Miracle-dz/UniFAR-FTUs huggingface dataset:
Better Technical Debt Detection via SURVEYing

Software analytics can be improved by surveying; i.e. rechecking and (possibly) revising the labels offered by prior analysis. Surveying is a time-consuming task and effective surveyors must carefully manage their time. Specifically, they must balance the cost of further surveying against the additional benefits of that extra effort. This paper proposes SURVEY0, an incremental Logistic Regression estimation method that implements cost/benefit analysis. Some classifier is used to rank the as-yet-unvisited examples according to how interesting they might be. Humans then review the most interesting examples, after which their feedback is used to update an estimator for estimating how many examples are remaining. This paper evaluates SURVEY0 in the context of self-admitted technical debt. As software project mature, they can accumulate "technical debt" i.e. developer decisions which are sub-optimal and decrease the overall quality of the code. Such decisions are often commented on by programmers in the code; i.e. it is self-admitted technical debt (SATD). Recent results show that text classifiers can automatically detect such debt. We find that we can significantly outperform prior results by SURVEYing the data. Specifically, for ten open-source JAVA projects, we can find 83% of the technical debt via SURVEY0 using just 16% of the comments (and if higher levels of recall are required, SURVEY0can adjust towards that with some additional effort).
A Two-stage Classification Method for High-dimensional Data and Point Clouds

High-dimensional data classification is a fundamental task in machine learning and imaging science. In this paper, we propose a two-stage multiphase semi-supervised classification method for classifying high-dimensional data and unstructured point clouds. To begin with, a fuzzy classification method such as the standard support vector machine is used to generate a warm initialization. We then apply a two-stage approach named SaT (smoothing and thresholding) to improve the classification. In the first stage, an unconstraint convex variational model is implemented to purify and smooth the initialization, followed by the second stage which is to project the smoothed partition obtained at stage one to a binary partition. These two stages can be repeated, with the latest result as a new initialization, to keep improving the classification quality. We show that the convex model of the smoothing stage has a unique solution and can be solved by a specifically designed primal-dual algorithm whose convergence is guaranteed. We test our method and compare it with the state-of-the-art methods on several benchmark data sets. The experimental results demonstrate clearly that our method is superior in both the classification accuracy and computation speed for high-dimensional data and point clouds.
Slow-roll inflation in scalar-tensor models

The linear and quadratic perturbations for a scalar-tensor model with non-minimal coupling to curvature, coupling to the Gauss-Bonnet invariant and non-minimal kinetic coupling to the Einstein tensor are developed. The quadratic action for the scalar and tensor perturbations is constructed and the power spectra for the primordial scalar and tensor fluctuations are given. A consistency relation that is useful to discriminate the model from the standard inflation with canonical scalar field was found. For some power-law potentials it is shown that the Introduction of additional interactions, given by non-minimal, kinetic and Gauss-Bonnet couplings, can lower the tensor-to-scalar ratio to values that are consistent with latest observational constraints, and the problem of large fields in chaotic inflation can be avoided.
RASNet: Segmentation for Tracking Surgical Instruments in Surgical Videos Using Refined Attention Segmentation Network

Segmentation for tracking surgical instruments plays an important role in robot-assisted surgery. Segmentation of surgical instruments contributes to capturing accurate spatial information for tracking. In this paper, a novel network, Refined Attention Segmentation Network, is proposed to simultaneously segment surgical instruments and identify their categories. The U-shape network which is popular in segmentation is used. Different from previous work, an attention module is adopted to help the network focus on key regions, which can improve the segmentation accuracy. To solve the class imbalance problem, the weighted sum of the cross entropy loss and the logarithm of the Jaccard index is used as loss function. Furthermore, transfer learning is adopted in our network. The encoder is pre-trained on ImageNet. The dataset from the MICCAI EndoVis Challenge 2017 is used to evaluate our network. Based on this dataset, our network achieves state-of-the-art performance 94.65% mean Dice and 90.33% mean IOU.
SvTPM: A Secure and Efficient vTPM in the Cloud

