Miracle-dz/UniFAR-FTUs huggingface dataset:
Analyzing Requirements and Traceability Information to Improve Bug Localization

Locating bugs in industry-size software systems is time consuming and challenging. An automated approach for assisting the process of tracing from bug descriptions to relevant source code benefits developers. A large body of previous work aims to address this problem and demonstrates considerable achievements. Most existing approaches focus on the key challenge of improving techniques based on textual similarity to identify relevant files. However, there exists a lexical gap between the natural language used to formulate bug reports and the formal source code and its comments. To bridge this gap, state-of-the-art approaches contain a component for analyzing bug history information to increase retrieval performance. In this paper, we propose a novel approach TraceScore that also utilizes projects' requirements information and explicit dependency trace links to further close the gap in order to relate a new bug report to defective source code files. Our evaluation on more than 13,000 bug reports shows, that TraceScore significantly outperforms two state-of-the-art methods. Further, by integrating TraceScore into an existing bug localization algorithm, we found that TraceScore significantly improves retrieval performance by 49% in terms of mean average precision (MAP).
A Cognitive Sub-Nyquist MIMO Radar Prototype

We present a cognitive prototype that demonstrates a colocated, frequency-division-multiplexed, multiple-input multiple-output (MIMO) radar, which implements both temporal and spatial sub-Nyquist sampling. The signal is sampled and recovered via the Xampling framework. Cognition is due to the fact that the transmitter adapts its signal spectrum by emitting only those subbands that the receiver samples and processes. Real-time experiments demonstrate sub-Nyquist MIMO recovery of target scenes with 87.5% spatio-temporal bandwidth reduction and signal-to-noise-ratio of <inline-formula><tex-math notation="LaTeX">$-$</tex-math></inline-formula>10 dB.
Multi-label Learning with Highly Incomplete Data via Collaborative Embedding

Tremendous efforts have been dedicated to improving the effectiveness of multi-label learning with incomplete label assignments. Most of the current techniques assume that the input features of data instances are complete. Nevertheless, the co-occurrence of highly incomplete features and weak label assignments is a challenging and widely perceived issue in real-world multi-label learning applications due to a number of practical reasons including incomplete data collection, moderate labels from annotators, etc. Existing multi-label learning algorithms are not directly applicable when the observed features are highly incomplete. In this work, we attack this problem by proposing a weakly supervised multi-label learning approach, based on the idea of collaborative embedding. This approach provides a flexible framework to conduct efficient multi-label classification at both transductive and inductive mode by coupling the process of reconstructing missing features and weak label assignments in a joint optimisation framework. It is designed to collaboratively recover feature and label information, and extract the predictive association between the feature profile and the multi-label tag of the same data instance. Substantial experiments on public benchmark datasets and real security event data validate that our proposed method can provide distinctively more accurate transductive and inductive classification than other state-of-the-art algorithms.
DeepMatching: A Structural Seed Identification Framework for Social Network Alignment

Network alignment aims at finding a bijective mapping between nodes of two networks. Due to its wide application in various fields (e.g., Computer Vision, Data Management, Bioinformatics, and Privacy Protection), researchers have proposed many network alignment algorithms, most of which rely on a set of pre-mapped seeds. However, it is challenging to identify an initial credible set of seeds solely with structural information. In this paper, by exploiting the observation that a true mapping leads to a large portion of consistent edges among the mapped nodes, we formally define the credibility of a mapping as its deviation from a random one. This enables us to measure the credibility of an initial set of seeds. We also present DeepMatching which is a seed identification framework for social network alignment. First, we represent the nodes of the two mapping networks with their structural feature vectors by employing graph embedding techniques. Second, we obtain an initial mapping of the nodes based on the obtained vectors by leveraging point set registration methods. Third, we develop a heuristic algorithm to extract a credible set of seed from the initial mapping. Finally, we utilize the extracted seed set as input of an efficient propagation-based algorithm for large scale network alignment. We conduct extensive experiments to evaluate the performance of DeepMatching, and the results clearly demonstrate its effectiveness and the efficiency.
Do Developers Update Third-Party Libraries in Mobile Apps?

