Miracle-dz/UniFAR-FTUs huggingface dataset:
You Can Run, But You Cannot Hide: Using Elevation Profiles to Breach Location Privacy through Trajectory Prediction

The extensive use of smartphones and wearable devices has facilitated many useful applications. For example, with Global Positioning System (GPS)-equipped smart and wearable devices, many applications can gather, process, and share rich metadata, such as geolocation, trajectories, elevation, and time. For example, fitness applications, such as Strava and Runkeeper, utilize information for activity tracking, and have recently witnessed a boom in popularity. Those trackers have their own web platforms, and allow users to share activities on such platforms, or even with other social network platforms. To preserve privacy of users while allowing sharing, those platforms allow users to disclose partial information, such as the elevation profile for an activity, which supposedly will not leak the location trajectory. In this work we examine the extent to which publicly available elevation profiles can be used to predict the location trajectory of users. To tackle this problem, we devise three threat settings under which the city, borough, or even a route can be predicted. Those threat settings define the amount of information available to the adversary to launch the prediction attacks. Establishing that simple features of elevation profiles, e.g., spectral features, are insufficient, we devise both natural language processing (NLP)-inspired text-like representation and computer vision-inspired image-like representation of elevation profiles, and we convert the problem at hand into text and image classification problem. We use both traditional machine learning- and deep learning-based techniques, and achieve a prediction success rate ranging from 59.59% to 95.83%. The findings are alarming, and highlight that sharing information such as elevation profile may have significant privacy risks.
Introduction to Coresets: Accurate Coresets

A coreset (or core-set) of an input set is its small summation, such that solving a problem on the coreset as its input, provably yields the same result as solving the same problem on the original (full) set, for a given family of problems (models, classifiers, loss functions). Over the past decade, coreset construction algorithms have been suggested for many fundamental problems in e.g. machine/deep learning, computer vision, graphics, databases, and theoretical computer science. This introductory paper was written following requests from (usually non-expert, but also colleagues) regarding the many inconsistent coreset definitions, lack of available source code, the required deep theoretical background from different fields, and the dense papers that make it hard for beginners to apply coresets and develop new ones. The paper provides folklore, classic and simple results including step-by-step proofs and figures, for the simplest (accurate) coresets of very basic problems, such as: sum of vectors, minimum enclosing ball, SVD/ PCA and linear regression. Nevertheless, we did not find most of their constructions in the literature. Moreover, we expect that putting them together in a retrospective context would help the reader to grasp modern results that usually extend and generalize these fundamental observations. Experts might appreciate the unified notation and comparison table that links between existing results. Open source code with example scripts are provided for all the presented algorithms, to demonstrate their practical usage, and to support the readers who are more familiar with programming than math.
Trends in Computing 2015 ( ICRTC-2015 ) Test Case Generation and Optimization using UML Models and Genetic Algorithm

Software testing plays an important role in software development because it can minimize the development cost. Software testing approaches are divided into three parts i.e. code based testing, specification based testing and model based testing. In model based testing, the testing begins at design phase. So, early detection of faults can be achieved by using this approach further reducing time, cost and efforts of the developer to a large extent. In this paper we propose a technique for generating test cases using both UML sequence diagram and state chart diagram. Our approach is to convert Sequence Diagram into Sequence Graph and then to convert State Chart Diagram into State chart Graph. Finally System Testing Graph (SYTG) is formed by combining the two Graphs. From the graphs different control flow sequences also called test cases are identified and then optimized using evolutionary Algorithm, called Genetic Algorithm. Although State chart did better at revealing unit level faults and sequence diagram is better at removing integration level faults, but after integrating the two graphs, generated test cases are appropriate for system testing and to identify Error handling, correctness, pre-post condition faults, interaction, message sequence faults and scenario faults. © 2015 The Authors. Published by Elsevier B.V. Peer-review under responsibility of organizing committee of the 3rd International Conference on Recent Trends in Computing 2015 (ICRTC-2015).
Reducing bycatch using modifications to sweeps and lines anterior to the trawl mouth-collaboration with the Technical University of Denmark FINAL REPORT