Virtual Trusted Platform Modules (vTPMs) have been widely used in commercial cloud platforms (e.g. Google Cloud, VMware Cloud, and Microsoft Azure) to provide virtual root-of-trust for virtual machines. Unfortunately, current state-of-the-art vTPM implementations are suffering from confidential data leakage and high performance overhead. In this paper, we present SvTPM, a secure and efficient software-based vTPM implementation based on hardware-rooted Trusted Execution Environment (TEE), providing a whole life cycle protection of vTPMs in the cloud. SvTPM offers strong isolation protection, so that cloud tenants or even cloud administrators cannot get vTPM's private keys or any other sensitive data. In SvTPM, we identify and solve a couple of critical security challenges for vTPM protection with SGX, such as NVRAM replacement attack, rollback attacks, trust establishment, and a fine-grained trusted clock. We implement a prototype of SvTPM on both QEMU and KVM. Performance evaluation results show that SvTPM achieves orders of magnitude of performance gains comparing to the vTPMs protected with physical TPM. The launch time of SvTPM is 2600$\times$ faster than vTPMs built upon hardware TPM. In the micro-benchmarks evaluation, we find that the command execution latency of SvTPM is smaller than or equal to the existing schemes.
Towards analysing the effects of node mobility on the accuracy of Wi-Fi triangulation

The increased popularity of wireless networks has enabled the development of Location Based Service (LBS) techniques that rely on wireless signal strength. These techniques are being used extensively in military, healthcare, industrial and other environments in Wireless Sensor Networks (WSNs) and Mobile Ad hoc Networks (MANETs). In some networks, it is important to keep track of units/nodes that are mobile. However, the overall accuracy of Wi-Fi triangulation in any network is affected by mobile nodes. Such effects are dependent on link failure, the relative speed of the node(s), signal strength, node density and the mobility pattern of the node(s). This article analyzes the effects of node mobility on Wi-Fi triangulation. Furthermore, recommendations are provided for developing methods to mitigate their negative effects on the accuracy of Wi-Fi triangulation. Our goal is to develop more accurate Wi-Fi triangulation techniques that are not affected by node mobility.
ARF: identifying re-delegation vulnerabilities in Android system services

Over the past decade, the security of the Android platform has undergone significant scrutiny by both academic and industrial researchers. This scrutiny has been largely directed towards third-party applications and a few critical system interfaces, leaving much of Android's middleware unstudied. Building upon recent efforts to more rigorously analyze authorization logic in Android's system services, we revisit the problem of permission re-delegation, but in the context of system service entry points. In this paper, we propose the Android Re-delegation Finder (ARF) analysis framework for helping security analysts identify permission re-delegation vulnerabilities within Android's system services. ARF analyzes an interconnected graph of entry points in system services, deriving calling dependencies, annotating permission checks, and identifying potentially vulnerable deputies that improperly expose information or functionality to third-party applications. We apply ARF to Android AOSP version 8.1. 0 and find that it refines the set of 15,483 paths between entry points down to a manageable set of 490 paths. Upon manual inspection, we found that 170 paths improperly exposed information or functionality, consisting of 86 vulnerable deputies. Through this effort, we demonstrate the need for continued investigation of automated tools to analyze the authorization logic within the Android middleware.
Predicting Personality Using Facebook Status Based on Semi-supervised Learning

Personality analysis on social media is a research hotspot due to the importance of personality research in psychology as well as the rapid development of social media. Many studies have used social media status to analyze user's personality, but most of them are conducted on inadequate label data and linguistic features. In this paper, to explore the usage of unlabeled data on personality analysis, a personality analysis framework based on semi-supervised learning is introduced. Besides, for making full use of the language information in social media status, the well-known n-gram model is adopted to extract linguistic features. The experimental results demonstrate the semi-supervised learning can take advantage of unlabeled data and improve the accuracy of prediction model.
Infuencee Oriented Topic Prediction: Investigating the Effect of Influence on the Author

In this paper, we study the problem of topic adoption prediction for an author within a social academic network. The previous efforts on the problem use topic similarity and topic adoption of co-authors. We model the problem with an influence detection point of view, and propose that the influence on the author is an important factor. Hence, we define a novel influencee prediction based feature. To this aim, in this work, an algorithm is proposed to calculate the influence propagated towards the author. The effect of this feature is explored together with and in comparison to other features used in the literature for the problem. The experiments conducted on Arnet Miner data set show that accumulated influence on author is effective for predicting topic adoption.
A Variational Approach on Level sets and Linear Convergence of Variable Bregman Proximal Gradient Method for Nonconvex Optimization Problems

