Miracle-dz/UniFAR-FTUs huggingface dataset:
CodeGrid: A Grid Representation of Code

Code representation is a key step in the application of AI in software engineering. Generic NLP representations are effective but do not exploit all the rich structure inherent to code. Recent work has focused on extracting abstract syntax trees (AST) and integrating their structural information into code representations.These AST-enhanced representations advanced the state of the art and accelerated new applications of AI to software engineering. ASTs, however, neglect important aspects of code structure, notably control and data flow, leaving some potentially relevant code signal unexploited. For example, purely image-based representations perform nearly as well as AST-based representations, despite the fact that they must learn to even recognize tokens, let alone their semantics. This result, from prior work, is strong evidence that these new code representations can still be improved; it also raises the question of just what signal image-based approaches are exploiting. We answer this question. We show that code is spatial and exploit this fact to propose , a new representation that embeds tokens into a grid that preserves code layout. Unlike some of the existing state of the art, is agnostic to the downstream task: whether that task is generation or classification, can complement the learning algorithm with spatial signal. For example, we show that CNNs, which are inherently spatially-aware models, can exploit outputs to effectively tackle fundamental software engineering tasks, such as code classification, code clone detection and vulnerability detection. PixelCNN leverages ’s grid representations to achieve code completion. Through extensive experiments, we validate our spatial code hypothesis, quantifying model performance as we vary the degree to which the representation preserves the grid. To demonstrate its generality, we show that augments models, improving their performance on a range of tasks, On clone detection, improves ASTNN’s performance by 3.3% F1 score.
Rare Path Guided Fuzzing

Starting with a random initial seed, fuzzers search for inputs that trigger bugs or vulnerabilities. However, fuzzers often fail to generate inputs for program paths guarded by restrictive branch conditions. In this paper, we show that by first identifying rare-paths in programs (i.e., program paths with path constraints that are unlikely to be satisfied by random input generation), and then, generating inputs/seeds that trigger rare-paths, one can improve the coverage of fuzzing tools. In particular, we present techniques 1) that identify rare paths using quantitative symbolic analysis, and 2) generate inputs that can explore these rare paths using path-guided concolic execution. We provide these inputs as initial seed sets to three state of the art fuzzers. Our experimental evaluation on a set of programs shows that the fuzzers achieve better coverage with the rare-path based seed set compared to a random initial seed.
SmartState: Detecting State-Reverting Vulnerabilities in Smart Contracts via Fine-Grained State-Dependency Analysis

Smart contracts written in Solidity are widely used in different blockchain platforms such as Ethereum, TRON and BNB Chain. One of the unique designs in Solidity smart contracts is its statereverting mechanism for error handling and access control. Unfortunately, a number of recent security incidents showed that adversaries also utilize this mechanism to manipulate critical states of smart contracts, and hence, bring security consequences such as illegal profit-gain and Deny-of-Service (DoS). In this paper, we call such vulnerabilities as the State-reverting Vulnerability (SRV). Automatically identifying SRVs poses unique challenges, as it requires an in-depth analysis and understanding of the state-dependency relations in smart contracts. This paper presents SmartState, a new framework for detecting state-reverting vulnerability in Solidity smart contracts via finegrained state-dependency analysis. SmartState integrates a set of novel mechanisms to ensure its effectiveness. Particularly, Smart- State extracts state dependencies from both contract bytecode and historical transactions. Both of them are critical for inferring dependencies related to SRVs. Further, SmartState models the generic patterns of SRVs (i.e., profit-gain and DoS) as SRV indicators, and hence effectively identify SRVs based on the constructed statedependency graph. To evaluate SmartState, we manually annotated a ground-truth dataset which contains 91 SRVs in the real world. Evaluation results showed that SmartState achieves a precision of 87.23% and a recall of 89.13%. In addition, SmartState successfully identifies 406 new SRVs from 47,351 real-world smart contracts. 11 of these SRVs are from popular smart contracts with high transaction amounts (i.e., top 2000). In total, our reported SRVs affect a total amount of digital assets worth 428,600 USD.
What You See Is What You Get? It Is Not the Case! Detecting Misleading Icons for Mobile Applications