Penaeid trawling is among the world's least selective fishing methods which, in several fisheries, has led to spatial closures being implemented if certain bycatch caps are exceeded. Considerable work has been done to develop modifications to trawls that reduce unwanted bycatches with most focussed at, or near, the codend. But more recent efforts have examined 30 ways to prevent bycatch entry into trawls entirely—via modifications to anterior components. This study assessed the utility of proactively lowering the headlines of Australian penaeid trawls, using clips at the otter boards, to 68 and 54% of the conventional height, and demonstrated mean total bycatch reductions (by weight) of 69 and 79%, respectively, without affecting catches of the targeted Metapenaeus macleayi. The results provide insights into the 35 location and behaviour of various species in the water column as trawls approach, while
Myocardial Steatosis and Left Ventricular Function in Type 2 Diabetes Mellitus : Assessed with Magnetic Resonance Imaging and Spectroscopy

The aim of the thesis was to provide more insight into the influence of myocardial steatosis on left ventricular function in healthy volunteers and in patients with type 2 diabetes mellitus. Therefore we developed a reproducible proton magnetic resonance (MR) spectroscopic technique with respiratory motion compensation to study myocardial steatosis. Using these technique, combined with MR imaging to study myocardial function, correlations between myocardial steatosis and left ventricular function were shown in several (patho)physiological conditions. Furthermore, we showed that myocardial triglyceride content is increased in patients with type 2 diabetes mellitus and is an independent predictor of left ventricular diastolic dysfunction. In addition, differential, tissue-specific partitioning of triglycerides and/or fatty acids among non-adipose organs during various diets was shown. Given the obesity and type 2 diabetes mellitus pandemic and the increasing evidence indicating that lipid oversupply to cardiomyocytes plays a role in the development of diabetic cardiomyopathy, therapeutic strategies that target reduction of cardiac lipid overexposure might be beneficial to prevent diabetic cardiomyopathy.
The effect of spectral centroid on perceived birdsong in urban forests

Probe into the relationship between spectral centroid and perception of birdsong in urban forests. Spectral centroid (SC) and Spectral centroid bandwidth (SBW) were considered as potential factors affecting perceived birdsong. In this study, we made the subjective evaluation of birdsong in three urban forested area. In the meantime, we collected recordings at each observation site. Finally, through polynomial fitting, the relationship between the SC, SBW, and the pleasantness of perceived birdsong were found. Results show that both of SC and SBW are potential factors and the latter is more important. The trends of SC and SBW with perceived birdsong is opposite.
No RNN SQuAD

In this paper, we present a deep learning approach to answering questions in SQuAD, the Stanford Question Answering Dataset. We train and test on SQuAD 2.0, where the model either gives an answer if one exists or predicts N/A if an answer does not exist. Given that current state-of-the-art models fall short of human performance, our goal is to understand the challenges that come with implementing a system to answer these questions, analyze the benefits and shortcomings of such systems, and implement our own system that relies on successful techniques. Our aim was to improve a baseline BiDAF model by first incorporating small yet significant changes to the original model, such as adding character embeddings to the model. Then, we sought to change the structure of the model to a QANet model, which relies on an intricate structure based on encoder blocks and advanced attention techniques to provide answers as output to the input questions. Our team experimented with a variety of techniques, including analyzing a characterembedding system versus a QANet system, tweaking hyperparameters such as the hidden size, batch size, and the dropout rate, and experimenting with various optimizers. We found that our implementation of QANet produced mediocre results, even though we checked the dimensions and followed the paradigm as closely as we could. Instead, we ended up experimenting with a character-embedding system, modifying it strategically to analyze which factors would result in higher EM/F1 scores. Our system achieved dev scores of F1: 62.006 and EM: 65.472 and test scores of F1: 60.101 and EM: 63.716. Ultimately, the Adam optimizer with a variable learning rate outperformed the Adadelfa optimizer in the short run but fell short by the end, larger hidden size and batch size were better, and a dropout probability of 0.1 seemed to be most optimal.
$\lambda$-Domain Perceptual Rate Control for 360-Degree Video Compression

