This paper introduces a dual-task recognition system that uses Wi-Fi channel state information (CSI) for human Activity Recognition (AR) and Indoor Localization (IL), effectively addressing the limitations of single-task recognition approaches. The proposed system leverages complementary information derived from dual-stream signal characteristics by capitalizing on the relationship between the two tasks. Moreover, it incorporates a technique to mitigate sampling irregularities, effectively reducing data misrepresentation. Extensive experimental evaluations on two datasets substantiate the efficacy of the proposed method in managing dual-task recognition scenarios, consistently outperforming specialized single-task recognition solutions. These results highlight its potential as a versatile solution for Wi-Fi CSI-based recognition applications, including IoT monitoring, surveillance, and healthcare.

Proper naming of methods can make program code easier to understand, and thus enhance software maintainability. Yet, developers may use inconsistent names due to poor communication or a lack of familiarity with conventions within the software development lifecycle. To address this issue, much research effort has been invested into building automatic tools that can check for method name inconsistency and recommend consistent names. However, existing datasets generally do not provide precise details about why a method name was deemed improper and required to be changed. Such information can give useful hints on how to improve the recommendation of adequate method names. Accordingly, we construct a sample method-naming benchmark, ReName4J, by matching name changes with code reviews. We then present an empirical study on how state-of-the-art techniques perform in detecting or recommending consistent and inconsistent method names based on ReName4J. The main purpose of the study is to reveal a different perspective based on reviewed names rather than proposing a complete benchmark. We find that the existing techniques underperform on our review-driven benchmark, both in inconsistent checking and the recommendation. We further identify potential biases in the evaluation of existing techniques, which future research should consider thoroughly.

Mutation testing consists of generating test cases that detect faults injected into software (generating mutants) which its original test suite could not. By running such an augmented set of test cases, it may discover actual faults that may have gone unnoticed with the original test suite. It is thus a desired practice for embedded software running in safety-critical cyber-physical systems (CPS). Unfortunately, the state-of-the-art tool targeting C, a typical language for CPS software, relies on symbolic execution, whose limitations often prevent its application. MOTIF overcomes such limitations by leveraging grey-box fuzzing tools to generate unit test cases in C that detect injected faults in mutants. Indeed, fuzzing tools automatically generate inputs by exercising the compiled version of the software under test guided by coverage feedback, thus overcoming the limitations of symbolic execution. Our empirical assessment has shown that it detects more faults than symbolic execution (i.e., up to 47 percentage points), when the latter is applicable.

In this paper, we construct a network for human activity recognition based on the tokenized Wi-Fi signals on an attention mechanism. After standardizing the signals, the WiFi channel state information is utilized as a set of time-series data, acknowledging its inherent temporal structure. Motivated by the Transformer’s ability to model temporal dependencies, the construction is enriched with a frequency-based tokenization scheme. This unique construction is adept at managing noise and sensitivity intrinsic to Wi-Fi signals, effectively mitigating the challenges in Wi-Fi-based human activity recognition. Our experimental evaluations validated the effectiveness of the proposed structure.

In this paper, we propose a fusion network for human activity recognition based on the Wi-Fi Channel State Information (CSI) signals. The system employs a Bidirectional Long Short-Term Memory (BiLSTM) layer to extract action features from CSI data blocks and then trains them using the Kernel Ridge Regression (KRR). The trained block responses are subsequently fused based on the sum-rule to form the final decision. In contrast to deep learning, this process is computationally efficient because there is no need to train the BiLSTM. The proposed method has been tested on two publicly available databases to validate the accuracy performance.

Weakly hard real-time systems can, to some degree, tolerate deadline misses, but their schedulability still needs to be analyzed to ensure their quality of service. Such analysis usually occurs at early design stages to provide implementation guidelines to engineers so they can make better design decisions. Estimating worst-case execution times (WCET) is a key input to schedulability analysis. However, early on during system design, estimating WCET values is challenging, and engineers usually determine them as plausible ranges based on their domain knowledge. Our approach aims at finding restricted, safe WCET sub-ranges given a set of ranges initially estimated by experts in the context of weakly hard real-time systems. To this end, we leverage (1) multi-objective search aiming at maximizing the violation of weakly hard constraints to find worst-case scheduling scenarios and (2) polynomial logistic regression to infer safe WCET ranges with a probabilistic interpretation. We evaluated our approach by applying it to an industrial system in the satellite domain and several realistic synthetic systems. The results indicate that our approach significantly outperforms a baseline relying on random search without learning and estimates safe WCET ranges with a high degree of confidence in practical time (< 23 h).

