Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Locative Inconsistency-tolerant Hierarchical Probabilistic CTL Model Checking: Survey and Future Work

A locative inconsistency-tolerant hierarchical probabilistic computation tree logic (LIHpCTL) is introduced in this paper to establish the logical foundation of a new model checking paradigm. This logic is an extension of several previously proposed extensions of the standard temporal logic known as CTL, which is widely used for model checking. The extended model checking paradigm proposed is intended to appropriately verify locative (spatial), inconsistent, hierarchical, probabilistic (randomized), and time-dependent concurrent systems. Additionally, a survey of various studies on probabilistic, inconsistency-tolerant, and hierarchical temporal logics and their applications in model checking is conducted.
Sparse Multivariate Bernoulli Processes in High Dimensions

We consider the problem of estimating the parameters of a multivariate Bernoulli process with auto-regressive feedback in the high-dimensional setting where the number of samples available is much less than the number of parameters. This problem arises in learning interconnections of networks of dynamical systems with spiking or binary valued data. We also allow the process to depend on its past up to a lag p, for a general $p \geq 1$, allowing for more realistic modeling in many applications. We propose and analyze an $\ell_1$-regularized maximum likelihood (ML) estimator under the assumption that the parameter tensor is approximately sparse. Rigorous analysis of such estimators is made challenging by the dependent and non-Gaussian nature of the process as well as the presence of the nonlinearities and multi-level feedback. We derive precise upper bounds on the mean-squared estimation error in terms of the number of samples, dimensions of the process, the lag $p$ and other key statistical properties of the model. The ideas presented can be used in the rigorous high-dimensional analysis of regularized $M$-estimators for other sparse nonlinear and non-Gaussian processes with long-range dependence.
Multiview monocular depth estimation using unsupervised learning methods

Existing learned methods for monocular depth estimation use only a single view of scene for depth evaluation, so they inherently overfit to their training scenes and cannot generalize well to new datasets. This thesis presents a neural network for multiview monocular depth estimation. Teaching a network to estimate depth via structure from motion allows it to generalize better to new environments with unfamiliar objects. This thesis extends recent work in unsupervised methods for single-view monocular depth estimation and uses the reconstruction losses for training posed in those works. Models and baseline models were evaluated on a variety of datasets and results indicate that indicate multiview models generalize across datasets better than previous work. This work is unique in that it emphasizes cross domain performance and ability to generalize more so than performance on the training set. Thesis Supervisor: Jonathan P. How Title: Professor of Aeronautics and Astronautics
Novelty Detection for Person Re-identification in an Open World

A fundamental assumption in most contemporary person re-identification research, is that all query persons that need to be re-identified belong to a closed gallery of known persons, i.e., they have been observed and a representation of their appearance is available. For several real-world applications, this closed-world assumption does not hold, as image queries may contain people that the re-identification system has never observed before. In this work, we remove this constraining assumption. To do so, we introduce a novelty detection mechanism that decides whether a person in a query image exists in the gallery. The re-identification of persons existing in the gallery is easily achieved based on the persons representation employed by the novelty detection mechanism. The proposed method operates on a hybrid person descriptor that consists of both supervised (learnt) and unsupervised (hand-crafted) components. A series of experiments on public, state of the art datasets and in comparison with state of the art methods shows that the proposed approach is very accurate in identifying persons that have not been observed before and that this has a positive impact on re-identification accuracy.
Air Quality Prediction in Visakhapatnam with LSTM based Recurrent Neural Networks

The research activity considered in this paper concerns about efficient approach for modeling and prediction of air quality. Poor air quality is an environmental hazard that has become a great challenge across the globe. Therefore, ambient air quality assessment and prediction has become a significant area of study. In general, air quality refers to quantification of pollution free air in a particular location. It is determined by measuring different types of pollution indicators in the atmosphere. Traditional approaches depend on numerical methods to estimate the air pollutant concentration and require lots of computing power. Moreover, these methods cannot draw insights from the abundant data available. To address this issue, the proposed study puts forward a deep learning approach for quantification and prediction of ambient air quality. Recurrent neural networks (RNN) based framework with special structured memory cells known as Long Short Term Memory (LSTM) is proposed to capture the dependencies in various pollutants and to perform air quality prediction. Real time dataset of the city Visakhapatnam having a record of 12 pollutants was considered for the study. Modeling of temporal sequence data of each pollutant was performed for forecasting hourly based concentrations. Experimental results show that proposed RNN-LSTM frame work attained higher accuracy in estimating hourly based air ambience. Further, this model may be enhanced by adopting bidirectional mechanism in recurrent layer.
The nature of inhibition in a working memory search task