The 360-degree video is projected to 2-D formats using various projection methods for efficient compression. As a necessary part of general-video compression, rate control is also indispensable for the projected 360-degree video compression. However, the current rate control algorithm has not been optimized for the 360-degree video compression yet. The Coding Tree Unit (CTU) level bit allocation in the rate control algorithm has not taken into consideration the characteristic that various pixels in 2-D formats have different influences on the visual experiences. In this article, we first propose an optimal CTU level weight taking this characteristic into consideration. The CTU level weight is an approximation to the pixel level weight since the smallest granularity of a rate control algorithm is usually CTU. Second, based on the CTU level weight, a weighted CTU level bit allocation algorithm is proposed to achieve better coding performance. The bits of each CTU are assigned that the Lagrange multiplier $\lambda$ of a CTU is inversely proportional to its CTU level weight. This CTU level bit allocation scheme is applied to all the 360-degree video projection formats. Third, we propose a CTU row (CR) level rate control algorithm for the Equi-Rectangle Projection (ERP) format. Different CTUs in the same row in the ERP format are combined into a CR to provide more stable model parameters. The proposed algorithms are implemented in the newest video coding standard High Efficiency Video Coding (HEVC) reference software. The experimental results show that the proposed algorithm is able to achieve much better subjective and objective qualities as well as smaller bitrate errors compared with the state-of-the-art rate control algorithm.
Detoxification of Arsenic and Chromium through Chelators and enzymes : an In-silico approach

Rapid industrialization has led to an increase in the release of heavy metals in the environment thus becoming a major concern for the human health. Some heavy metals are essential for the biochemical processes of organisms but their increased concentration may lead to toxicity and many diseases. Thus there is an urgent necessity to look for new and more effective methods for the removal of heavy metals. Chelation therapy is one of the upcoming and efficient approach for the removal of heavy metals. Chelators such as Dimercaptosuccinic acid (DMSA), 2, 3-Dimercapto-1propanesulfonic acid (DMPS), and ethylene diamine tetra acetic acid (EDTA) etc. were used in earlier studies but their clinical trials have shown no effect against arsenic and chromium detoxification. In the present work, we conducted molecular docking studies of enzymes and chelators with chromium and arsenic compounds. Combinatorial studies of chelators bounded protein with other chelators were also performed. The chelators, pentetic acid and Calcium disodium versenate (CaNa2EDTA) exhibited best docking scores with arsenic and chromium compounds. Thus it can be proposed that these chelators may be used in metal detoxification, also this is for the first time that these chelators are used for the remediation of heavy metals.
MODIS and PROBA-V NDVI Products Differ when Compared with Observations from Phenological Towers at Four Tropical Dry Forests in the Americas