One of the most common strategies to develop new software is to take advantage of existing source code, which is available in comprehensive packages called third-party libraries. As for all software systems, even these libraries change to offer new functionalities and fix bugs or security issues. The way the changes are propagated has been studied by researchers, interested in understanding their impact on the non-functional attributes of the systems source code. While the research community mainly focused on the change propagation phenomenon in the context of traditional applications, only little is known regarding the mobile context. In this paper, we aim at bridging this gap by conducting an empirical study on the evolution history of 291 mobile apps, by investigating (i) whether mobile developers actually update third-party libraries, (ii) which are the categories of libraries with respect to the developers' proneness to update their apps, (iii) what are the common patterns followed by developers when updating a software library, and (iv) whether high-and low-rated apps present peculiar update patterns. The results of the study showed that mobile developers rarely update their apps with respect to the used libraries, and when they do, they mainly tend to update the libraries related to the Graphical User Interface, with the aim of keeping the mobile apps updated with the latest design tendencies. In some cases developers ignore updates because of a poor awareness of the benefits, or a too high cost/benefit ratio. Finally, high-and low-rated apps present strong differences.
Failure to Meet Nurse Staffing Standards: A Litigation Case Study of a Large US Nursing Home Chain

Large for-profit nursing home chains in the United States have generally reported low nurse staffing levels. This historical case study examined a class action litigation case regarding staffing levels, resident rights, and quality outcomes in 12 Arkansas nursing homes owned by a large for-profit chain. The questions were as follows: (1) How did the residents’ care needs compare with actual nurse staffing levels? (2) How did the staffing levels compare with federal and state nurse staffing requirements and professional staffing standards? (3) Did the facilities comply with state and federal residents’ rights and quality of care requirements? The findings showed staffing levels marginally above state minimum standards, staffing shortages that violated state standards, staffing levels not adjusted for resident acuity, and shortages that resulted in omitted care. Staffing levels were lower than needed according to nursing directors, lower than average facilities in the state, and lower than professional standards. The findings showed many resident grievances regarding basic care and residents’ rights, clinical measures of poor quality, and state deficiencies. A large settlement was agreed on to compensate the residents. The case shows that chain’s management, as well as the regulatory system, failed to ensure adequate staffing levels that took into account regulatory requirements and professional standards and resulted in violations of residents’ rights, health, safety, and well-being.
Metformin Treatment Inhibits Motility and Invasion of Glioblastoma Cancer Cells

Glioblastoma multiforme (GBM) is one of the most common and deadliest cancers of the central nervous system (CNS). GBMs high ability to infiltrate healthy brain tissues makes it difficult to remove surgically and account for its fatal outcomes. To improve the chances of survival, it is critical to screen for GBM-targeted anticancer agents with anti-invasive and antimigratory potential. Metformin, a commonly used drug for the treatment of diabetes, has recently emerged as a promising anticancer molecule. This prompted us, to investigate the anticancer potential of metformin against GBMs, specifically its effects on cell motility and invasion. The results show a significant decrease in the survival of SF268 cancer cells in response to treatment with metformin. Furthermore, metformin's efficiency in inhibiting 2D cell motility and cell invasion in addition to increasing cellular adhesion was also demonstrated in SF268 and U87 cells. Finally, AKT inactivation by downregulation of the phosphorylation level upon metformin treatment was also evidenced. In conclusion, this study provides insights into the anti-invasive antimetastatic potential of metformin as well as its underlying mechanism of action.
DILOF: Effective and Memory Efficient Local Outlier Detection in Data Streams

With precipitously growing demand to detect outliers in data streams, many studies have been conducted aiming to develop extensions of well-known outlier detection algorithm called Local Outlier Factor (LOF), for data streams. However, existing LOF-based algorithms for data streams still suffer from two inherent limitations: 1) Large amount of memory space is required. 2) A long sequence of outliers is not detected. In this paper, we propose a new outlier detection algorithm for data streams, called DILOF that effectively overcomes the limitations. To this end, we first develop a novel density-based sampling algorithm to summarize past data and then propose a new strategy for detecting a sequence of outliers. It is worth noting that our proposing algorithms do not require any prior knowledge or assumptions on data distribution. Moreover, we accelerate the execution time of DILOF about 15 times by developing a powerful distance approximation technique. Our comprehensive experiments on real-world datasets demonstrate that DILOF significantly outperforms the state-of-the-art competitors in terms of accuracy and execution time. The source code for the proposed algorithm is available at our website: http://di.postech. ac.kr/DILOF.
Integrating platelet and coagulation activation in fibrin clot formation