With the prevalence of smartphones, people nowadays can access a wide variety of services through diverse apps. A good Graphical User Interface (GUI) can make an app more appealing and competitive in app markets. Icon widgets, as an essential part of an app’s GUI, leverage icons to visually convey their functionalities to facilitate user interactions. Whereas, designing intuitive icon widgets can be a non-trivial job. Developers should follow a series of guidelines and make appropriate choices from a plethora of possibilities. Inappropriately designed or misused icons may cause user confusion, lead to wrong operations, and even result in security risks (e.g., revenue loss and privacy leakage). To investigate the problem, we manually checked 9,075 icons of 1,111 top-ranked commercial apps from Google Play and found 640 misleading icons in 312 ( 28%) of these apps. This shows that misleading icons are prevalent among real-world apps, even the top ones. Manually identifying misleading icons to improve app quality is time-consuming and laborious. In this work, we propose the first framework, IconSeer, to automatically detect misleading icons for mobile apps. Our basic idea is to find the discrepancies between the commonly perceived intentions of an icon and the actual functionality of the corresponding icon widget. IconSeer takes an Android app as input and reports potential misleading icons. It is powered by a comprehensive icon-intention mapping constructed by analyzing 268,353 icons collected from 15,571 popular Android apps in Google Play. The mapping includes 179 icon classes and 852 intention classes. Given an icon widget under analysis, IconSeer first employs a pre-trained open-set deep learning model to infer the possible icon class and the potential intentions. IconSeer then extracts developer-specified text properties of the icon widget, which indicate the widget’s actual functionality. Finally, IconSeer determines whether an icon is misleading by comparing the semantic similarity between the inferred intentions and the extracted text properties of the widget. We have evaluated IconSeer on the 1,111 Android apps with manually established ground truth. IconSeer successfully identified 1,172 inconsistencies (with an accuracy of 0.86), among which we further found 482 real misleading icons.
API2Vec: Learning Representations of API Sequences for Malware Detection

Analyzing malware based on API call sequence is an effective approach as the sequence reflects the dynamic execution behavior of malware.Recent advancements in deep learning have led to the application of these techniques for mining useful information from API call sequences. However, these methods mainly operate on raw sequences and may not effectively capture important information especially for multi-process malware, mainly due to the API call interleaving problem. Motivated by that, this paper presents API2Vec, a graph based API embedding method for malware detection. First, we build a graph model to represent the raw sequence. In particular, we design the temporal process graph (TPG) to model inter-process behavior and temporal API graph (TAG) to model intra-process behavior. With such graphs, we design a heuristic random walk algorithm to generate a number of paths that can capture the fine-grained malware behavior. By pre-training the paths using the Doc2Vec model, we are able to generate the embeddings of paths and APIs, which can further be used for malware detection. The experiments on a real malware dataset demonstrate that API2Vec outperforms the state-of-the-art embedding methods and detection methods for both accuracy and robustness, especially for multi-process malware.
CoopHance: Cooperative Enhancement for Robustness of Deep Learning Systems

Adversarial attacks have been a threat to Deep Learning (DL) systems to be reckoned with. By adding human-imperceptible perturbation to benign inputs, adversarial attacks can cause the incorrect behavior of DL systems. Considering the popularity of DL systems in the industry, it is critical and urgent for developers to enhance the robustness of DL systems against adversarial attacks. In this study, we propose a novel enhancement technique for DL systems, namely CoopHance. CoopHance leverages two specifically customized components, Regulator and Inspector, to cooperatively enhance the DL systems' robustness against adversarial examples with different distortions. Regulator can purify adversarial examples with low or moderate distortions, while Inspector is responsible for detecting these adversarial examples with high distortion by capturing the abnormal status of DL systems. Our evaluation using various attacks shows that, on average, CoopHance can successfully resist 90.62% and 96.56% of the adversarial examples that are generated for the unprotected systems on CIFAR-10 and SVHN datasets separately, which is 188.14% more effective than five state-of-the-art enhancement techniques, including Feature Squeeze, LID, SOAP, Adversarial Training, and MagNet. Meanwhile, when attackers generate new adversarial examples on the enhanced systems, CoopHance can reject 78.06% of attacks, which outperforms the best of five enhancement techniques by 82.71% on average.
Vectorizing Program Ingredients for Better JVM Testing

