Reviewerly/exHarmony huggingface dataset:
Freedom of Expression and Censorship

The freedom of expression (FE) is a fundamental right whose forms of interference occur in various types and degrees in countries of the American continent. The newspapers’ financial crisis has forced a great amount of independent media to close cycles, or else enter into partnerships with large media groups whose interests and political orientation prevail over local needs. In several countries of the Americas, the Special Rapporteur for Freedom of Expression has documented numerous cases of incriminated journalists as well as individuals that take part or witness social protests under different criminal types. In pursuit of FE, several advocacy organizations have focused their efforts on the topic of violence against journalists and media organizations. Progress in norms preventing the form of interference with FE remains confined to a select group of countries that include Chile, Colombia, Uruguay, Argentina and Peru.
A Unified Reconfigurable Commutation Scheme of Fast Fourier Transform

The article offers the method for FFT calculation by means of unified communication scheme stage-by-stage. There is an iterating equation for hardware and software implementation. Also the reconfiguration of schemes by different samples number is provided too. The rotating multipliers are the same like in non-reconfigurable (fixed) communication scheme. So the offered approach does not required additional hardware or software resources.
Capturing Information Behaviour in the Wild

Understanding complex information behaviour in an empirical, ecologically valid way is difficult at the best of times. It verges on impossible with artificial environments and tightly constrained tasks such as those found in usability labs. Similarly, artificial measures of user satisfaction that are readily available from test collections are unlikely to provoke revolutionary insights. The alternative is to study user behaviour in the wild, however this involves complex trade-offs between method, precision, privacy, interpretation and generalizability. This tutorial addresses both qualitative and quantitative methods, and how to balance the risks of studying unpredictable but fully human behaviour.
Lesion Detection of Cerebral Infarction by a Machine Learning Model that Learned ‘Normal’ [Presidential Award Proceedings]

We attempted to detect and diagnose cerebral infarction on diffusion weighted image (DWI) using a machine learning model trained using normal brain data. Our machine learning model consisted of two parts. One consisted of an autoencoder (AE), which learned only normal DWI information. This AE model did not reproduce the lesion ; the input image was subtracted and the generated image contributed to abnormal signal detection. The other part was a classifier constructed with SqueezeNet, which distinguished between infarct and normal areas using the original DWI image, AE generated images, and subtraction images. The accuracy of the combination of AE and SqueezeNet in 2-class classification for abnormal detection on DWI was 0.97.
Pattern Classification in Heterogeneous Domains Based on Evidence Theory (Poster)

In pattern classification, there exists a challenging problem when there are no training patterns. In some real applications, there may exist some labeled data in other related domain (called source domain), and such labeled data can be helpful to solve the classification problem in target domain. It is considered that the source domain and target domain are heterogeneous here. A new heterogeneous data transfer classification method based on evidence theory is proposed. Some corresponding patterns (pattern pairs) in source domain and target domain are given to build the link of these two domains. For each pattern in target domain, it is hard to determine one exact mapping value in source domain due to the distinct characteristics of these two domains. So we estimate a mapping scope in source domain using KNN technique. The target pattern is allowed to have multiple mapping values in the scope with different weights/reliabilties. These mapping values can produce different classification results. Evidence theory is good at combining the uncertain information. Therefore a new weighted DS fusion method is developed for combining these classification results, which are discounted by the corresponding weights, and the final class decision is made according to the combination result. A pair of heterogeneous remote sensing images and some UCI data sets are used in this paper to test the performance of our method with respect to several other methods, and it shows that the new method can efficiently improve the classification accuracy.
Feasibility of Force Presentation in an Arbitrary Direction Using the Ungrounded Force Display Device with Cam

This research proposes the ungrounded force display device using the cam. Using the human illusion, the user can feel the force in one direction when they are imposed to asymmetric reciprocating motion. This device is mainly configured the cam and the slider. The slider follows the cam and moves in asymmetric reciprocating motion. We have proposed the two prototypes in the previous papers. In the first prototype, we confirmed the usefulness of the generation of the asymmetric acceleration. In the second prototype, we proposed to solve the problems in the first prototype. It presents the force to users more clearly than the first prototype. These prototypes present the force to only one direction. However, in order to apply the device to a certain practical application such as mobile navigation device to present the direction to a destination, the device needs to present the force in any direction in user's hand. Therefore, in this paper, we investigate the feasibility of omni-directional force presentation using developed second prototype. We conducted 2 experiments and 1 additional experiment. These experimental results confirm that the feasibility of omni-directional force presentation. In addition, these results suggest that we should grasp the device using fingertips to clearly perceive the force direction.
Deep learning detects actionable molecular and clinical features directly from head/neck squamous cell carcinoma histopathology slides