ABSTRACT Inhibition is one of the key processes that regulate working memory (WM) activity. The main research question concerned the nature of inhibition of highlighted information in WM. The study was designed to examine two competing hypotheses. The first assumed that inhibition is carried out by deletion of irrelevant information from the focus of attention (cognitive inhibition account). An alternative hypothesis assumed that it is the response to a correctly recognised stimulus that is inhibited (effortful response inhibition account). Two experiments with a spatial modification of the WM serial search task were conducted wherein two factors were manipulated: serial order of the items, and distance from the central fixation point. In both experiments, higher response time was correlated with a smaller distance from the fixation point. The inhibited items were recognised equally efficiently or better than the uninhibited items. The results support the effortful response inhibition account over the cognitive inhibition account.
Improving Video Object Detection by Seq-Bbox Matching

Video object detection has drawn more and more attention in recent years. Compared with object detection from image, object detection in video is more useful in many practical applications, e.g. self-driving cars, smart video surveillance, etc. It is highly required to build a fast, reliable and low-cost video-based object detection system for these applications. In this work, we propose a novel, simple and highly effective boxlevel post-processing method to improve the accuracy of video object detection. The proposed method is based on both online and an offline settings. Our experiments on ImageNet object detection from video (VID) dataset show that our method brings important accuracy gains, especially to more challenging fast-moving object detection, with quite light computational overhead in both settings. Applied to YOLOv3, our system achieves so far the best speed/accuracy trade-off for offline video object detection and competitive detection improvements for online object detection.
Left Ventricle Motion Estimation in Cine MRI With Multilayer Iterative Deformable Graph Matching

Quantifying regional myocardial motion and deformation from cardiac magnetic resonance imaging (MRI) plays an important role in clinical applications. In this paper, we present a novel approach for the estimation of left ventricle myocardial motion based on iterative deformable graph matching for cine MRI. Graph matching is a shape matching approach that can be used to determine the correspondence between two objects. However, existing graph matching algorithms are unsuitable for applications with large deformations. In this paper, we propose an iterative deformable graph matching framework for estimating the correspondence between points extracted from left ventricle myocardium at the end-diastolic and end-systolic phases to estimate cardiac motion. A new cost function for graph matching is defined to measure the discrepancy between the nodes and edges of two graphs under a transformation. By introducing a spatial transformation with a sparsity constraint, we can estimate a robust deformation field, alleviating the influence of inevitable graph mismatches. The correspondence between points is then updated by mapping the source graph using the estimated transformation. The cost function is optimized by alternatively optimizing for correspondence and spatial transformation. Furthermore, we propose a multilayer framework to improve correspondence accuracy using a bottom-up matching procedure. This framework estimates the deformation field between an image at the end-systolic phase and an image at the end-diastolic phase in an MRI sequence. Evaluations of two public cardiac datasets indicate that the proposed framework outperforms traditional graph matching algorithms in accuracy and robustness.
Precise, General, and Efficient Data-flow Analysis for Security Vetting of Android Apps

vii CHAPTER 1: INTRODUCTION 1 1.1 Android Security Issues and Vetting System 1 1.2 Previous Works on Android Security Analysis 2 1.2.1 Android Static Analysis 2 1.2.2 Dynamic and Hybrid Analysis 5 1.3 Android Static Analysis Challenges 6 1.4 Contributions 7 CHAPTER 2: ANDROID SYSTEM MODEL 10 2.1 Runtime Environment Model 10 2.1.1 Event-based System 10 2.1.2 Component-level Environment Model 12 2.2 System API Model 13 2.2.1 Heap Manipulation Summary Language 14 CHAPTER 3: DATA-FLOW ANALYSIS 16 3.1 Reaching Facts Analysis (RFA) 17 3.1.1 Notations 17 3.1.2 The Basic DFG Building Process 18 3.1.3 Handle Library APIs 22 3.2 Summary-based Bottom-up Data-flow Analysis (SBDA) 22 3.3 Discussion 25 CHAPTER 4: COMPONENT-BASED ANALYSIS 27 4.1 Motivating Example 27 4.2 Building the Component-level Data-flow Graph 30 4.3 Building the Component-level Data Dependence Graph 30 4.4 Linking Inter-component Data Flows 32 4.4.1 Intent 36 4.4.1.1 ST Construction 36 4.4.1.2 Stitching Intent Channels – Intent Destination Resolution 37 4.4.2 RPC 38 4.4.2.1 ST Construction 38
Applying the Decision Model and Notation in Practice: A Method to Design and Specify Business Decisions and Business Logic