The Normalized Difference Vegetation Index (NDVI) is widely used to monitor vegetation phenology and productivity around the world. Over the last few decades, phenology monitoring at large scales has been possible due to the information and metrics derived from satellite sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS) or the Project for On-Board Autonomy–Vegetation (PROBA-V). However, due to their temporal and spatial resolution, adequate ground comparison is lacking. In this paper, we analyze how NDVI products from MODIS (Aqua and Terra) and PROBA-V predict vegetation phenology when compared with near-surface observations. We conduct this comparison at four tropical dry forests (TDFs) in the Americas. We undertake this study by comparing the following: (i) Dissimilarities of the standardized NDVI (NDVIS) using dynamic time warping, (ii) the differences of daily NDVIS between seasons and ENSO months using generalized linear models, and (iii) phenometrics derived from NDVI time series. Overall, our results suggest that NDVIS from satellite observations present DTW distances (dissimilarities) between 2.98 and 46.57 (18.91 ± 12.31) when compared with near-surface observations. Furthermore, NDVIS comparisons reveal that overall differences between satellite and near-surface observations are close to zero, but this tends to differ between seasons or when El Nino Southern Oscillation (ENSO) is present. Phenometrics comparisons show that metrics derived from satellite observations such as green-up, maturity, and start and end of the wet season strongly correlate with those from near-surface observations. In contrast, phenometrics that describe the day of the highest or lowest NDVI tend to be inconsistent with those from near-surface observations. All findings were observed independently of the NDVI source. Our results suggest that satellite-based NDVI products tend to be inconsistent descriptors of vegetation events on tropical deciduous forests in comparison with near-surface observations. These results reinforce the idea that satellite-based NDVI products should be used and interpreted with great caution and only in ecosystems with well-established knowledge of their vegetation phenology.
Effectiveness of the Newsletter "Treating Bipolar Depression in the Family Practice Setting from the Differential Diagnosis of Mood Disorders Series"

Background: Outcomes™, along with the University of Cincinnati and Avalon Clinical Communications, examined the effectiveness of a newsletter, Treating Bipolar Depression in the Family Practice Setting (issue 6 of the Differential Diagnosis of Mood Disorders newsletter series of 7 issues). The newsletter, designed to provide information to family physicians on the treatment of bipolar depression, was supported by an educational grant from AstraZeneca. The purpose of the study was to determine the efficacy of the enduring material (newsletter) as determined by long-term retention of information and increased confidence level of physicians participating in the study. Here we report those findings and also highlight the case-vignette method for determining long-term evidence-based learning. Methods: Effectiveness was measured by use of a survey composed of evidence-based case vignettes on which questions about physician confidence and barriers to the optimal screening and management of patients with bipolar depression were based. Surveys were administered to a group of participants (n = 85) and a similar group of nonparticipants (n = 56), allowing for assessment and reporting based on the following questions: How do practice approaches to the diagnosis and management of mood disorders by newsletter participants compare with those of nonparticipants? Does adherence to clinical practice guidelines and evidence differ between participants and nonparticipants? Were there differences in perceived barriers to the optimal diagnosis and management of mood disorders associated with newsletter participation? Was there a difference in the confidence of managing mood disorders between participants and nonparticipants? Effect size for this activity was calculated by using the publication’s targeted audience of family physicians. Results: The average education effectiveness score for the participants was 67%. The average nonparticipant score was 57%, and the average difference between scores was 10%. This average difference between the 2 groups of physicians equals an effect size of 0.58 on a scale of –3.0 through +3.0. Conclusions: When presented with case vignettes, participants were 35% more likely to make clinical decisions consistent with evidence-based recommendations covered by the program than a similar group of physicians who had not been exposed to the program.
AirCSI – Remotely Criminal Investigator

Summary: Once a location associated with a committed crime must be preserved, even before criminal experts start collecting and analyzing evidences, the crime scene should be recorded with minimal human interference. In this work, we introduce an autonomous system for investigation of crime scene using a drone. Our proposed intelligent system recognizes objects considered as important evidence of the crime scene, and defines the trajectories through which the drone performs a detailed search to record evidences of the scene. We used our own method, called Air-SSLAM, to estimate drone's pose, as well as proportional–integral–derivative (PID) controllers for aircraft stabilization, while flying through the paths defined by the environment recognition step. We evaluated the performance of our system in a simulator, also preparing a real-drone system to work in a real environment.
Accounting for Object Weight in Interaction Design for Virtual Reality