The purpose of this abstract is to describe the application of deep learning to digital histopathology slide data for detection of clinically relevant features. Deep learning is a form of artificial intelligence which can process graphical data and “learn” to extract hidden features. Here we test the ability of deep learning to detect human papilloma virus, location of origin, and other features.
Control System Design and Implementation

This chapter covers control system design for the robot. The control system of the pipe inspection robot is based on several concepts, related to control of mobile robots and arm-type robots. Structure of the control system is discussed in the aspect of communication, power supply, user interface and connections between different elements. Finally, software implementation is discussed on the level of on-board controller and high-level PC computer application with user graphical interface. Execution of the pedipulators trajectories is provided by the designed control system to transform the robot to different work environments.
Research on New Event Detection Methods for Mongolian News

New event detection (NED) aims at detecting the first news from one or multiple streams of news stories. This paper is aimed at the field of journalism and studies the related methods of Mongolian new event detection. The paper proposes a method that combines the similarity of news content with the similarity of news elements to detect the new event. For the news content representation, according to the characteristics of the news and the different vocabulary expressions in different news categories, improve the traditional TF-IDF method. In addition, extract the main elements of the news, including time, place, subject, object, denoter, and calculate the similarity of news elements between the two news documents. Finally, the similarity between the news content and the news elements is combined to calculate the final similarity for new event detection. The experimental results show that the improved method is obvious, and the performance is significantly improved compared with the traditional new event detection system.
INDIAN-CELEBNET: A Dataset for Generative Modelling of Indian Celeb Faces

In the past decade, Deep Learning has enjoyed success mostly in Supervised-Learning problems, due to which, the future of Deep Learning lies in solving Semi-supervised, Unsupervised and Reinforcement Learning Problems. Generative Modeling is a technique which is highly essential for tackling these areas of problems. In this paper, we contribute to the research in the field of Generative Modeling by presenting a medium sized, high quality, manually curated and robust dataset of consistent images containing faces of Indian Celebrities titled: INDIAN-CELEBNET. We also provide four baseline GANs for the future research to improve upon. The results of our experiments demonstrate the ability and copiousness of the dataset to allow the research of new architectures for deep generative networks.
Research on Evaluation of Innovation and Entrepreneurship Education in Local Universities under the Background of Big Data

The arrival of the era of big data has had a profound impact on the education system. As far as local university entrepreneurship education is concerned, in the context of big data, the evaluation model can be better applied to analyze the basic features of entrepreneurship education. Based on the profound influence of big data on entrepreneurship education, this paper analyzes the combination of entrepreneurship education and big data with the sample of entrepreneurship education in local colleges and universities, and analyzes the construction of scientific and reasonable evaluation system of entrepreneurship education.
Adversarial Machine Learning Challenges

Machine learning algorithms provide the ability to quickly adapt and find patterns in large diverse data sources and therefore are a potential asset to application developers in enterprise systems, networks, and security domains. They make analyzing the security implications of these tools a critical task for machine learning researchers and practitioners alike, spawning a new subfield of research into adversarial learning for security-sensitive domains. The work presented in this book advanced the state of the art in this field of study with five primary contributions: a taxonomy for qualifying the security vulnerabilities of a learner, two novel practical attack/defense scenarios for learning in real-world settings, learning algorithms with theoretical guarantees on training-data privacy preservation, and a generalization of a theoretical paradigm for evading detection of a classifier. However, research in adversarial machine learning has only begun to address the field's complex obstacles—many challenges remain. These challenges suggest several new directions for research within both fields of machine learning and computer security. In this chapter we review our contributions and list a number of open problems in the area.
Coding in Term-rank Metric

This paper investigates coding for crisscross errors in the term-rank metric. The term-rank of a matrix equals to minimal number of row and columns that collectively contain all of non zero elements of the matrix. We consider data stored in arrays (matrices) over finite fields. Crisscross errors occur e.g. in memory arrays, FSK demodulation, magnetic tapes or in OFDM and FDM transmissions. In the paper, we introduce construction of term-rank codes with different distances. Also, we investigate polynomial-time decoding for term-rank codes. Research results derived from the paper show that the term-rank metric is more suitable than the rank metric for correcting crisscross errors.
A Robust Student's t-Based Labeled Multi-Bernoulli Filter

