Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Value of Bug Reports for Retrieval-Based Bug Localization

Software engineering researchers have been applying tools and techniques from information retrieval (IR) to problems such as bug localization to lower the manual effort required to perform maintenance tasks. The central challenge when using an IR-based tool is the formation of a high-quality query. When performing bug localization, one easily accessible source of query words is the bug report. A recent paper investigated the sufficiency of this source by using a genetic algorithm (GA) to build high quality queries. Unfortunately, the GA in essence "cheats" as it makes use of query performance when evolving a good query. This raises the question, is it feasible to attain similar results without "cheating? " One approach to providing cheat-free queries is to employ automatic summarization. The performance of the resulting summaries calls into question the sufficiency of the bug reports as a source of query words. To better understand the situation, Information Need Analysis (INA) is applied to quantify both how well the GA is performing and, perhaps more importantly, how well a bug report captures the vocabulary needed to perform IR-based bug localization. The results find that summarization shows potential to produce high-quality queries, but it requires more training data. Furthermore, while bug reports provide a useful source of query words, they are rather limited and thus query expansion techniques, perhaps in combination with summarization, will likely produce higher-quality queries.
ADNet: A Deep Network for Detecting Adverts

Online video advertising gives content providers the ability to deliver compelling content, reach a growing audience, and generate additional revenue from online media. Recently, advertising strategies are designed to look for original advert(s) in a video frame, and replacing them with new adverts. These strategies, popularly known as product placement or embedded marketing, greatly help the marketing agencies to reach out to a wider audience. However, in the existing literature, such detection of candidate frames in a video sequence for the purpose of advert integration, is done manually. In this paper, we propose a deep-learning architecture called ADNet, that automatically detects the presence of advertisements in video frames. Our approach is the first of its kind that automatically detects the presence of adverts in a video frame, and achieves state-of-the-art results on a public dataset.
How to Find if a Single-Board Computer Loses Audio Samples in an Isochronous Data Transfer via USB

This work deals with the issue of loss of samples in a data acquisition process using an audio capturer device connected to a low cost and consumption single-board computer via USB interface. A question that may arise in this kind of hardware is if it can acquire data from the device avoiding an overrunning buffer. A method for finding lost samples is presented that is based on the acquisition of a linear frequency modulation signal and the analisys of its discrete Fourier Transform. An example illustrates the effectiveness of the proposed method.
Analyzing Image Segmentation for Connectomics

Automatic image segmentation is critical to scale up electron microscope (EM) connectome reconstruction. To this end, segmentation competitions, such as CREMI and SNEMI, exist to help researchers evaluate segmentation algorithms with the goal of improving them. Because generating ground truth is time-consuming, these competitions often fail to capture the challenges in segmenting larger datasets required in connectomics. More generally, the common metrics for EM image segmentation do not emphasize impact on downstream analysis and are often not very useful for isolating problem areas in the segmentation. For example, they do not capture connectivity information and often over-rate the quality of a segmentation as we demonstrate later. To address these issues, we introduce a novel strategy to enable evaluation of segmentation at large scales both in a supervised setting, where ground truth is available, or an unsupervised setting. To achieve this, we first introduce new metrics more closely aligned with the use of segmentation in downstream analysis and reconstruction. In particular, these include synapse connectivity and completeness metrics that provide both meaningful and intuitive interpretations of segmentation quality as it relates to the preservation of neuron connectivity. Also, we propose measures of segmentation correctness and completeness with respect to the percentage of “orphan” fragments and the concentrations of self-loops formed by segmentation failures, which are helpful in analysis and can be computed without ground truth. The introduction of new metrics intended to be used for practical applications involving large datasets necessitates a scalable software ecosystem, which is a critical contribution of this paper. To this end, we introduce a scalable, flexible software framework that enables integration of several different metrics and provides mechanisms to evaluate and debug differences between segmentations. We also introduce visualization software to help users to consume the various metrics collected. We evaluate our framework on two relatively large public groundtruth datasets providing novel insights on example segmentations.
Replicating Natural Approaching Behavior of Humans for Improving Robot's Approach toward Two Persons During a Conversation

Service robots have been developed to enhance the living standard of people. Users of these service robots are not experts in robotic domain. Hence, they prefer to have human-friendly features in these service robots. These service robots often need to navigate toward their users when accomplishing service tasks demanded by the users. Therefore, the ability of a service robot to approach a user in a human-friendly manner would increase the rapport between the user and the robot. This paper proposes a method for human-friendly approaching of a service robot toward a user who is having a conversation with another person. The algorithm behind the proposed approaching method has been designed in such a way that the robot can replicate the natural human tendencies to a greater extent. Natural approaching behavior of humans identified from a previously conducted human study has been utilized for this purpose. The proposed approaching method has been implemented on MIRob platform and a user study has been conducted to validate the capabilities of the proposed concept. The experimental results validate that the proposed approaching method of the robot is capable of maintaining the satisfaction of the users during approaches.
Key Concept Identification: A Sentence Parse Tree-Based Technique for Candidate Feature Extraction From Unstructured Texts