Proper decision-making is one of the most important capabilities of an organization. Therefore, it is important to make explicit all decisions that are relevant to manage for an organization. In 2015 the Object Management Group published the Decision Model and Notation (DMN) standard that focuses on modelling business decisions and underlying business logic. DMN is being adopted at an increasing rate, however, theory does not adequately cover activities or methods to guide practitioners modelling with DMN. To tackle this problem this paper presents a method to guide the modelling process of business decisions with DMN. The method has been validated and improved with an experiment using thirty participants. Based on this method, future research could focus on further validation and improvement by using more participants from different industries.
Effective practical learning model for the subject of basic information technology

Learning process requires students to have the ability to use computer as a means of life. A learning that utilizes technological devices entails special treatment in its process, including practice pattern usage. This article reveals the concept of technology learning process based on students’ perspective. The research applied a facts disclosure method of technology learning based on the observation and interviews with the X class students at the International Standard Boarding School of Amantul Ummah, Mojokerto, on learning introduction of MsWord as word processor software. The research finding shows that the effectiveness of technology learning process is influenced by the adequate facilities, teacher competence, student motivation, and learning atmosphere. As a part of technological learning, the effective practical technology-based learning process should: explain the benefits that students will gain; the ease of technology device usage; the access and availability of technology devices, the facilitation for student’s knowledge sharing, and using of multimedia in the learning process.
Effect of Closed-Loop Motion Cueing Algorithm for a Six-Degrees-of-Freedom Dynamic Simulator on Pupil Diameter as a Driver Stress Factor

This paper describes the contribution of the closed-loop control of the motion platform (six degrees of freedom: longitudinal, lateral, and vertical displacements; pitch, roll, yaw) and motion platform’s three-dimensional (3D) displacement scale factor (SF) (0.2 and 1.0) on eye pupil diameter (PD) as an objective measure of driver cognitive load. Longitudinal, lateral, and vertical accelerations as well as longitudinal, lateral, and vertical positions from the center of gravity (CG) of the vehicle were registered through the driving simulation software SCANeRstudio® from OKTAL. Closed-loop control decreases the driver mental load. This type of closed-loop control can be used to decrease the driver mental load.
Classifying abnormalities in computed tomography radiology reports with rule-based and natural language processing models

Purpose: When conducting machine learning algorithms on classification and detection of abnormalities for medical imaging, many researchers are faced with the problem that it is hard to get enough labeled data. This is especially difficult for modalities such as computed tomography (CT) with potentially 1000 or more slice images per case. To solve this problem, we plan to use machine learning algorithms to identify abnormalities within existing radiologist reports, thus creating case-level labels that may be used for weakly supervised training on the image data. We used a two-stage procedure to label the CT reports. In the first stage, a rule-based system labeled a smaller set of cases automatically with high accuracy. In the second stage, we developed machine learing algorithms using the labels from the rule-based system and word vectors learned without supervision from unlabeled CT reports. Method: In this study, we used approximately 24,000 CT reports from Duke University Health System. We initially focused on three organs, the lungs, liver/gallbladder, and kidneys. We first developed a rule-based system that can quickly identify certain types of abnormalities within CT reports with high accuracy. For each organ and disease combination, we produced several hundred cases with rule-based labels. These labels were combined with word vectors generated using word2vec from all the unlabeled reports to train two different machine learning algorithms: (a) average of word vectors merged by logistic regression, and (b) recurrent neural network (RNN). Result: Performance was evaluated by receiver operating characteristic (ROC) area under the curve (AUC) over an independent test set of 440 reports for which those organs were manually labeled as normal or abnormal by clinical experts. For lungs, the performance was 0.796 for average word vector and 0.827 for RNN. Liver performance was 0.683 for average word vector and 0.791 for RNN. For kidneys, it was 0.786 for average word vector and 0.928 for RNN. Conclusion: It is possible to label large numbers of cases automatically. These rule-based labels can then be used to build a classification model for large numbers of medical reports. With word2vec and other transfer learning techniques, we can get a good generalization performance.
Syntactic Matching Methods in Pivot Translation

The pivot translation is useful method for translating between languages that contain little or no parallel data by utilizing equivalents in an intermediate language such as English. Commonly, phrase-based or tree-based pivot translation methods merge source pivot and pivot target translation models into a source target model. This tactic is known as triangulation. However, the combination is based on the surface forms of constituent words, and it often produces incorrect source target phrase pairs because of interlingual di erences and semantic ambiguities in the pivot language. The translation accuracy is thus degraded. This paper proposes a triangulation approach that utilizes syntactic subtrees in the pivot language to avoid incorrect phrase combinations by distinguishing pivot language words by their syntactic roles. The results of the experiments conducted on the United Nations Parallel Corpus demonstrate that the proposed method is superior to other pivot translation approaches in all tested combinations of languages.
ElGamal cryptosystem-based secure authentication system for cloud-based IoT applications