The paper presents the problem of multi-target tracking with heavy-tailed process and measurement noises. Such heavy-tailed noises can reflect unmodeled anomalies, sudden disturbance, or temporary sensor failures. A robust Student&#x0027;s t-based labeled multi-Bernoulli (RSTLMB) filter is designed for such systems, where the state predicted probability density and measurement likelihood function of individual targets are modeled as Student&#x0027;s t distributions. A closed form recursion of the RSTLMB filter to jointly estimate the target state and the parameters of the Student&#x0027;s t distribution is derived in the variational Bayesian framework. Simulations on multi-target tracking with heavy tailed process and measurement noises demonstrate the effectiveness and superiority of the proposed RSTLMB filter.
Evaluating deep learning based models for predicting click through rate

Deep neural networks are finding their way into most of the fields in today's world. One such field is predicting the click through rate for online advertising. Multiple deep neural network based models for click through rate prediction have been proposed in the last few years. Model assessment is crucial in solving any real world problem. With this paper, we shed some light on the recent deep learning models which have been outperforming traditional models lately. Two benchmark datasets were used: namely Criteo and Avazu dataset. Comprehensive experiments were conducted to demonstrate the superiority of some of the models over others. Most studies include pre-setting the hyper parameters for optimum performance. In this study, the models learn the hyper parameters to further improve the results and maximize the predictive accuracy. Key findings include how inclusion of feature dependency in the model can be beneficial when data present has higher number of features. Factors influencing model selection such as no pre-training or feature engineering required might not be the best options when aiming for performance. Finally, this study concludes how hyper parameters affect chosen deep model's performance.
Hadath: From Social Media Mapping to Multi-Resolution Event-Enriched Maps

Publicly available data is increasing rapidly, and will continue to grow with the advancement of technologies in sensors, smartphones and the Internet of Things. Data from multiple sources can improve coverage and provide more relevant knowledge about surrounding events and points of Interest. The strength of one source of data can compensate for the shortcomings of another source by providing supplementary information. Maps are also getting popular day-by-day and people are using it to achieve their daily task smoothly and efficiently. Starting from paper maps hundred years ago, multiple type of maps are available with point of interest, real-time traffic update or displaying micro-blogs from social media. In this paper, we introduce Hadath, a system that displays multi-resolution live events of interest from a variety of available data sources. The system has been designed to be able to handle multiple type of inputs by encapsulating incoming unstructured data into generic data packets. System extracts local events of interest from generic data packets and identify their spatio-temporal scope to display such events on a map, so that as a user changes the zoom level, only events of appropriate scope are displayed. This allows us to show live events in correspondence to the scale of view - when viewing at a city scale, we see events of higher significance, while zooming in to a neighbourhood, events of a more local interest are highlighted. The final output creates a unique and dynamic map browsing experience. Finally, to validate our proposed system, we conducted experiments on social media data.
The dynamics of permutations on irreducible polynomials

We study degree preserving maps over the set of irreducible polynomials over a finite field. In particular, we show that every permutation of the set of irreducible polynomials of degree k over Fq is induced by an action from a permutation polynomial of Fqk with coefficients in Fq. The dynamics of these permutations of irreducible polynomials of degree k over Fq, such as fixed points and cycle lengths, are studied. As an application, we also generate irreducible polynomials of the same degree by an iterative method.
Secured CBIR with Anonimity Preserving for Images and Users in Cloud Environment

With advancement in imaging devices, the images are playing a vital role in everyday life. Multimedia image usage across various platforms are increased by major usage of communication technologies. The images in medical, automotive and various other domain carries the sensitive information in images. Handling images on large scale is expensive and computationally tedious. Major organization outsource the centralized image repository and other computations on image to cloud server. The cloud being the cheapest way of handling operations with images but lacks the concern of security and privacy. Example, the images of patients in hospitals and other personal images have to be encrypted before outsourcing to the cloud vendor, Content Based Image Retrieval (CBIR) then can be used for querying image. The Paper presents a novel method of outsourcing image repository without revealing information about images to cloud vendors by encrypting image dataset. The extracted feature vectors are used to classify the images are also encrypted, and a local hashing table is developed to improve the search efficiency. The generated feature vector values are secured using kNN. The experiment results have shown acceptable level of efficiency.
Cooperative Semi-supervised Regression Algorithm based on Belief Functions Theory