We develop a new variational approach on level sets aiming towards convergence rate analysis of a variable Bregman proximal gradient (VBPG) method for a broad class of nonsmooth and nonconvex optimization problems. With this new approach, we are able to extend the concepts of Bregman proximal mapping and their corresponding Bregman proximal envelops, Bregman proximal gap function to nonconvex setting. Properties of these mappings and functions are examined. An aim of this work is to provide a solid foundation on which further design and analysis of VBPG for more general nonconvex optimization problems are possible. Another aim is to provide a unified theory on linear convergence of VBPG with a particular interest towards proximal gradient methods. Centrol to our analysis for achieving the above goals is an error bound in terms of level sets and subdifferentials (level-set subdifferential error bound) along with its links to other level-set error bounds. As a consequence, we have established a number of positive results. These newly established results not only enable us to show that any accumulation of the sequence generated by VBPG is at least a critical point of the limiting subdifferential or even a critical point of the proximal subdifferential with a fixed Bregman function in each iteration, but also provide a fresh perspective that allows us to explore inner-connections among many known sufficient conditions for linear convergence of various first-order methods. Along the way, we are able to derive a number of verifiable conditions for level-set error bounds to hold, obtain linear convergence of VBPG, and derive necessary conditions and sufficient conditions for linear convergence relative to a level set for nonsmooth and nonconvex optimization problems.
Deep learning to generate in silico chemical property libraries and candidate molecules for small molecule identification in complex samples.

Comprehensive and unambiguous identification of small molecules in complex samples will revolutionize our understanding of the role of metabolites in biological systems. Existing and emerging technologies have enabled measurement of chemical properties of molecules in complex mixtures and, in concert, are sensitive enough to resolve even stereoisomers. Despite these experimental advances, small molecule identification is inhibited by (i) chemical reference libraries (e.g., mass spectra, collision cross section, and other measurable property libraries) representing <1% of known molecules, limiting the number of possible identifications, and (ii) the lack of a method to generate candidate matches directly from experimental features (i.e., without a library). To this end, we developed a variational autoencoder (VAE) to learn a continuous numerical, or latent, representation of molecular structure to expand reference libraries for small molecule identification. We extended the VAE to include a chemical property decoder, trained as a multitask network, in order to shape the latent representation such that it assembles according to desired chemical properties. The approach is unique in its application to metabolomics and small molecule identification, with its focus on properties that can be obtained from experimental measurements (m/z, CCS) paired with its training paradigm, which involved a cascade of transfer learning iterations. First, molecular representation is learned from a large dataset of structures with m/z labels. Next, in silico property values are used to continue training, as experimental property data is limited. Finally, the network is further refined by being trained with the experimental data. This allows the network to learn as much as possible at each stage, enabling success with progressively smaller datasets without overfitting. Once trained, the network can be used to predict chemical properties directly from structure, as well as generate candidate structures with desired chemical properties. Our approach is orders of magnitude faster than first-principles simulation for CCS property prediction. Additionally, the ability to generate novel molecules along manifolds, defined by chemical property analogues, positions DarkChem as highly useful in a number of application areas, including metabolomics and small molecule identification, drug discovery and design, chemical forensics, and beyond.
CODIT: Code Editing with Tree-Based NeuralMachine Translation

The way developers edit day-to-day code tends to be repetitive, often using existing code elements. Many researchers have tried to automate repetitive code changes by learning from specific change templates which are applied to limited scope. The advancement of Neural Machine Translation (NMT) and the availability of vast open-source evolutionary data opens up the possibility of automatically learning those templates from the wild. However, unlike natural languages, for which NMT techniques were originally devised, source code and its changes have certain properties. For instance, compared to natural language, source code vocabulary can be significantly larger. Further, good changes in code do not break its syntactic structure. Thus, deploying state-of-the-art NMT models without adapting the methods to the source code domain yields sub-optimal results. To this end, we propose a novel Tree based NMT system to model source code changes and learn code change patterns from the wild. We realize our model with a change suggestion engine: CODIT and train the model with more than 30k real-world changes and evaluate it on 6k patches. Our evaluation shows the effectiveness of CODIT in learning and suggesting patches.CODIT also shows promise generating bug fix patches.
Representation of numerical magnitude in math-anxious individuals

Larger distance effects in high math-anxious individuals (HMA) performing comparison tasks have previously been interpreted as indicating less precise magnitude representation in this population. A recent study by Dietrich, Huber, Moeller, and Klein limited the effects of math anxiety to symbolic comparison, in which they found larger distance effects for HMA, despite equivalent size effects. However, the question of whether distance effects in symbolic comparison reflect the properties of the magnitude representation or decisional processes is currently under debate. This study was designed to further explore the relation between math anxiety and magnitude representation through three different tasks. HMA and low math-anxious individuals (LMA) performed a non-symbolic comparison, in which no group differences were found. Furthermore, we did not replicate previous findings in an Arabic digit comparison, in which HMA individuals showed equivalent distance effects to their LMA peers. Lastly, there were no group differences in a counting Stroop task. Altogether, an explanation of math anxiety differences in terms of less precise magnitude representation is not supported.
Relapse prevention by means of paroxetine in ECT‐treated patients with major depression: a comparison with imipramine and placebo in medium‐term continuation therapy

