Miracle-dz/UniFAR-FTUs huggingface dataset:
Small baseline InSAR time series analysis: Unwrapping error correction and noise reduction

Abstract We present a review of small baseline interferometric synthetic aperture radar (InSAR) time series analysis with a new processing workflow and software implemented in Python, named MintPy ( https://github.com/insarlab/MintPy ). The time series analysis is formulated as a weighted least squares inversion. The inversion is unbiased for a fully connected network of interferograms without multiple subsets, such as provided by modern SAR satellites with small orbital tube and short revisit time. In the routine workflow, we first invert the interferogram stack for the raw phase time-series, then correct for the deterministic phase components: the tropospheric delay (using global atmospheric models or the delay-elevation ratio), the topographic residual and/or phase ramp, to obtain the noise-reduced displacement time-series. Next, we estimate the average velocity excluding noisy SAR acquisitions, which are identified using an outlier detection method based on the root mean square of the residual phase. The routine workflow includes three new methods to correct or exclude phase-unwrapping errors for two-dimensional algorithms: (i) the bridging method connecting reliable regions with minimum spanning tree bridges (particularly suitable for islands), (ii) the phase closure method exploiting the conservativeness of the integer ambiguity of interferogram triplets (well suited for highly redundant networks), and (iii) coherence-based network modification to identify and exclude interferograms with remaining coherent phase-unwrapping errors. We apply the routine workflow to the Galapagos volcanoes using Sentinel-1 and ALOS-1 data, assess the qualities of the essential steps in the workflow and compare the results with independent GPS measurements. We discuss the advantages and limitations of temporal coherence as a reliability measure, evaluate the impact of network redundancy on the precision and reliability of the InSAR measurements and its practical implication for interferometric pairs selection. A comparison with another open-source time series analysis software demonstrates the superior performance of the approach implemented in MintPy in challenging scenarios.
Differentiating Malicious and Benign Android App Operations Using Second‐Step Behavior Features

Security-sensitive operations in Android applications (apps for short) can either be benign or malicious. In this work, we introduce an approach of static program analysis that extracts fisecond-step behavior featuresfl, i.e., what was triggered by the security-sensitive operation, to assist app analysis in differentiating between malicious and benign operations. Firstly, we summarized the characteristics of malicious operations, such as spontaneity, independence, stealthiness and continuity, which can be used to classify the malicious operations and benign ones. Secondly, according to these characteristics, Second step behavior features (SSBFs for short) have been presented, including structural features and semantic features. Thirdly, an analysis prototype named SSdroid has been implemented to automatically extract SSBFs of security-sensitive operations. Finally, experiments on 9285 operations from both benign and malicious apps show that SSBFs are effective and usefulness. Our evaluation results suggest that the second-step behavior can greatly assist in Android malware detection.
Propose a Travel-Package Recommendation System

: Traveling recommendation is very important for user who is that the set up for traveling. There are several existing techniques that are used for travel recommendation. the present system have some drawbacks of system or technology. To resolve the matter of providing travel package recommendation system. On the opposite side, to urge a lot of business and profit, the travel corporations need to perceive these preferences from completely different tourists and serve a lot of attractive packages. Therefore, the demand for intelligent travel services, from each tourists and travel corporations, is predicted to extend dramatically. Since recommender systems are with success applied to boost the standard of service for patrons in a very range of ﬁelds, it's natur al direction to develop recommender systems for customized travel package recommendation.
Rethinking Network QoS using Cloud Overlays

We revisit the long-standing problem of providing network QoS to applications, and propose the concept of judicious QoS -- combining the cheaper, best effort IP service with the cloud, which offers a highly reliable infrastructure and the ability to add in-network services, albeit at higher cost. Our proposed J-QoS framework offers a range of reliability services with different cost vs. delay trade-offs, including: i) a forwarding service that forwards packets over the cloud overlay, ii) a caching service, which stores packets inside the cloud and allows them to be pulled in case of packet loss or disruption on the Internet, and iii) a novel coding service that provides the least expensive packet recovery option by combining packets of multiple application streams and sending a small number of coded packets across the more expensive cloud paths. We demonstrate the feasibility of these services using measurements from RIPE Atlas and a live deployment on PlanetLab. We also consider case studies on how J-QoS works with services up and down the network stack, including Skype video conferencing, TCP-based web transfers, and cellular access networks.
Vital-Sign Processing Receiver With Clutter Elimination Using Servo Feedback Loop for UWB Pulse Radar System