JVM testing is one of the most widely-used methodologies for guaranteeing the quality of JVMs. Among various JVM testing techniques, synthesis-based JVM testing, which constructs a test program by synthesizing various code snippets (also called program ingredients), has been demonstrated state-of-the-art. The existing synthesis-based JVM testing work puts more efforts in ensuring the validity of synthesized test programs, but ignores the influence of huge ingredient space, which largely limits the ingredient exploration efficiency as well as JVM testing performance. In this work, we propose Vectorized JVM Testing (called VECT) to further promote the performance of synthesis-based JVM testing. Its key insight is to reduce the huge ingredient space by clustering semantically similar ingredients via vectorizing ingredients using state-of-the-art code representation. To make VECT complete and more effective, based on vectorized ingredients, VECT further designs a feedback-driven ingredient selection strategy and an enhanced test oracle. We conducted an extensive study to evaluate VECT on three popular JVMs (i.e., HotSpot, OpenJ9, and Bisheng JDK) involving five OpenJDK versions. The results demonstrate VECT detects 115.03% ~ 776.92% more unique inconsistencies than the state-of-the-art JVM testing technique during the same testing time. In particular, VECT detects 26 previously unknown bugs for them, 15 of which have already been confirmed/fixed by developers.
BehAVExplor: Behavior Diversity Guided Testing for Autonomous Driving Systems

Testing Autonomous Driving Systems (ADSs) is a critical task for ensuring the reliability and safety of autonomous vehicles. Existing methods mainly focus on searching for safety violations while the diversity of the generated test cases is ignored, which may generate many redundant test cases and failures. Such redundant failures can reduce testing performance and increase failure analysis costs. In this paper, we present a novel behavior-guided fuzzing technique (BehAVExplor) to explore the different behaviors of the ego vehi- cle (i.e., the vehicle controlled by the ADS under test) and detect diverse violations. Specifically, we design an efficient unsupervised model, called BehaviorMiner, to characterize the behavior of the ego vehicle. BehaviorMiner extracts the temporal features from the given scenarios and performs a clustering-based abstraction to group behaviors with similar features into abstract states. A new test case will be added to the seed corpus if it triggers new behav- iors (e.g., cover new abstract states). Due to the potential conflict between the behavior diversity and the general violation feedback, we further propose an energy mechanism to guide the seed selec- tion and the mutation. The energy of a seed quantifies how good it is. We evaluated BehAVExplor on Apollo, an industrial-level ADS, and LGSVL simulation environment. Empirical evaluation results show that BehAVExplor can effectively find more diverse violations than the state-of-the-art.
Proteome profiling of nasopharynx reveals pathophysiological signature of COVID-19 disease severity

An aberrant innate immune system caused by the beta coronavirus SARS-CoV-2 is a characteristic manifestation of severe coronavirus disease 2019 (COVID-19). Here, we performed proteome profiling of nasopharyngeal (NP) swabs from 273 hospitalized patients with mild and severe COVID-19 symptoms, including non-survivors. We identified depletion in STAT1-mediated type I interferon response, retinol metabolism and NRF2 antioxidant system that are associated with disease severity in our patient demography. We found that the dysregulation of glucocorticoid signaling and renin-angiotensin-aldosterone system (RAAS) contribute to the pathophysiology of COVID-19 fatality. Hyperactivation of host innate immune system was observed in severe patients, marked by elevated proteins involved in neutrophil degranulation and platelet aggregation. Our study using high-throughput proteomics on the nasopharynx of COVID-19 patients provides additional evidence on the SARS-CoV-2-induced pathophysiological signatures of disease severity and fatality.
Real-time targeted illumination in widefield microscopy achieves confocal quality neuronal images