The effectiveness of automatic key concept or keyphrase identification from unstructured text documents mainly depends on a comprehensive and meaningful list of candidate features extracted from the documents. However, the conventional techniques for candidate feature extraction limit the performance of keyphrase identification algorithms and need improvement. The objective of this paper is to propose a novel parse tree-based approach for candidate feature extraction to overcome the shortcomings of the existing techniques. Our proposed technique is based on generating a parse tree for each sentence in the input text. Sentence parse trees are then cut into sub-trees to extract branches for candidate phrases (i.e., noun, verb, and so on). The sub-trees are combined using parts-of-speech tagging to generate the flat list of candidate phrases. Finally, filtering is performed using heuristic rules and redundant phrases are eliminated to generate final list of candidate features. Experimental analysis is conducted for validation of the proposed scheme using three manually annotated and publicly available data sets from different domains, i.e., Inspec, 500N-KPCrowed, and SemEval-2010. The proposed technique is fine-tuned to determine the optimal value for the parameter context window size and then it is compared with the existing conventional n-gram and noun-phrase-based techniques. The results show that the proposed technique outperforms the existing approaches and significant improvements of 13.51% and 30.67%, 12.86% and 5.48%, and 13.16% and 31.46% are achieved, in terms of precision, recall, and F-measure when compared with noun-phrase-based scheme and n-gram-based scheme, respectively. These results give us confidence to further validate the proposed technique by developing a keyphrase extraction algorithm in the future.
Patellar fractures are associated with an increased risk of total knee arthroplasty: A MATCHED COHORT STUDY OF 6096 PATELLAR FRACTURES WITH A MEAN FOLLOW‐UP OF 14.3 YEARS

Aims The aim of this study was to investigate the incidence of knee arthroplasty and arthroscopy following patellar fractures, and to compare this with an age‐ and gender‐matched group without a prior patellar fracture. Patients and Methods A national matched cohort study based on the Danish National Patient Register including all citizens of Denmark (approximately 5.3 million) was undertaken. A total of 6096 patients who sustained a patellar fracture in Denmark between 1 January 1996 and 31 December 2000 were included. The median age of these patients was 50.6 years (interquartile range (IQR) 28.5 to 68.9); 49.1% were women. Patients were followed‐up until 31 December 2015, with regard to treatment with knee arthroplasty and/or knee arthroscopy. Results Patients with a patellar fracture had an increased risk of knee arthroplasty (hazard ratio (HR) 1.83, 95% confidence interval (CI) 1.57 to 2.13) compared with citizens without a patellar fracture, and the effect was strongest during the first five years (HR 3.02, 95% CI 2.26 to 4.03). Patients with a patellar fracture also had a higher risk of knee arthroscopy (HR 3.94, 95% CI 3.49 to 4.46), and the effect was highest during the first five years after the fracture (HR 7.40, 95% CI 6.32 to 8.66). Conclusion Patellar fractures are associated with an increased risk of knee arthroplasty and knee arthroscopy. The consequences of a patellar fracture may be more severe than previously considered, and patients must expect a lifelong increased risk of knee arthroplasty. Cite this article: Bone Joint J 2018;100‐B:1477‐81.
Center Difference Set Membership Filter by Zonotopes for Nonlinear System

The paper proposes an improved approach to state estimation for nonlinear discrete-time systems based on zonotopes. To overcome the inherent defect of Taylors formula, a lower-order multi-dimensional extension of Stirling's interpolation formula is used to realize the linearization of nonlinear models. A nonlinear programming method is used to optimize the guaranteed margin of linearization error to obtain a more compact bound estimation, thereby reducing the conservativeness of the algorithm. Simulation results have shown the effectiveness and improved performance of the proposed algorithm.
Association of High-Sensitivity Troponin T With Left Ventricular Dysfunction in Ankylosing Spondylitis