This brief presents a vital-sign processing circuit for simultaneous dc/near-dc elimination and out-of-band interference rejection without any digital signal processing or algorithm assistance for the ultrawideband (UWB) pulse-based radar system. An intrinsic self-balanced MOS diode (SBMD) was proposed as a stable and balanced pseudo resistor applied under a servo feedback loop in a vital-sign receiver of the sensing radar to perform as a high-pass filter (HPF) with an ultralow corner frequency lower than 0.5 Hz for removing undesired clutters of the reflected signals and input dc-offset voltages from innate circuit offsets. A third-order switched-capacitor (SC) Chebyshev low-pass filter (LPF) with leap-frog topology as the subsequent stage was adopted to suppress the out-band noises, thereby establishing an integrated vital-sign processing circuit with bandpass frequency response and incorporating it into a radar module to verify its viability.
MmWave Multiuser MIMO Precoding With Fixed Subarrays and Quantized Phase Shifters

Millimeter-wave (mmWave) wireless communication promises high data-rates when combined with multiuser multiple-input multiple-output (MU-MIMO) technology. A practical deployment of these technologies, however, faces numerous challenges, including the design of energy-efficient analog hardware. To address these challenges, we consider a hybrid base station architecture that consists of a set of fixed subarrays with quantized phase shifters (FS-QPS). For this system, we investigate the multiuser beamforming gains with different phase-quantization levels and subarray geometries. We show that for zero forcing baseband precoding, analog precoder optimization becomes an eigenvalue maximization problem, which can be approximated efficiently by received power maximization. We develop an efficient, optimal analog precoder for well-established mmWave channel models, and provide performance bounds characterizing the required phase-shifting accuracy for a beam-steering codebook as a function of the geometric size of the subarray. To demonstrate the efficacy of the proposed FS-QPS architecture, we show simulation results using the latest 3GPP mmWave channel model for multiuser spectral efficiency, and compare our solution to existing architectures.
A Multiobjective QoS Model for Trading Cloud of Things Resources

The emerging Cloud of Things (CoT) paradigm promises to meet the diverse requirements of many real-world applications, which previously could not be fulfilled by either cloud computing or Internet of Things (IoT). Trading CoT resources is a challenging aspect, particularly when managing quality of service (QoS) as resource providers and application developers have different priorities. This article focuses on the challenge of supporting QoS when trading CoT resources and performing resource allocation. The contributions of this article are: 1) the problem of managing QoS while trading CoT resources is investigated as an optimization problem; 2) a QoS model is proposed to solve the problem by optimizing five different QoS objectives; and 3) experimental evaluation of the proposed model using three optimization algorithms. The evaluation results show the efficiency and dynamism of the proposed model in optimizing CoT resource allocation based on diverse QoS objectives, including resource cost, energy consumption, response time, fault tolerance, and resource coverage.
A Noncoherent Massive MIMO System Employing Beamspace Techniques

Noncoherent detection schemes are an appealing and low-complexity alternative in multi-user massive MIMO uplink systems compared to classical coherent detection algorithms, since no actual channel knowledge is required at the receiver. For noncoherent multi-user detection to function, the induced power at the base station is utilized to separate the different users. However, spatial separation is impossible when the users are located in the far-field of the receiving antenna array. Consequently, noncoherent detection fails in such scenarios. To this end, beamspace techniques can be applied, focusing the energy of the incident wave to a smaller subset of the receive antennas and enabling again the noncoherent detection scheme. This paper analyzes the beamspace capabilities of a dielectric lens and an analog beamforming network applied at the receiver. Furthermore, a sub-array architecture is proposed, relaxing the design requirements for practical implementation. It is shown that noncoherent detection in combination with beamspace techniques performs comparably to channel-estimation-based detection. In addition, the sub-array architecture revealed a significant performance enhancement accompanied by a reduced user separability.
Text Mining and Subject Analysis for Fiction; or, Using Machine Learning and Information Extraction to Assign Subject Headings to Dime Novels

Abstract This article describes multiple experiments in text mining at Northern Illinois University that were undertaken to improve the efficiency and accuracy of cataloging. It focuses narrowly on subject analysis of dime novels, a format of inexpensive fiction that was popular in the United States between 1860 and 1915. NIU holds more than 55,000 dime novels in its collections, which it is in the process of comprehensively digitizing. Classification, keyword extraction, named-entity recognition, clustering, and topic modeling are discussed as means of assigning subject headings to improve their discoverability by researchers and to increase the productivity of digitization workflows.
Spatiotemporal Representation Learning For Human Action Recognition And Localization