Widefield fluorescence imaging has significant challenges in visualizing neuronal fibers near cell bodies. Specifically, out-of-focus and scattered light from the bright cellbody often obscures nearby dim fibers and degrades their signal-to-background ratio. Scanning techniques can solve this problem but are limited by reduced imaging speed and increased cost. We greatly reduce stray light by modulating the illumination intensity to different structures. We use a digital micromirror device in the illumination channel of a common widefield microscope and use real-time image processing to pattern the illumination. With the setup, we illuminate bright cell bodies with minimal light intensity, and illuminate in focus fiber-like structures with high light intensity to reveal weak signals. Thus, we minimize the background and enhance the visibility of fibers in the final image. This targeted illumination significantly improves fiber contrast while maintaining a fast-imaging speed and low cost. Using a targeted illumination setup in a widefield microscope, we demonstrate confocal quality imaging of complex neurons in live C. elegans and zebrafish larva, as well as in in vitro mice brain slice.
Observer-based bumpless switching control for switched linear systems with sensor faults

This paper investigates the bumpless transfer problem of switched linear systems with sensor faults. The framework of the switched control systems includes two kinds of controllers, one is for normal use and the other is for fault-tolerant control when sensor faults have occurred. Considering that the information of the sensor faults is unknown to the controller, an observer is designed for the fault-tolerant controller. Moreover, in view of the issue that the output difference between the online and offline controllers may induce undesired oscillations in control input at the switching instant, a bumpless transfer compensator is presented based on sliding mode control. Due to the reasonably designed compensator, the difference can be minimized to a small value to guarantee smooth transitions of the control input when sensor faults have occurred. Finally, numerical simulations verify the effectiveness of the proposed method.
Evidence for multiscale multiplexed representation of visual features in EEG

Distinct neural processes are often encoded across distinct time scales of neural activations. However, it has remained unclear if this multiscale coding strategy is also implemented for separate features of the same process. One difficulty is that the conventional methods of time scale analysis provide imperfect estimations of time scales when several components are active during a single process. Developing a novel decoding-based time scale estimation method, we detected distinct time scales for simultaneously present features of visual stimuli in electroencephalography. We observed that orientation and colour of grating stimuli were encoded in shorter whereas the spatial frequency and contrast of those stimuli were encoded in longer time scales. The conventional autocorrelation-based estimation of time scale was unable to detect these distinguishable time scales. These results provide new evidence for a multiscale multiplexed neural code in the human visual system and introduces a flexible method for estimating neural time scales.
Hebbian learning of stimulus-response associations using transcranial magnetic stimulation

Classical conditioning states that the systematic co-occurrence of a neutral stimulus with an unconditioned stimulus can cause the neutral stimulus to, over time, evoke the same response as the unconditioned stimulus. On a neural level, Hebbian learning suggests that this type of learning occurs through changes in synaptic plasticity when two neurons are simultaneously active, resulting in increased connectivity between them. Inspired by learning theories, we here investigated whether the mere co-activation of visual stimuli and stimulation of the primary motor cortex using transcranial magnetic stimulation (TMS) would result in stimulus-response associations that can impact future behaviour. During a learning phase, we repeatedly paired the presentation of a specific colour (but not other colours) with a TMS pulse over the motor cortex. Next, participants performed a two-alternative forced choice task where they had to categorize simple shapes and we studied whether the shapes’ task-irrelevant colour (and its potentially associated involuntary motor activity) affected the required motor response. Participants showed more errors on incongruent trials for stimuli that were previously paired with high intensity TMS pulses, but only when tested on the same day. Using a drift diffusion model for conflict tasks, we further demonstrate that this interference occurred early, and gradually increased as a function of associated TMS intensity. Taken together, our findings show that the human brain can learn stimulus-response associations using externally induced motor cortex stimulation.
Multigene Sequence-Based and Phenotypic Characterization Reveals the Occurrence of a Novel Entomopathogenic Nematode Species, Steinernema anantnagense n. sp.