In this study also from Turkey, the association between increased levels of high-sensitivity troponin, a marker of myocardial injury, and left ventricular dysfunction was found in 40 patients with ankylosing spondylitis when compared to a control group. Thus, high-sensitivity troponin could be a valuable marker of heart dysfunction in these patients. Background Ankylosing spondylitis (AS) is a systemic inflammatory disease, and cardiac dysfunction has not been clearly described clinically. High-sensitivity cardiac troponin T (hs-cTnT) is a noninvasive marker for subclinical myocardial injury. Objective In this study, we aimed to investigate any relationship between hs-cTnT and left ventricular (LV) function evaluated via tissue Doppler imaging in AS patients with no known cardiac risk factor. Methods Our study used a cross-sectional case protocol design and was conducted between January 2016 and June 2016. In total, 40 AS patients (17 females and 23 males) were age and sex matched with healthy volunteers (20 females and 20 males) and enlisted for this study. Detailed transthoracic echocardiography was performed, and tissue Doppler imaging was used to assess systolic and diastolic functions. High-sensitivity cardiac troponin T levels were measured and compared between 2 groups. Results Compared with control subjects, AS patients had lower early (Em)/late (Am) diastolic myocardial velocities, mitral annular plane systolic excursion, and end-diastolic distance from the mitral annulus to the LV apex. Conversely, they had greater systolic myocardial velocity (Sm), isovolumetric relaxation time, and displacement index (p < 0.001, for all). Higher hs-cTnT levels were measured in AS patients (0.45 ± 0.22 vs. 1.11 ± 0.27, p < 0.001), and multivariate logistic regression analyses revealed that hs-cTnT was an independent predictor of LV diastolic dysfunction in AS patients. Conclusions These data show that AS patients had impaired LV functions and increased hs-cTnT levels. Tissue Doppler imaging may be a useful tool for detection of early functional LV abnormalities, and hs-cTnT may be valuable biomarker of diastolic LV dysfunction in AS patients.
Road Damage Detection And Classification In Smartphone Captured Images Using Mask R-CNN

This paper summarizes the design, experiments and results of our solution to the Road Damage Detection and Classification Challenge held as part of the 2018 IEEE International Conference On Big Data Cup. Automatic detection and classification of damage in roads is an essential problem for multiple applications like maintenance and autonomous driving. We demonstrate that convolutional neural net based instance detection and classfication approaches can be used to solve this problem. In particular we show that Mask-RCNN, one of the state-of-the-art algorithms for object detection, localization and instance segmentation of natural images, can be used to perform this task in a fast manner with effective results. We achieve a mean F1 score of 0.528 at an IoU of 50% on the task of detection and classification of different types of damages in real-world road images acquired using a smartphone camera and our average inference time for each image is 0.105 seconds on an NVIDIA GeForce 1080Ti graphic card. The code and saved models for our approach can be found here : this https URL Submission
A Stock Trading Intention Recognition Model Based on Data Clustering

In practice, many physics principles have been employed to derive various models of stock transaction behavior analysis. However, few studies have been done on the stock trading intention recognition based on feature selection and match on time series stock transaction data. The purpose of this paper is to show how to discover the real stock trading intention by analyzing historical transaction data. Therefore, the proposed recognition model utilizes data clustering with feature selection, trace constructing and pattern matching together to fulfill intention mining and recognizing. The experiment results show the better performance.
Classifier Result Aggregation for Automatically Grading Histopathological Images

The large amount of histopathological images that are produced in hospitals worldwide often request an overwhelming effort from the human pathology experts. In this respect, large efforts are made by scientists in various disciplines and especially in computer science to develop automatic procedures to distinguish between different grades of cancer based on the histopathological slides. The current work employs seven different machine learning approaches to classify the microscopical images from a real-world medical data set. Additionally, a combination of the results of the seven classifiers that gives a higher weight to a relatively new approach leads to classification results that are significantly better than those of the best performing algorithm of the considered collection.
24-GHz Self-Injection-Locked Vital-Sign Radar Sensor With CMOS Injection-Locked Frequency Divider Based on Push–Push Oscillator Topology

In this letter, a 24-GHz CMOS injection-locked frequency divider (ILFD) based on the push–push oscillator topology is designed and fabricated. It is further treated as the kernel component of a self-injection-locked (SIL) radar sensor for noncontact vital-sign detection. The push–push topology is employed to double the oscillation frequency of the 12-GHz LC oscillator to provide the 24-GHz transmitting signal of the radar sensor, while the direct injection-locked configuration is used to divide the receiving 24-GHz injection signal by 2. Hence, the 12-GHz output of the ILFD can be connected with a differentiator-based envelope detector for vital-sign demodulation. By packaging the developed ILFD chip and connecting it with two antennas, a gain block, a differentiator-based envelope detector, and a 24-GHz SIL radar sensor can be built to detect the physiological chest movement of a human subject. The feasibility of developing a SIL radar sensor in the millimeter-wave range is also demonstrated.
Miniature MIMO Antennas for 5G Mobile Terminals