Mutation testing can help reduce the risks of releasing faulty software. For such reason, it is a desired practice for the development of embedded software running in safety-critical cyber-physical systems (CPS). Unfortunately, state-of-the-art test data generation techniques for mutation testing of C and C++ software, two typical languages for CPS software, rely on symbolic execution, whose limitations often prevent its application (e.g., it cannot test black-box components). We propose a mutation testing approach that leverages fuzz testing, which has proved effective with C and C++ software. Fuzz testing automatically generates diverse test inputs that exercise program branches in a varied number of ways and, therefore, exercise statements in different program states, thus maximizing the likelihood of killing mutants, our objective. We performed an empirical assessment of our approach with software components used in satellite systems currently in orbit. Our empirical evaluation shows that mutation testing based on fuzz testing kills a significantly higher proportion of live mutants than symbolic execution (i.e., up to an additional 47 percentage points). Further, when symbolic execution cannot be applied, fuzz testing provides significant benefits (i.e., up to 41% mutants killed). Our study is the first one comparing fuzz testing and symbolic execution for mutation testing; our results provide guidance towards the development of fuzz testing tools dedicated to mutation testing.

Estimating worst-case execution time (WCET) is an important activity at early design stages of real-time systems. Based on WCET estimates, engineers make design and implementation decisions to ensure that task executions always complete before their specified deadlines. However, in practice, engineers often cannot provide precise point WCET estimates and prefer to provide plausible WCET ranges. Given a set of real-time tasks with such ranges, we provide an automated technique to determine for what WCET values the system is likely to meet its deadlines and, hence, operate safely with a probabilistic guarantee. Our approach combines a search algorithm for generating worst-case scheduling scenarios with polynomial logistic regression for inferring probabilistic safe WCET ranges. We evaluated our approach by applying it to three industrial systems from different domains and several synthetic systems. Our approach efficiently and accurately estimates probabilistic safe WCET ranges within which deadlines are likely to be satisfied with a high degree of confidence.

In this paper, we propose to extract the intersection points of the palmprint and the palm-vein lines from multi-spectral images and use them as reliable features for identity verification. Essentially, by utilizing a sum of cardinal directional image difference operation, the palmprint and palm-vein line features are respectively extracted from palm images of the Blue channel and the NIR channel of image spectrums based on simple matrix projection. Subsequently, the intersection locations of the two biometric line features are extracted and utilized to compute a set of keypoint descriptors. After calculating the match scores based on the extracted keypoint descriptors, a score level fusion of the matching results obtained from the Blue channel and the NIR channel is adopted to enhance the verification performance. The proposed method has been experimented on a public domain multispectral palm database where encouraging results in terms of verification accuracy have been obtained.

In real-time systems, priorities assigned to real-time tasks determine the order of task executions, by relying on an underlying task scheduling policy. Assigning optimal priority values to tasks is critical to allow the tasks to complete their executions while maximizing safety margins from their specified deadlines. This enables real-time systems to tolerate unexpected overheads in task executions and still meet their deadlines. In practice, priority assignments result from an interactive process between the development and testing teams. In this article, we propose an automated method that aims to identify the best possible priority assignments in real-time systems, accounting for multiple objectives regarding safety margins and engineering constraints. Our approach is based on a multi-objective, competitive coevolutionary algorithm mimicking the interactive priority assignment process between the development and testing teams. We evaluate our approach by applying it to six industrial systems from different domains and several synthetic systems. The results indicate that our approach significantly outperforms both our baselines, i.e., random search and sequential search, and solutions defined by practitioners. Our approach scales to complex industrial systems as an offline analysis method that attempts to find near-optimal solutions within acceptable time, i.e., less than 16 hours.

In recent years, the use of Information Retrieval (IR) techniques to automate the localization of buggy files, given a bug report, has shown promising results. The abundance of approaches in the literature, however, contrasts with the reality of IR-based bug localization (IRBL) adoption by developers (or even by the research community to complement other research approaches). Presumably, this situation is due to the lack of comprehensive evaluations for state-of-the-art approaches which offer insights into the actual performance of the techniques.

An orthogonal frequency division multiplexing (OFDM) has been considered as most promising technique for the next generation mobile communication and broadcasting system to realize high-speed data rate and robustness to fading channel. However, main drawback in OFDM system is the high peak-to-average power ratio (PAPR) when a number of independently modulated subcarriers are added up coherently, and amplified by the high power amplifier (HPA). This paper classifies and illustrates systematic code design method of the high PAPR codes. Look-up table (LUT) based PAPR reduction method is proposed to exclude the high PAPR codes.

This paper introduces a novel data synchronization method for ATSC distributed translator (DTxR) to improve the indoor reception performance of DTV (Digital TV). The proposed method extracts the frame sync information from the received RF signal and utilizes it for constructing the rebroadcast signal of DTxR. Computer Simulations are performed for the basic working algorithm of prototype system and some practical application issues are described.