Abstract Three entomopathogenic nematode populations were isolated from agricultural fields in the Anantnag district of Jammu and Kashmir (India). Sequences of multiple gene regions and phenotypic features show that they are conspecific and represent a novel species. Molecular and morphological features provided evidence for placing the new species into the “Kushidai” clade. Within this clade, analysis of sequence data of the internal transcribed spacer (ITS) gene, the D2D3 region of the 28S rRNA gene, the mitochondrial cytochrome oxidase I (mtCOI) gene, and the mitochondrial 12S (mt12S) gene depicted the novel species as a distinctive entity closely related to Steinernema akhursti, S. kushidai, and S. populi. Phylogenetic analyses also show that the new species is a sister species to S. akhursti, and these two species are closely related to S. kushidai and S. populi. Additionally, the new species does not mate or produce fertile progeny with any of the closely related species, reinforcing its uniqueness from a biological species concept standpoint. The new species is further characterized by the third-stage infective juveniles with almost straight bodies (0.7–0.8 mm length), poorly developed stoma and pharynx, and conoid-elongate tail (49–66 µm) with hyaline posterior part. Adult females are characterized by short and conoid tails bearing a short mucron in the first generation and long conoid tails with thin mucron in the second generation. Adult males have ventrally curved spicules in both generations. Moreover, the first-generation male has rounded manubrium, fusiform gubernaculum, conoid and slightly ventrally curved tails with minute mucron, and the second generation has rhomboid manubrium anteriorly ventrad bent, and tails with long and robust mucron. The morphological, morphometrical, molecular, and phylogenetic analyses support the new species status of this nematode, which is hereby described as Steinernema anantnagense n. sp. The bacterial symbiont associated with S. anantnagense n. sp. represents a novel species, closely related to Xenorhabdus japonica. These findings shed light on the diversity of entomopathogenic nematodes and their symbiotic bacteria, providing valuable information for future studies in this field.
Wavelet-based estimation for Gaussian time series and spatio-temporal processes

Modern statistical analyses often require the modeling non-Markov time series and spatio-temporal dependencies. Traditional likelihood methods are computationally demanding for these models, leading us to consider approximate likelihood methods that are computationally efficient, while not overly compromising on the efficiency of the parameter estimates. In this dissertation various wavelet-based Whittle approximations are investigated to model a certain class of nonstationary Gaussian time series and a class of Gaussian spatio-temporal processes. Wavelet transforms can help decorrelate processes across and within wavelet scales, allowing for the simplified modeling of time series and spatio-temporal processes. In addition to being computationally efficient, the proposed maximum wavelet-Whittle likelihood estimators of a Gaussian process are shown to be asymptotically normal. Asymptotic properties of the estimators are verified in simulation studies, demonstrating that the typical independence everywhere assumption assumed for wavelet-based estimation is not optimal. These methods are applied to the analyses of a Southern Oscillation Index climate series and the Irish wind speed data.
Silicon photonic modulators for PAM transmissions

High-speed optical interconnects are crucial for both data centers and high performance computing systems. High power consumption and limited device bandwidth have hindered the move to higher optical transmission speeds. Integrated optical transceivers in silicon photonics (SiP) using pulse-amplitude modulation (PAM) are a promising solution to increase data rates. In this paper, we review recent progress in SiP for PAM transmissions. We focus on materials and technologies available CMOS-compatible photonics processes. Performance metrics of SiP modulators and crucial considerations for high-speed PAM transmissions are discussed. Various driving strategies to achieve optical PAM signals are presented. Some of the state-of-the-art SiP PAM modulators and integrated transmitters are reviewed.