In‐patients with severe major depression were treated in the acute phase with electroconvulsive therapy (ECT) in combination with antidepressants. The drug treatment consisted of two randomized trials which were both extended into the post‐ECT continuation phase. Patients with electrocardiological impairment were randomized to either 30 mg paroxetine daily or placebo under blind conditions. Patients without electrocardiological impairment were randomized to either 30 mg paroxetine daily or 150 mg imipramine daily. There was a high level of agreement between the Hamilton Depression Scale and the Melancholia Scale, demonstrating that the patients treated with ECT plus imipramine in the acute phase showed greater symptom reduction than those treated with ECT plus paroxetine. However, in the post‐ECT phase paroxetine was superior to both imipramine and placebo in preventing relapse. Thus in the post‐ECT phase 65% of the placebo‐treated patients relapsed, compared to 30% of the imipramine‐treated patients and 10% of the paroxetine‐treated patients. The psychometric analysis of the Melancholia Scale in the continuation or post‐ECT phase showed that relapsing patients displayed a pattern with lack of interests, impaired concentration, depressed mood and anxiety among the less severe symptoms (first‐compartment symptoms). In other words, these symptoms represent the gate to full‐blown depression (second‐compartment symptoms). Serotonin‐selective antidepressants such as paroxetine appear I to be more effective in controlling the first‐compartment symptoms.
Epidemiology-Based Assessment of Tumor Marker Overordering in Breast Cancer: An Algorithm to Examine Different Disease Conditions

Laboratory tests are frequently overused and have elevated inappropriateness rates. We previously developed a model to investigate the rate of utilization of tumor markers (TMs) in outpatients as an indirect indicator of inappropriateness. The model was based on the comparison between the number of actually ordered and expected tests, with the latter estimated on the basis of both epidemiological data and recommendations of available clinical practice guidelines. In this paper we propose an algorithm to distinguish prevalent cases without evidence of disease from those with metastatic spread, on the basis of both epidemiological and clinical information. The algorithm allows for a more precise prediction of the expected TM requests per year, to be compared with the actual number of requested TMs in order to assess possibly inappropriate overordering rates. Moreover, the implementation of the algorithm renders the epidemiologically based model more flexible to develop accurate indicators for appropriateness in the use of TMs in different stages of disease and for different clinical questions. A practical application with CA15.3 requests in breast cancer is presented.
Lost traffic encryption: fingerprinting LTE/4G traffic on layer two

Long Term Evolution (LTE) provides the communication infrastructure for both professional and private use cases and has become an integral part of our everyday life. Even though LTE/4G overcomes many security issues of previous standards, recent work demonstrates several attack vectors on the physical and network layers of the LTE stack. We do, however, have only limited insights into the security and privacy aspects of the second layer. In this work, we investigate the impact of fingerprinting attacks on encrypted LTE/4G layer-two traffic. Traffic fingerprinting enables an adversary to exploit the metadata side-channel of transmissions---with severe consequences for the user's privacy. In multiple lab and commercial network experiments, we demonstrate the feasibility of passive and active fingerprinting attacks. First, passive website fingerprinting allows the attacker to learn a user's accessed website from encrypted transmissions. While being a well-known attack in other contexts, we provide an extensive performance baseline of state-of-the-art website fingerprinting attacks of encrypted LTE traffic in a lab setup and successfully repeat the experiments in a commercial network. Second, in an active identity-mapping attack, we inject watermarks and localize users within a radio cell. Our attacks succeed for the current LTE/4G specification and exploit features that also persist in the upcoming 5G standard.
LODBookRec: Linked Open Data for Books Recommendation

In this paper, we present the LODBookRec application. LODBookRec builds on top of Linked Open Data (LOD) knowledge about literature domain and provides information retrieval GUI to conveniently present this knowledge to the end users. As such, LODBookRec aims to contribute towards better utilization of LOD and provide a suitable platform for on-line evaluation of information retrieval methods, especially recommender systems. LODBookRec contains a basic search GUI and several recommendation methods, with the primary focus on item-based recommendations. The results of offline evaluation indicates that content-based recommendations utilizing attribute-based similarity of books provides best item-based recommendations w.r. t. recommendation relevance for both highly popular books as well as long-tail books. However, modification of the original algorithm via maximal margin relevance increases diversity of the recommended lists with a modest relevance penalties.