SPATIOTEMPORAL REPRESENTATION LEARNING FOR HUMAN ACTION RECOGNITION AND LOCALIZATION Alaaeldin Ali Advisor: University of Guelph, 2019 Dr. Graham W. Taylor Co-Advisor: Dr. Rozita Dara Human action understanding from videos is one of the foremost challenges in computer vision. It is the cornerstone of many applications like human-computer interaction and automatic surveillance. The current state of the art methods for action recognition and localization mostly rely on Deep Learning. In spite of their strong performance, Deep Learning approaches require a huge amount of labeled training data. Furthermore, standard action recognition pipelines rely on independent optical flow estimators which increase their computational cost. We propose two approaches to improve these aspects. First, we develop a novel method for efficient, real-time action localization in videos that achieves performance on par or better than other more computationally expensive methods. Second, we present a self-supervised learning approach for spatiotemporal feature learning that does not require any annotations. We demonstrate that features learned by our method provide a very strong prior for the downstream task of action recognition.
A synergetic strategy of automobile intelligent cruise system based on fuzzy control adopting hierarchical structure

To improve automobile travel safety and relieve the intension of driving fatigue, a synergetic strategy on the throttle and brake combined control of automobile cruise system is proposed based on fuzzy control theory. A hierarchical control structure, including the upper fuzzy controller and the lower model match controller, is designed for integrated control function. The upper controller consists of the two input variables, that is, the deviation of the theoretical safe distance and the relative distance and the relative velocity between the host car and the preceding vehicle, and the single output variable, that is, the excepted acceleration of host car. The lower controller has achieved the fast compensation of excepted acceleration by adopting parallel feedforward and feedback compensator to input into the inverse dynamics model of automobile cruise system. Using MATLAB/Simulink, the control function on the proposed synergetic strategy is validated for different working cases. The results show that the synergetic strategy possesses an integrated capability involved automated stop & go control, and following the preceding car to prevent rear-end collision, and the constant velocity cruise whether the driving conditions of the city road or expressway.
Campus-Level Instagram Traffic: A Case Study

Instagram is a popular network application for photo sharing, video streaming, and online social media interaction. In this paper, we present results from an initial characterization study of Instagram network traffic, as viewed from a large campus edge network. Despite the challenges of NAT, DHCP, end-to-end encryption, and high traffic volume, we are able to identify key characteristics of Instagram traffic, which exceeds 1 TB per day. The main highlights from our study include classic observations such as diurnal usage patterns, Zipf-like distributions for IP frequency-rank profile, and heavy-tailed transfer size distributions.
A Holistic Approach to Insider Threat Detection

Insider threats constitute a major problem for many organizations. Traditional security mechanisms, such as intrusion detection systems and firewalls, do not represent optimal solutions for insider threat detection and prevention. That is because insider threats are generally performed by people that are already trusted, and who possess access to, and knowledge of, important organizational assets. In this thesis, we explore three possible approaches to applying machine learning to classify insider threat behaviors; supervised-, unsupervised-, and reinforcement learning. We describe the development of an unsupervised machine learning system that aims to detect malicious insider threat activity by analyzing data from different technical sources. The system was developed to be simple and easy to assemble. By utilizing existing machine learning algorithms we tested the performance of this system. The results showed that the system was able to detect malicious insider activity with a weak to moderate positive relationship in the training phase, and a negligent positive relationship in the testing phase. The results suggest that we cannot solely rely on this machine learning system for the detection of insider threats with the system in its current state. We conclude from these preliminary explorations that machine learning shows some promise as a measure for insider threat detection if used in adjunct to manual forensics work. To improve the performance of the current system, it seems necessary to include more substance to the selected features, such as the name of files, subject and header of e-mail, what type of websites are visited. In addition, the physical security and cybersecurity aspects, as well as psychological, and organizational factors should be addressed when considering the insider threat. Future research should focus on acquiring real datasets, aggregation of insider threat scenarios and use cases, and testing different machine learning approaches both from technical and non-technical sources.
A Review, Current Challenges, and Future Possibilities on Emotion Recognition Using Machine Learning and Physiological Signals