Platelets interact with the coagulation system in a multitude of ways, not only during the phases of thrombus formation, but also in specific areas within a formed thrombus. This review discusses current concepts of platelet control of thrombin generation, fibrin formation and structure, and anticoagulation. Indicated are how combined signalling via the platelet receptors for collagen (glycoprotein VI) and thrombin induces the secretion of (anti)coagulation factors, as well as surface exposure of phosphatidylserine, thereby catalysing thrombin generation. This procoagulant platelet response is also facilitated by the adhesive complexes glycoprotein Ib‐V‐IX and integrin αIIbβ3. In the buildup of a platelet‐fibrin thrombus, the extrinsic, tissue factor–driven coagulation pathway is predominant in early stages, while the intrinsic, factor XII pathway seems to promote at later time points. Already early generation of thrombin enforces platelet responses and stimulates intra‐thrombus heterogeneity with patches of loosely aggregated, contracted, and phosphatidylserine‐exposing platelets. Fibrin actively formed on the surface of activated platelets supports thrombus growth, but also captures thrombin. The fibrin distribution in a thrombus appears to rely on the local procoagulant trigger and the blood flow rate. Clinical studies support the importance of the platelet‐coagulation interplay, by showing beneficial effects of combination therapy in the secondary prevention of cardiovascular disease.
PEA: Parallel Evolutionary Algorithm by Separating Convergence and Diversity for Large-Scale Multi-Objective Optimization

Running evolutionary algorithms in parallel is an intuitive way to speed up the process of solving large-scale multi-objective optimization problems, which have hundreds or thousands of decision variables. However, the framework of the existing multi-objective evolutionary algorithms seriously limits their parallelization. During each iteration, the environmental selection operators present in the existing framework need to collect and compare all the candidate solutions to balance the convergence and diversity, thus dividing the whole evolutionary process into a series of dependent sub-processes and resulting in frequent data transmission. To address this issue, we propose a novel parallel framework that separates the environmental selection operator from the entire evolutionary process, evidently removing the dependencies among sub-processes and reducing the data transmission. On the basis of the parallel framework, a new parallel evolutionary algorithm, namely PEA, is designed. In PEA, the convergence is achieved by a series of independent sub-populations, and the diversity is merely emphasized at the converged solutions from each subpopulation, which is helpful for avoiding that the environmental selection operator limits the parallelization of the algorithm. Moreover, a new environmental selection strategy is proposed to improve the diversity without considering the convergence. To assess the performance of the proposed PEA, we compare it with five representative multi-objective evolutionary algorithms in terms of both the convergence and diversity. The performance of the parallel framework is also analyzed by comparing with two existing parallel models. The experimental results demonstrate the superiority of the proposed parallel algorithms in terms of the convergence, diversity, and speedup.
Analysis and Design of Ultra-Wideband mm-Wave Injection-Locked Frequency Dividers Using Transformer-Based High-Order Resonators

A transformer-based high-order resonator is proposed to improve the locking range (LR) of the millimeter-wave injection-locked frequency dividers (ILFDs). The LR limitations on ILFDs are discussed, and the operating principles of the proposed high-order resonator are analyzed based on their flattened phase response. The inductive gain peaking technique and the tail current source requirement are further analyzed for low power considerations. Two chips are fabricated in a 65-nm CMOS process to implement the proposed techniques: the first one measures an LR of 62.9% from 27.9 to 53.5 GHz while consuming 5.8 mW from a 1-V power supply and the second chip achieves an LR of 62.7% from 32.4 to 61.9 GHz while consuming only 1.2 mW from a 0.42-V power supply. The best figure of merit can achieve up to 24.7 GHz/mW.
Time-Scale 2D Representation of EEG Long-Term Records as a Convenient Instrument for Big Data Analysis in Neurology and Psychiatry

The report is devoted to computer analysis of long-term EEG records. The visual review of such big data requires a lot of time and is not always unbiased. That is why the search for efficient instruments of long-term EEG records processing is a task of utmost importance for neurologic analysis. To solve this problem, we developed a convenient instrument that expands a one-dimensional EEG signal into a two-dimensional representation. In contrast to the well-known time-frequency representations we choose for EEG-like signals another representation domain - the time-scale one. This is done because fragments of interest in the EEG signal - seizures, coma signals, alpha-spindles and others often represent a few repeating waveforms, whose spectra are obscure. The most effective here is the correlative-type analysis, a special form of which - the multi-scale correlative analysis is put forward as the basis for a novel representation. The paper discusses in detail the multi–scale correlation algorithm of the application developed and shows how the suggested functionality of the instrument is provided by the algorithmic features. The performance of the instrument is illustrated by the results of processing rats long–term EEG, in particular, before and after traumatic brain injury.
A Double Sensing Scheme With Selective Bitline Voltage Regulation for Ultralow-Voltage Timing Speculative SRAM

