatial data can be visualized in immersive VR systems under current technological conditions is presented in Sect. 4.4. We argue that current geographic information systems (GIS) are not yet directly compatible with VR output devices, challenging both theoretical and practical models of GIS-based spatial data. The method proposed is set into practice in Sect. 4.5 using the example of a coral reef ecosystem. As is clear from these theoretical and practical considerations, immersive VR technologies can provide powerful tools to facilitate a better understanding of spatial problems in the twenty-first century.
Efficient Estimation of AUC in a Sliding Window

In many applications, monitoring area under the ROC curve (AUC) in a sliding window over a data stream is a natural way of detecting changes in the system. The drawback is that computing AUC in a sliding window is expensive, especially if the window size is large and the data flow is significant. In this paper we propose a scheme for maintaining an approximate AUC in a sliding window of length $k$. More specifically, we propose an algorithm that, given $\epsilon$, estimates AUC within $\epsilon / 2$, and can maintain this estimate in $O((\log k) / \epsilon)$ time, per update, as the window slides. This provides a speed-up over the exact computation of AUC, which requires $O(k)$ time, per update. The speed-up becomes more significant as the size of the window increases. Our estimate is based on grouping the data points together, and using these groups to calculate AUC. The grouping is designed carefully such that ($i$) the groups are small enough, so that the error stays small, ($ii$) the number of groups is small, so that enumerating them is not expensive, and ($iii$) the definition is flexible enough so that we can maintain the groups efficiently. Our experimental evaluation demonstrates that the average approximation error in practice is much smaller than the approximation guarantee $\epsilon / 2$, and that we can achieve significant speed-ups with only a modest sacrifice in accuracy.
Matlab GUI Application for Moving Object Detection and Tracking

In this paper a novel tool for moving object detection and tracking is presented. The main contribution of the proposed application is the achievement of a simple and intuitive graphic interface during the extraction the silhouette of targets by means of a new algorithm. This proposed algorithm which combined frame difference method, background subtraction method, Laplace filter and Canny edge detector together can realize a way to achieve sparse detection fast. Some modular architecture in this Graphical User Interface has been developed in order to enhance the user’s experience. The experiment was tested by using sequence images from the MULTIVITION dataset, and experimental results showed that our proposed method has more validity and flexibility to get the desired result than conventional algorithm.
Classifying Emotions in Twitter Messages Using a Deep Neural Network

Many people use social media nowadays to express their emotions or opinions about something. This paper proposes the use of a deep learning network architecture for emotion classification in Twitter messages, using the six emotions model of Ekman: happiness, sadness, anger, fear, disgust and surprise. We collected the tweets from a labeled dataset that contains about 2.5 million tweets and used the Word2Vec predictive model to learn the relations of each word and transform them into numbers that the deep network receives as input. Our approach achieved a 63% accuracy with all the classes and 77% accuracy on a binary classification scheme.
Mining High-Utility Irregular Itemsets

High-utility itemset mining (HUIM) currently plays an important role in a wide range of applications and data mining community. Several algorithms, methods and data structures have been proposed to improve efficiency of mining for such itemsets. Besides, HUIM is extended in several aspects including the regarding of “regularity or irregularity of occurrence” on high utility itemsets. This leads to the emerging of high-utility regular itemsets mining (HURIM) and high-utility irregular itemsets mining (HUIIM) which can help to observe occurrence behavior of high utility itemsets. Based on HURIM, the regularity threshold is applied to measure interestingness of itemsets and to prune search space. However, on HUIIM, the threshold cannot help to prune uninteresting itemsets causing this task consumes high computational cost. Thus, in this paper, we here present a single-pass algorithm, called HUIIM (High-Utility Irregular Itemset Miner), for efficiently mining high-utility irregular itemsets. The new-modified utility list structure (NUL) is applied for maintaining occurrence information simultaneously with utility value of an itemset and also for fast calculation of total utility of the itemset. Moreover, a new pruning technique is designed and applied to improve computational performance of HUIIM. Experimental studies were conducted on synthetic and real datasets to show efficiency of HUIIM (with and without the new pruning technique) in the terms of computational time and memory usage.
A method to generate traverse paths for eliciting missing requirements

After a developer has developed a software system, his/her customers sometimes claim missing requirements. Since there are various kinds of missing requirements, there are no silver bullets to solve all of these situations. The approach of our research to solve this problem is based on conceptual models. We focus on a web system as an example to find missing requirements with regard to accessing paths to pull certain information from known information. An association between two concepts in a conceptual model implies that a user of the web system traverses from a page in which he/she got information to another page in which he/she will find new information. We exploit a method to derive possible accessing paths from associations in a conceptual model. Class Diagram in UML is applied to draw conceptual models with actors and their accessing permissions; which are, create, read, update, and delete. As a result, linearly connected paths are generated for each actor with regard to their accessing permissions. Each path from one concept to another concept via multiple concepts is called a traverse. The method is implemented as a tool named Traverser. Traverser automatically generates an actors' possible accessing paths with a conceptual model, so that developers and requesters able to become aware of missing requirements. In order to evaluate the effectiveness of the method, we studied a case and clarified whether the tool contributes to the completeness of requirements or not. The domain of the case is academic affairs. As a result of the case study, Traverser was able to generate new accessing paths that had not previously been implemented within the system.
Moving Object Classification Under Illumination Changes Using Binary Descriptors

Object recognition and classification has become important in a surveillance video situated at prominent areas such as airports, banks, military installations, etc. Outdoor environments are more challenging for moving object classification because of incomplete appearance details of moving objects due to illumination changes and large distance between the camera and moving objects. As such, there is a need to monitor and classify the moving objects by considering the challenges of video in the real time. Training the classifiers using feature-based approaches is easier and faster than pixel-based approaches in object classification. Extraction of a set of features from the object of interest is most important for classification. Viewpoint and sources of light illumination plays major role in the appearance of an object. Abrupt transitions are identified using Chi-square and corners are detected using Harris corner detection. Silhouettes are captured using background subtraction and feature extraction is done using ORB. k-NN classifier is used for classification.