The seminal work on Affective Computing in 1995 by Picard set the base for computing that relates to, arises from, or influences emotions. Affective computing is a multidisciplinary field of research spanning the areas of computer science, psychology, and cognitive science. Potential applications include automated driver assistance, healthcare, human-computer interaction, entertainment, marketing, teaching and many others. Thus, quickly, the field acquired high interest, with an enormous growth of the number of papers published on the topic since its inception. This paper aims to (1) Present an introduction to the field of affective computing though the description of key theoretical concepts; (2) Describe the current state-of-the-art of emotion recognition, tracing the developments that helped foster the growth of the field; and lastly, (3) point the literature take-home messages and conclusions, evidencing the main challenges and future opportunities that lie ahead, in particular for the development of novel machine learning (ML) algorithms in the context of emotion recognition using physiological signals.
Inflamação e coagulação em pacientes infectados pelo HIV-1

The author discuses the management of some opportunistic diseases more commonly observed in South American AIDS patients than in European ones. Characteristics of coinfection with HIV and leprosy, paracoccidioidomycosis, Chagas's disease, mucocutaneous leishmaniasis, malaria, disseminated BCG and strongyloidiasis are reviewed, with special emphasis on preferred therapeutic schedules for these conditions. (AU);
A multifaceted students’ performance assessment framework for motion-based game-making projects with Scratch

ABSTRACT In the last few years, engaging students to create digital games has been a pole of attraction for many teachers and researchers, resulting in highly positive learning experiences and promoting their thinking skills, e.g., programming and computational thinking (CT) skills. Researchers have already stated about the need for further research not only around the evaluation techniques and tools of the quality of these complex educational interventions, but mainly about ways to ease the assessment of students’ performance from multiple perspectives with authenticity. This paper contributes to proposing a multifaceted assessment framework of the degree of students’ acquisition of multiple skills, when they get involved in digital motion-based touchless game-making course-projects with the MIT Scratch tool. The results of its implementation during a pilot study with computer science undergraduate students, which are presented, highlight the positive effects of combining and extending various assessment techniques and tools to draw holistic conclusions about students’ higher skills including computational and spatial thinking skills.
Resource Allocation Schemes Based on Coalition Games for Vehicular Communications

In this letter, we propose a suboptimal resource allocation scheme for the uplink vehicular communications networks. Our aim is to maximize the minimum throughput of all vehicle-to-vehicle (V2V) links under the constraints of the minimum vehicle-to-infrastructure (V2I) link throughput requirements. The optimization problem has a non-linear and non-convex objective function and we cannot obtain the optimal solutions directly. To solve this problem, we first propose a V2V partitioning method from the view point of game theory. Then, an efficient power allocation algorithm is developed to obtain the optimal power allocation solutions by exploiting the D.C. (difference of convex functions) structure of the objective function. Simulation results demonstrate the effectiveness of the proposed resource allocation algorithms.
Augmented Reality Using Visible Light Communication

In this thesis we propose the use of visible light communication to implement Augmented Reality System. Augmented Reality (AR) is an interactive, real time system that combines virtual information over real graphical information. Visible light communication uses light in visible light spectrum between 400 to 800 THz as a medium of communication. Existing methods of achieving augmented reality using visible light communication either use a custom made image sensors to detect light position or do not overlay virtual information over real environment accurately. In addition some existing solutions make use of color markers which are useless in the dark. We propose an AR system using Light Emitting Diodes (LEDs) installed on target objects as markers to display the virtual information accurately. This type of AR system is particularly useful to mark large objects like exterior of a building or its sections for example each floor or each window. Each light source transmits information which can be read using image sensor in a camera and determine the exact position where virtual information needs to be displayed. The virtual information can be predetermined or may depend on the information received in the form of light. An algorithm has been developed to detect the position of LEDs and project a predetermined virtual information in camera live feed. A prototype has been developed to evaluate the algorithm using custom designed programmable LED lights, Point Gray Grasshoper3 camera, NVidia Terga K1 GPU running Ubuntu 14.04 to aid intensive computation and C++ with OpenCV library for image
Optimizing Network Latency with Fast Path Assignment for Incoming Flows

—Various ﬂows in the network require to go through different types of middlebox. The improper placement of network middlebox and path assignment for ﬂows could greatly increase the network latency and also decrease the performance of network. Minimizing the total end to end latency of all the ows requires to assign path for the incoming ﬂows. In this paper, the ﬂow path assignment problem in regard to the placement of various kinds of middlebox is studied. The ﬂow path assignment problem is formulated to a linear programming problem, which is very time consuming. On the other hand, a naive greedy algorithm is studied. Which is very fast but causes much more latency than the linear programming algorithm. At last, the paper presents a heuristic algorithm named FPA, which takes bottleneck link information and estimated bandwidth occupancy into consideration, and achieves near optimal latency in much less time. Evaluation results validate the effectiveness of the proposed algorithm.