Identifying Patch Correctness in Test-Based Program Repair

Test-based automatic program repair has attracted a lot of attention in recent years. However, the test suites in practice are often too weak to guarantee correctness and existing approaches often generate a large number of incorrect patches. To reduce the number of incorrect patches generated, we propose a novel approach that heuristically determines the correctness of the generated patches. The core idea is to exploit the behavior similarity of test case executions. The passing tests on original and patched programs are likely to behave similarly while the failing tests on original and patched programs are likely to behave differently. Also, if two tests exhibit similar runtime behavior, the two tests are likely to have the same test results. Based on these observations, we generate new test inputs to enhance the test suites and use their behavior similarity to determine patch correctness. Our approach is evaluated on a dataset consisting of 139 patches generated from existing program repair systems including jGenProg, Nopol, jKali, ACS and HDRepair. Our approach successfully prevented 56.3\% of the incorrect patches to be generated, without blocking any correct patches.Comment: ICSE 201

A scientometric analysis and review of fall from height research in construction

Fall from height (FFH) in the construction industry has earned much attention among researchers in recent years. The present review-based study introduced a science mapping approach to evaluate the FFH studies related to the construction industry. This study, through an extensive bibliometric and scientometric assessment, recognized the most active journals, keywords and the nations in the field of FFH studies since 2000. Analysis of the authors’ keywords revealed the emerging research topics in the FFH research community. Recent studies have been discovered to pay more attention to the application of Computer and Information Technology (CIT) tools, particularly building information modelling (BIM) in research related to FFH. Other emerging research areas in the domain of FFH include rule checking, and prevention through design. The findings summarized the mainstream research areas (e.g., safety management program), discussed existing research gaps in FFH domain (e.g., the adaptability of safety management system), and suggests future directions in FFH research. The recommended future directions could contribute to improving safety for the FFH research community by evaluating existing fall prevention programs in different contexts; integrating multiple CIT tools in the entire project lifecycle; designing fall safety courses to workers associated with temporary agents and prototype safety knowledge tool development. The current study was restricted to the FFH literature sample included the journal articles published only in English and in Scopus

Automated Morphological Classification of SDSS Red Sequence Galaxies

(abridged) In the last decade, the advent of enormous galaxy surveys has motivated the development of automated morphological classification schemes to deal with large data volumes. Existing automated schemes can successfully distinguish between early and late type galaxies and identify merger candidates, but are inadequate for studying detailed morphologies of red sequence galaxies. To fill this need, we present a new automated classification scheme that focuses on making finer distinctions between early types roughly corresponding to Hubble types E, S0, and Sa. We visually classify a sample of 984 non-starforming SDSS galaxies with apparent sizes >14". We then develop an automated method to closely reproduce the visual classifications, which both provides a check on the visual results and makes it possible to extend morphological analysis to much larger samples. We visually classify the galaxies into three bulge classes (BC) by the shape of the light profile in the outer regions: discs have sharp edges and bulges do not, while some galaxies are intermediate. We separately identify galaxies with features: spiral arms, bars, clumps, rings, and dust. We find general agreement between BC and the bulge fraction B/T measured by the galaxy modeling package GIM2D, but many visual discs have B/T>0.5. Three additional automated parameters -- smoothness, axis ratio, and concentration -- can identify many of these high-B/T discs to yield automated classifications that agree ~70% with the visual classifications (>90% within one BC). Both methods are used to study the bulge vs. disc frequency as a function of four measures of galaxy 'size': luminosity, stellar mass, velocity dispersion, and radius. All size indicators show a fall in disc fraction and a rise in bulge fraction among larger galaxies.Comment: 24 pages, 20 figures, MNRAS accepte

Long-term effects of automated mechanical peripheral stimulation on gait patterns of patients with Parkinson's disease

New treatments based on peripheral stimulation of the sensory–motor system have been inspiring new rehabilitation approaches in Parkinson’s disease (PD), especially to reduce gait impairment, levodopa washout effects, and the incidence of falls. The aim of this study was to evaluate the change in gait and the clinical status of PD patients after six sessions of a treatment based on automated mechanical peripheral stimulation (AMPS). Eighteen patients with PD and 15 age-matched healthy individuals (control group) participated in this study. A dedicated medical device delivered the AMPS. PD patients were treated with AMPS six times once every 4 days. All PD patients were treated in the off-levodopa phase and were evaluated with gait analysis before and after the first intervention (acute phase), after the sixth intervention, 48 h after the sixth intervention, and 10 days after the end of the treatment. To compare the differences among the AMPS interventions (pre, 6 AMPS, and 10 days) in terms of clinical scales, a t-test was used (α≤0.05). In addition, to compare the differences among the AMPS interventions (pre, post, 6 AMPS, 48 h and 10 days), the gait spatiotemporal parameters were analyzed using the Friedman test and the Bonferroni post-hoc test (α≤0.05). Also, for comparisons between the PD group and the control group, the gait spatiotemporal parameters were analyzed using the Mann–Whitney test and the Bonferroni post-hoc test (α≤0.05). The results of the study indicate that the AMPS treatment has a positive effect on bradykinesia because it improves walking velocity, has a positive effect on the step and stride length, and has a positive effect on walking stability, measured by the increase in stride length. These results are consistent with the improvements measured with clinical scales. These findings indicate that AMPS treatment seems to generate a more stable walking pattern in PD patients, reducing the well-known gait impairment that is typical of PD; regular repetition every 4 days of AMPS treatment appears to be able to improve gait parameters, to restore rhythmicity, and to reduce the risk of falls, with benefits maintained up to 10 days after the last treatment. The trial was registered online at ClinicalTrials.gov (number identifier: NCT0181528)

Wearable Fall Detector Using Recurrent Neural Networks

Falls have become a relevant public health issue due to their high prevalence and negative effects in elderly people. Wearable fall detector devices allow the implementation of continuous and ubiquitous monitoring systems. The effectiveness for analyzing temporal signals with low energy consumption is one of the most relevant characteristics of these devices. Recurrent neural networks (RNNs) have demonstrated a great accuracy in some problems that require analyzing sequential inputs. However, getting appropriate response times in low power microcontrollers remains a difficult task due to their limited hardware resources. This work shows a feasibility study about using RNN-based deep learning models to detect both falls and falls’ risks in real time using accelerometer signals. The effectiveness of four different architectures was analyzed using the SisFall dataset at different frequencies. The resulting models were integrated into two different embedded systems to analyze the execution times and changes in the model effectiveness. Finally, a study of power consumption was carried out. A sensitivity of 88.2% and a specificity of 96.4% was obtained. The simplest models reached inference times lower than 34 ms, which implies the capability to detect fall events in real-time with high energy efficiency. This suggests that RNN models provide an effective method that can be implemented in low power microcontrollers for the creation of autonomous wearable fall detection systems in real-time

Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations

Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions

Jefferson Digital Commons quarterly report: January-March 2020

This quarterly report includes: New Look for the Jefferson Digital Commons Articles COVID-19 Working Papers Educational Materials From the Archives Grand Rounds and Lectures JeffMD Scholarly Inquiry Abstracts Journals and Newsletters Master of Public Health Capstones Oral Histories Posters and Conference Presentations What People are Saying About the Jefferson the Digital Common