A SURVEY OF STUDENTS’ ABILITY OF IDENTIFYING ERRORS IN WRONG SOLUTIONS FOR THE MATHEMATICAL PROBLEMS RELATED TO THE MONOTONICITY OF FUNCTIONS

The monotonicity of a function plays an important role in the general mathematics curriculum in Vietnam, because it is considered as an effective tool for solving mathematical problems involved with the monotonic intervals of functions, their extreme, absolute maximum value and absolute minimum value. Normally, students commit errors in solving these problems because of their complexity and difficulty. In addition, specific characteristics of knowledge also make children make mistakes. The sample consisted of 362 students, and they had the task of identifying errors in false assumptions. From the results of the survey, it was found that when dealing with the monotonicity of the functions, students were still misled.  Article visualizations

A comprehensive study on the efficacy of a wearable sleep aid device featuring closed-loop real-time acoustic stimulation

Difficulty falling asleep is one of the typical insomnia symptoms. However, intervention therapies available nowadays, ranging from pharmaceutical to hi-tech tailored solutions, remain ineffective due to their lack of precise real-time sleep tracking, in-time feedback on the therapies, and an ability to keep people asleep during the night. This paper aims to enhance the efficacy of such an intervention by proposing a novel sleep aid system that can sense multiple physiological signals continuously and simultaneously control auditory stimulation to evoke appropriate brain responses for fast sleep promotion. The system, a lightweight, comfortable, and user-friendly headband, employs a comprehensive set of algorithms and dedicated own-designed audio stimuli. Compared to the gold-standard device in 883 sleep studies on 377 subjects, the proposed system achieves (1) a strong correlation (0.89 ± 0.03) between the physiological signals acquired by ours and those from the gold-standard PSG, (2) an 87.8% agreement on automatic sleep scoring with the consensus scored by sleep technicians, and (3) a successful non-pharmacological real-time stimulation to shorten the duration of sleep falling by 24.1 min. Conclusively, our solution exceeds existing ones in promoting fast falling asleep, tracking sleep state accurately, and achieving high social acceptance through a reliable large-scale evaluation

Soluble trace metals associated with atmospheric fine particulate matter in the two most populous cities in Vietnam

Hanoi and Ho Chi Minh City (HCM), the most populous cities in Vietnam, have received increasing global attention because of their poor air pollution status. As part of the recent UK-Vietnam 2-Cities project, the concentrations of trace metals in fine particulate matter have been characterized. 24-hour samples of PM2 were collected at 2 sites in Hanoi and 3 sites in HCM during two 4-week periods in September/October 2018 and March 2019. The soluble fraction of 15 trace metal(oid)s (Fe, Al, Mn, Ti, Zn, V, Cu, Ni, Co, Cd, Pb, Th, Cr, As, and Sb) bound to PM2 were analyzed by ICP-MS. The results show that Zn was the most abundant soluble metal in PM2 in both cities, with very large numbers of road vehicles (e.g. tyre wear) likely contributing in both cities and non-ferrous metal production being a substantial additional source in Hanoi. Fe and Al, derived from crustal sources, were the dominant metals after Zn. Most trace metals concentrations in Hanoi were higher than in HCM, especially toxic metals such as Pb, Cd, Cr and As. V and Ni were the only two metals having higher concentrations in HCM than in Hanoi, likely due to shipping emissions (combustion of heavy fuel oil) that strongly affect the air quality in HCM. Coal-power plants and non-ferrous metal production are likely to be the major sources of trace metals in Hanoi. Health risk assessment shows that a high carcinogenic risk exists for inhalation exposure of soluble trace metals bound to PM2 in both cities

iFixR: bug report driven program repair

Issue tracking systems are commonly used in modern software development for collecting feedback from users and developers. An ultimate automation target of software maintenance is then the systematization of patch generation for user-reported bugs. Although this ambition is aligned with the momentum of automated program repair, the literature has, so far, mostly focused on generate-and- validate setups where fault localization and patch generation are driven by a well-defined test suite. On the one hand, however, the common (yet strong) assumption on the existence of relevant test cases does not hold in practice for most development settings: many bugs are reported without the available test suite being able to reveal them. On the other hand, for many projects, the number of bug reports generally outstrips the resources available to triage them. Towards increasing the adoption of patch generation tools by practitioners, we investigate a new repair pipeline, iFixR, driven by bug reports: (1) bug reports are fed to an IR-based fault localizer; (2) patches are generated from fix patterns and validated via regression testing; (3) a prioritized list of generated patches is proposed to developers. We evaluate iFixR on the Defects4J dataset, which we enriched (i.e., faults are linked to bug reports) and carefully-reorganized (i.e., the timeline of test-cases is naturally split). iFixR generates genuine/plausible patches for 21/44 Defects4J faults with its IR-based fault localizer. iFixR accurately places a genuine/plausible patch among its top-5 recommendation for 8/13 of these faults (without using future test cases in generation-and-validation)

The Three Pillars of Machine Programming

In this position paper, we describe our vision of the future of machine programming through a categorical examination of three pillars of research. Those pillars are:(i) intention,(ii) invention, and (iii) adaptation. Intention emphasizes advancements in the human-to-computer and computer-to-machine-learning interfaces. Invention emphasizes the creation or refinement of algorithms or core hardware and software building blocks through machine learning (ML). Adaptation emphasizes advances in the use of ML-based constructs to autonomously evolve software

Gain-Scheduled Filtering for Time-Varying Discrete Systems

Abstract—This paper deals with the design of gain-scheduled filters, whose state-space realization depends on real-time parameters of plants. Similar to well-recognized advantages of gain-scheduled controllers in control theory, gain-scheduled filters are expected to provide enhanced performance in comparison with customary nonadjustable filters. Our construction technique is based on nonlinear fractional transformation (NFT) representations of systems that are a generalization of widely used linear fractional transformation (LFT) representations. Both generalized 2 and discrete-time filter design problems are investigated together with their extension to mixed designs. This study leads to new linear matrix inequality (LMI) formulations, which in turn provide an effective and reliable design tool. The proposed design technique is finally evaluated in the light of simulation examples. Index Terms—Linear fractional transformation (LFT), linear matrix inequality (LMI), nonlinear fractional transformation (NFT). I

SemFix: Program repair via semantic analysis

10.1109/ICSE.2013.6606623Proceedings - International Conference on Software Engineering772-781PCSE