A double sensing with selective bitline voltage regulation (DS-SBVR) scheme is proposed to improve the throughput of ultralow-voltage static random access memory (SRAM). It senses the bitline voltage swing twice and compares two samples for confirmation. The bitline voltage is dynamically regulated by charge sharing between two sensing steps. Different from other timing speculative SRAMs, its error flag is generated much earlier; therefore, it achieves a higher reading throughput. Meanwhile, a digitized timing scheme is proposed to generate configurable timing pulses for the DS-SBVR. Compared with other timing techniques, it has a better ability to process, voltage, temperature (PVT) tracking and variance suppression. For fair comparison of performance/power/area, three different column-based timing speculative designs are implemented in the same technology. A 28-nm test chip including 40 SRAM macros (128 <inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> 32) is fabricated to demonstrate the scheme. Compared with the conventional design, measurements show that DS-SBVR achieves 1.45<inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> throughput gain at 0.6-V SS corner. The figure of merit (FOM) is introduced for power, performance, and area (PPA) gain comparison. Compared with the conventional design, the FOMs of PPA gain are 1.54 and 2.33 in 128-row and 512-row memories, respectively. Compared with other timing speculative SRAMs, it achieves 1.83<inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula>–2.24<inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> improvement.
RE-STORM: Mapping the Decision-Making Problem and Non-functional Requirements Trade-Off to Partially Observable Markov Decision Processes

Different model-based techniques have been used to model and underpin requirements management and decision-making strategies under uncertainty for self-adaptive Systems (SASs). The models specify how the partial or total fulfillment of non-functional requirements (NFRs) drive the decision-making process at runtime. There has been considerable progress in this research area. How-ever, precarious progress has been made by the use of models at runtime using machine learning to deal with uncertainty and support decision-making based on new evidence learned during execution. New techniques are needed to systematically revise the current model and the satisficement of its NFRs when empirical evidence becomes available from the monitoring infrastructure. In this paper, we frame the decision-making problem and trade-off specifications of NFRs in terms of Partially Observable Markov Decision Processes (POMDPs) models. The mathematical probabilistic framework based on the concept of POMDPs serves as a runtime model that can be updated with new learned evidence to support reasoning about partial satisficement of NFRs and their trade-o under the new changes in the environment. In doing so, we demonstrate how our novel approach RE-STORM underpins reasoning over uncertainty and dynamic changes during the system's execution.
Learning to Estimate the Travel Time

Vehicle travel time estimation or estimated time of arrival (ETA) is one of the most important location-based services (LBS). It is becoming increasingly important and has been widely used as a basic service in navigation systems and intelligent transportation systems. This paper presents a novel machine learning solution to predict the vehicle travel time based on floating-car data. First, we formulate ETA as a pure spatial-temporal regression problem based on a large set of effective features. Second, we adapt different existing machine learning models to solve the regression problem. Furthermore, we propose a Wide-Deep-Recurrent (WDR) learning model to accurately predict the travel time along a given route at a given departure time. We then jointly train wide linear models, deep neural networks and recurrent neural networks together to take full advantages of all three models. We evaluate our solution offline with millions of historical vehicle travel data. We also deploy the proposed solution on Didi Chuxing's platform, which services billions of ETA requests and benefits millions of customers per day. Our extensive evaluations show that our proposed deep learning algorithm significantly outperforms the state-of-the-art learning algorithms, as well as the solutions provided by leading industry LBS providers.
Exploring the Use of Automated API Migrating Techniques in Practice: An Experience Report on Android

In recent years, open source software libraries have allowed developers to build robust applications by consuming freely available application program interfaces (API). However, when these APIs evolve, consumers are left with the difficult task of migration. Studies on API migration often assume that software documentation lacks explicit information for migration guidance and is impractical for API consumers. Past research has shown that it is possible to present migration suggestions based on historical code-change information. On the other hand, research approaches with optimistic views of documentation have also observed positive results. Yet, the assumptions made by prior approaches have not been evaluated on large scale practical systems, leading to a need to affirm their validity. This paper reports our recent practical experience migrating the use of Android APIs in FDroid apps when leveraging approaches based on documentation and historical code changes. Our experiences suggest that migration through historical code-changes presents various challenges and that API documentation is undervalued. In particular, the majority of migrations from removed or deprecated Android APIs to newly added APIs can be suggested by a simple keyword search in the documentation. More importantly, during our practice, we experienced that the challenges of API migration lie beyond migration suggestions, in aspects such as coping with parameter type changes in new API. Future research may aim to design automated approaches to address the challenges that are documented in this experience report.