Life in modern society becomes easier due to the rapid growth of different technologies like real-time analytic, ubiquitous wireless communication, commodity sensors, machine learning and embedded systems. Nowadays, there seems to be a need to merge these technologies in the form of Internet of Things (IoT) so that smart systems can be achieved. On the other hand, cloud computing is a pillar in IoT by which end users get connected through the cloud servers for getting different services. However, to recognise the legitimacy of communicators during communication sessions through insecure channels like the Internet, serious issues in cloud-based IoT applications need to be addressed. Thus, authentication procedure is highly desirable to remove the unapproved access in IoT applications. This study presents an ElGamal cryptosystem and biometric information along with a user's password-based authentication scheme for cloud-based IoT applications refereed as SAS-Cloud . Security of the proposed scheme has been analysed by well popular random oracle model and it is found that SAS-Cloud has the ability to defend all the possible attacks. Furthermore, the performance of SAS-Cloud has been evaluated and it was found that SAS-Cloud has better efficiency than other existing competing ElGamal cryptosystem-based authentication schemes.
Driver Cognitive Workload Detection via Eye-tracking and Physiological Modalities

In recent years, autonomous car development has become one of the hottest topics in AI applications and the driver cognitive workload monitoring system is a critical element of the autonomous car. This study explored the feasibility of classifying driver cognitive workload levels with eye-tracking and physiological modalities individually. Around a 70% detection accuracy was obtained with both modalities for ternary
Robust Model-Based Reliability Approach to Tackle Shilling Attacks in Collaborative Filtering Recommender Systems

As the use of recommender systems becomes generalized in society, the interest in varying the orientation of their recommendations is increasing. There are shilling attacks’ strategies that introduce malicious profiles in collaborative filtering recommender systems in order to promote the own products or services or to discredit those of the competition. Academic research against shilling attacks has been focused in statistical approaches to detect the unusual patterns in user ratings. Nowadays, there is a growing research area focused on the design of robust machine learning methods to neutralize the malicious profiles inserted into the system. This paper proposes an innovative robust method, based on matrix factorization, to neutralize the shilling attacks. Our method obtains the reliability value associated with each prediction of a user to an item. By monitoring the unusual reliability variations in the items prediction, we can avoid promoting the shilling predictions to the erroneous recommendations. This paper openly provides more than 13 000 individual experiments involving a wide range of attack strategies, both push, and nuke, in order to test the proposed approach. The results show that the proposed method is able to neutralize most of the existing attacks; its performance only decreases in the not relevant situations: when the attack size is not large enough to effectively affect the recommendations provided by the system.
Linked open data of bibliometric networks: analytics research for personalized library services

Purpose – Citation analysis is an important measure for the assessment of quality and impact of academic entities (authors, papers and publication venues) used for ranking of research articles, authors and publication venues. It is a common observation that high-level publication venues, with few exceptions (Nature, Science and PLOS ONE), are usually topic specific. The purpose of this paper is to investigate the claim correlation analysis between topic specificity and citation count of different types of publication venues (journals, conferences and workshops). Design/methodology/approach – The topic specificity was calculated using the information theoretic measure of entropy (which tells us about the disorder of the system). The authors computed the entropy of the titles of the papers published in each venue type to investigate their topic specificity. Findings – It was observed that venues usually with higher citations (high-level publication venues) have low entropy and venues with lesser citations (not-high-level publication venues) have high entropy. Low entropy means less disorder and more specific to topic and vice versa. The input data considered here were DBLP-V7 data set for the last 10 years. Experimental analysis shows that topic specificity and citation count of publication venues are negatively correlated to each other. Originality/value – This paper is the first attempt to discover correlation between topic sensitivity and citation counts of publication venues. It also used topic specificity as a feature to rank academic entities.
Language cues in the formation of hierarchical representations of space

ABSTRACT We study the role of place and region names in the formation of spatial hierarchies. Representations were probed behaviorally, i.e., by choices between two equidistant routes differing only in the number of regions they touched. Routes crossing fewer region boundaries are preferred over routes crossing more region boundaries. Visual landmark objects from a common semantic category placed in spatial vicinity induce region representations. Here we show that region dependent route preference is reduced if the landmark objects are replaced by signs displaying object names. The effects of explicit region names and linguistic properties of different naming schemes are discussed.