Interaction design for virtual reality (VR) rarely takes the weight of an object – let alone its moment of inertia –into account. This clearly reduces user immersion and could lead to a break-in-presence. In this work, we propose methods for providing a higher fidelity in interactions with virtual objects. Specifically, we present different methods for picking up, handling, swinging, and throwing objects based on their weight, size, and affordances. We conduct user studies in order to gauge the differences in performance as well as sense of presence of the proposed techniques compared to conventional interaction techniques. While these methods all rely on the use of unmodified VR controllers, we also investigate the difference between using controllers to simulate a baseball bat and swinging a real baseball bat. Interestingly, we find that realism of the motions during interaction is not necessarily an important concern for all users. Our modified interaction techniques, however, have the ability to push user performance towards the slower motions that we observe when a real bat is used instead of a VR controller on its own.
Deep Learning Based Period Order Detection in Fringe Projection

Fringe projection profilometry (FPP) is a popular optical 3-dimensional (3D) scanning method which can obtain an object’s 3D model at low cost, and achieve high resolution, fast speed and full-field measurements. Traditional FPP methods suffer from the ambiguity problem that only the wrapped phase information can be measured while the true phase information is required to obtain the 3D model of the scene. Various phase unwrapping methods were suggested to recover the wrapped phase in FPP methods. However, most of them will fail when the captured fringe images contain complex structures such as having discontinuities due to sudden jumps in object’s height profile. To solve this problem, we propose in this paper to embed the fringe pattern with a set of textural patterns to encode the period order of the true phase information. During the offline phase, a convolutional neural network (CNN) is trained to learn a set of filters that will be activated when they see the code patterns. When the encoded fringe image is captured, the modified morphological component analysis is first performed to extract the code pattern. It is then decoded by the trained CNN to estimate the Kmap, which contains the period order of the true phase information. Experimental results show that the proposed method can measure the 3D profile of objects with abrupt jumps in height profile, where the conventional phase unwrapping algorithms often fail to perform. It also has a much higher computational efficiency due to the effective utilization of GPU by CNN.
The analysis of novel gene targets in breast cancer in women ≤35 years.

Breast cancer presenting in women aged ≤ 35 years has a more aggressive behaviour, and thus poorer prognosis, which is thought to be due to differences in the tumour biology of these tumours compared to those from older women. The aim of this study was to examine the expression of 9 novel gene targets (A-kinase anchor protein-1 (AKAP1), Acidic protein rich in leucines (APRIL), CCAAT enhancer binding protein alpha (C/EBPα), Damage-specific DNA binding protein 2 (DDB2), Granulin, Nuclear Receptor Coactivator 3 (NCOA3), Retinoic acid receptor Responder 3 (RARRES3), Retinoblastoma binding protein 4 (RBBP4), and Transforming Growth Factor beta Induced (TGFβI) identified previously by a cDNA microarray in breast cancers and female controls by RT-qPCR, western blotting, and immunohistochemistry. Six breast cell lines, 9 samples of organoids from reduction mammoplasty tissues, and 35 tumour tissues (20 cases >35 years, 15 cases ≤35 years in age) were analysed for the expression of the nine target genes using real time quantitative RT-PCR. Of the nine target genes investigated, five showed differences between normal and cancers ≤35, or between breast cancers ≤35 and those >35 years. NCOA3 and RARRES3 showed elevated levels of mRNA in breast cancers ≤35 years compared to those >35 years (p= 0.001 and p=0.002 respectively). Compared to the normal breast, TGFβI showed a reduced level of mRNA expression in both younger and older cases (p= 0.026 and p=0.001 respectively), while DDB2 and C-EBPα showed a reduced level of mRNA expression in younger group only (p= 0.002 and p=0.001 respectively). NCOA3 protein expression examination using western blotting found high levels in the ER+ve cell lines MCF-7, ZR-75-1 and T47-D with a weak expression in ER−ve cell lines HBL-100 and MDA-MB-468. RARRES3 protein expression was found in 4 breast cell lines (HBL-100, MDA-MB-468, MCF-7, and ZR-75-1). IHC found expression of NCOA3 in younger and older tumours including ER+ve and ER−ve cases. This study identifies NCAO3 and RARRES3 as potential markers for breast cancers in younger women, but the data need confirmation in a larger series of cases.