In this paper, we present the design and the measurement of a miniature MIMO dual-band (2.6/3.6 GHz) antenna array for future 5G handsets. The antenna element has a compact area with size of 10.5×14.5 mm2. The proposed antenna array elements are symmetrically placed along the long edges of the smartphone, and they are composed of four same antenna types. Two antennas of the same kind are then closely positioned on the same 120×50 mm2 Printed Circuit Board (PCB). The initial proposed handset-system is composed of U-Shape structure and a connected meandered monopole. Both simulated and measured results are shown to illustrate the MIMO capabilities of the proposed structures. These results show that the presented structure operate in dual-band at 2.5–2.7 GHz and 3.4–3.8 GHz, which has a bandwidth of 200 MHz and 400 MHz respectively for reflection coefficient less than −6dB. The port isolation is more than 11.5 dB across the whole band of interest without using any decoupling network for two antenna structures. The antenna is fabricated using FR4 substrate with relative permittivity of 4.4 and pert tangent of 0.02.
Hormone deficient mutants have distinct flavonoid proportion fingerprints in response to abiotic stress

ABSTRACT Distinct flavonoid profiles (a.k.a. ‘fingerprints’) are produced in the vegetative tissues of plants in response to different abiotic stresses, yet it remained unknown whether flavonoid levels or their relative their proportions are more tightly regulated in response to stress. Here we show that the relative proportions of 19 flavonoids were more stringently controlled compared to their levels in response to variety of abiotic stresses. We screened mutants that are deficient in the biosynthesis of the stress response hormones ABA, Eth, JA, and GA by growing them in an abiotic stress condition that induces the biosynthesis of a wide variety of flavonoids and found that mutants deficient in a particular hormone generally had a distinct flavonoid proportion fingerprint. Our results suggest that flavonoid proportion fingerprints of uncharacterized mutants could be used to predict gene involvement in particular hormone pathways that signal responses to abiotic stress.
An Analysis of Non-standard Bitcoin Transactions

In Bitcoin, the most common kind of transactions is in the form "Bob pays Alice", and it is based on the Pay to-Public Key Hash (P2PKH) script. P2PKH transactions are just one among many standard classes: a transaction is standard if it passes BitcoinCore's IsStandard() and IsStandardTx() tests. However, the creation of ad-hoc scripts to lock (and unlock) transactions allows for also generating non-standard transactions, which can be nevertheless broadcast and mined as well. In this work, we explore the Bitcoin block-chain with the purpose to analyse and classify standard and non-standard transactions, understanding how much the standard behaviour is respected.
Research on Real-time Video Action Classification Based on Three-Dimensional Convolutional Neural Network

This paper describes a model for performing action classification in real-time video streaming. This model can simultaneously analyze the spatio-temporal information of video under the constraint of low delay. In addition, in order to prevent the model from judging motionless segments in the video as motion, the model in this article is equipped with the ability of distinguish the segments of motion from the stationary ones. The experimental results show that the model can complete the action classification task with little delay, which ensures that the classification result can be output in real time with the constant input of the video image.
The Use of Codes with Fixed Multiplicites of Detected Unidirectional and Asymmetrical Errors in the Process of Organizing Combinational Circuit Testing

The methods of protective coding are widely applied in the process of organizing combinational circuit testing, including classical Berger codes and their modifications which are generally used for the solution of the given task. Modular codes with summation of unit and one weighted bits were introduced in the given article. The number of methods for building modified codes with summation was established for the preset value of the number of data bits. The class of du, da-UAED(m,k)-codes was introduced for consideration, where du and dα - are the smallest multiplicities of undetected unidirectional and asymmetrical errors. The conditions were established under which new modified codes with summation would detect any unidirectional errors, any unidirectional and asymmetrical errors up to the preset values of du and dα. The key characteristics of the new codes were given, which, if taken into account, may prove useful in the process of building capable discrete devices. It was shown, that, in practice, the use of new codes instead of classical Berger codes makes it possible to reduce overhead charges on the implementation of check circuits, in case of detection ability improvement of the obtained system.
Fractional-Order Darwinian Swarm Intelligence Inspired Multilevel Thresholding for Mammogram Segmentation

Multilevel thresholding is more accurate than the classical thresholding method for segmenting a digital mammogram, since it uses more number of intensities to represent the objects. It is intuitively appreciable for further detection of breast cancer using such kind of clinical database. The main goal of any multilevel thresholding based segmentation is to optimize its objective function to obtain different threshold levels; but multilevel thresholding is computationally expensive, and sometimes these optimized values are not accurate. Therefore, in this paper, an amalgamation of n-level thresholding and fractional-order Darwinian particle swarm optimization (FODPSO) is studied in detail, and was found to be the best among various PSO variants based thresholding for efficient mammogram image segmentation. The efficiency of the proposed technique is compared with other segmentation technique, based on thresholding such as particle swarm optimization (PSO) and Darwinian particle swarm optimization (DPSO). Individual performances of hereby employed algorithms are compared and analysed using the performance measures such as PSNR, SNR, SSIM, and MSE.