Semi-supervised learning (SSL), which can exploit both labeled and unlabeled samples, has attracted a lot of research attention. Semi-supervised regression is an important content in semi-supervised learning. The traditional semi-supervised regression methods may encounter uncertainty problems in the learning process. In this paper, a cooperative semi-supervised regression method based on belief functions theory is proposed. The proposed method uses belief functions to address the uncertainty in the semi-supervised regression. The algorithm uses two belief functions based regressors and labels the unlabeled samples based on the combined results of the two regressors. The labeling confidence of an unlabeled sample is estimated through the reduction in mean squared error over the labeled neighborhood of the given sample. Experimental results show that the proposed method can effectively exploit unlabeled samples to obtain better regression performance.
Cross Input Neighbourhood Difference for Re-identification of Occupational Humans

In this research study, we have proposed an appearance-based classifier to classify human corresponding to their attire and clothing. Personality re-identification proposes to address the issue of keeping track of persons. Our proposed methodology presents the advance work on both CUHK03 and CUHK01 dataset, and bypass over-fitting problem by artificially enlarging the dataset using label-preserving transformations. The model is fine-tunned on a small amount of target dataset, which achieved results equal to the state-of-the-art. Deep convolutional architecture has been used with Cross input Neighborhood difference to solve the problem of re-identification through robustness and positional differences.
Editorial : Archaeology and information research

The aim of this special issue is to highlight the interdisciplinary nexus in a series of papers that explore and investigate the intersections of archaeologies and the different areas of informatio ...
Incorporating Aircraft Kinematics and Radar Cross Section into the Performance Prediction of Air Surveillance

The evolution of modern radar is heading toward a networked, multifunctional, adaptive, and cognitive system. The network of software-controllable fast-adapting radars follows a highly complex control and operation logic. It is not straightforward to assess its instantaneous capability to detect, track, and recognize targets. To be able to predict or optimize the system performance, one has to understand its behavior not only on a general level, but also in various operating conditions and considering the target behavior and properties accurately. In this paper, we propose the fusion of radar and tracker recordings with an extensive database of cooperative aircraft navigation recordings and radar cross section data to assess and learn the performance measures for the air surveillance. The main contribution of this paper is the incorporation of the aircraft kinematics, orientation, and radar cross section into an automated measurement-based analysis. We consider the employment of the measurement-based metrics and machine learning in the performance prediction. Simulations and experiments with real-life data demonstrate the feasibility and potential of the proposed concept.
Integrated Local Linearization Particle Filter for Multiple Maneuvering Target Tracking in Clutter

The integrated particle filter (IPF) algorithm is proposed for single target tracking in clutter that combines the existing particle filters with false track discrimination (FTD) which distinguishes between the true tracks and the false tracks using the target existence probability as a track quality measure. To improve the tracking performance of IPF for maneuvering multitarget tracking, we propose an integrated local linearization particle filter (ILLPF) that applies the FTD to LLPF which approximates the optimal importance density with the updated estimates of a bank of tracking filters. The proposed algorithm is extended to accommodate interacting multiple model-linear multitarget-ILLPF (IMM-LM-ILLPF) for maneuvering target tracking with multiple target dynamic models for robust tracking. A study with Monte Carlo simulation demonstrates the improvement of maneuvering multitarget tracking performance in cluttered environments.
Parameter-free Image Segmentation Based on Extreme Learning Machine

For the problem of spending much time on adapting parameters, a parameter-free image segmentation method based on extreme learning machine (ELM) is proposed. Firstly, each image is segmented as superpixels by simple linear iterative clustering (SLIC) with different parameters. Secondly, each superpixel segmentation result is combined with some rules, and initial segmentation results are obtained. Each initial segmentation result is evaluated, and the parameter with the best performance is selected as its class. Thirdly, in order to construct the training sets of ELM, the cooccurrence of each image is constructed, and some of its attributes are calculated as its features, and a parameter-free framework is learned by ELM. The experimental results show that the proposed method in this paper gets better segmentation results, which is closer to human annotation than other methods.