Reviewerly/exHarmony huggingface dataset:
Research on Road Scene Understanding of Autonomous Vehicles Based on Multi-Task Learning

Road scene understanding is crucial to the safe driving of autonomous vehicles. Comprehensive road scene understanding requires a visual perception system to deal with a large number of tasks at the same time, which needs a perception model with a small size, fast speed, and high accuracy. As multi-task learning has evident advantages in performance and computational resources, in this paper, a multi-task model YOLO-Object, Drivable Area, and Lane Line Detection (YOLO-ODL) based on hard parameter sharing is proposed to realize joint and efficient detection of traffic objects, drivable areas, and lane lines. In order to balance tasks of YOLO-ODL, a weight balancing strategy is introduced so that the weight parameters of the model can be automatically adjusted during training, and a Mosaic migration optimization scheme is adopted to improve the evaluation indicators of the model. Our YOLO-ODL model performs well on the challenging BDD100K dataset, achieving the state of the art in terms of accuracy and computational efficiency.
A Survey of Internet of Things and Cyber-Physical Systems: Standards, Algorithms, Applications, Security, Challenges, and Future Directions

The smart city vision has driven the rapid development and advancement of interconnected technologies using the Internet of Things (IoT) and cyber-physical systems (CPS). In this paper, various aspects of IoT and CPS in recent years (from 2013 to May 2023) are surveyed. It first begins with industry standards which ensure cost-effective solutions and interoperability. With ever-growing big data, tremendous undiscovered knowledge can be mined to be transformed into useful applications. Machine learning algorithms are taking the lead to achieve various target applications with formulations such as classification, clustering, regression, prediction, and anomaly detection. Notably, attention has shifted from traditional machine learning algorithms to advanced algorithms, including deep learning, transfer learning, and data generation algorithms, to provide more accurate models. In recent years, there has been an increasing need for advanced security techniques and defense strategies to detect and prevent the IoT and CPS from being attacked. Research challenges and future directions are summarized. We hope that more researchers can conduct more studies on the IoT and on CPS.
Towards More Effective AI-Assisted Programming: A Systematic Design Exploration to Improve Visual Studio IntelliCode’s User Experience

AI-driven code editor extensions such as Visual Studio IntelliCode and Github CoPilot have become extremely popular. These tools recommend inserting chunks of code, with the lines to be inserted presented inline at the current cursor location as gray text. In contrast to their popularity, other AI-driven code recommendation tools that suggest code changes (as opposed to code completions) have remained woefully underused. We conducted lab studies at Microsoft to understand this disparity and found one major cause: discoverability. Code change suggestions are hard to surface through bold, inline interfaces and hence, developers often do not even notice them.Towards a systematic understanding of code change interfaces, we performed a thorough design exploration for various categories of code changes: additive single-line changes, single-line changes, and multi-line changes. Overall, we explored 19 designs through a series of 7 laboratory studies involving 61 programmers and distilled our findings into a set of 5 design principles. To validate our results, we built and deployed a new version of IntelliCode with two of our new inline interfaces in Microsoft Visual Studio 2022 and found that they lead to a significant increase in usage of the corresponding tools.
DNN model development of biogas production from an anaerobic wastewater treatment plant using Bayesian hyperparameter optimization

Deep neural networks have been regarded as accurate models to predict complex fermentation processes due to their capacity to learn from a high number of data sets via artificial intelligence. To enhance the performance of these models the main challenge is to select the appropriate hyperparameters, such as neural network architecture (number of hidden layers and hidden units), activation function, optimizer, learning rate, and others. This study aims to use a hybrid Bayesian optimization with tree-structured Parzen estimator (BO-TPE) to predict biogas production from real wastewater treatment plant data using deep neural network machine learning (DNN) with optimized hyperparameters. Due to the high number of missing sample measurement records, the data preprocessing process has been performed in three sequential steps: the first step is to remove columns with a high percentage of missing values. The second step concerns removing rows with a high number of missing values. The remaining missing values in the dataset were removed in the third step using the dropna function from Pandas' library. Then, on the remaining data, three normalization techniques (MinMaxScaler, RobustScaler, and StandardScaler) were used for 16 relevant features of the anaerobic digestion process (AD) and compared with Non-Normalized data. The RobustScaler technique demonstrated good prediction capabilities of biogas volume produced. The maximum predicted volume was 2236.105 Nm3/d, with the coefficient of determination (R2), the mean absolute error (MAE), and the root mean square error (RMSE) values of 0.712, 164.610, and 223.429, respectively. This value remains slightly higher than the actual biogas volume produced in the wastewater treatment plant, which was 2131 Nm3/d. Adopting StandardScaler, MinMaxScaler, and Non-Normalized data provided statistical performance indicator values of (0.690; 170.518; 231.519), (0.679; 180.575; 235.648), and (0.647; 183.403; 247.203), respectively. These findings offer an autonomous strategy to monitor the effective operational variables of a large-scale digester, ensuring the stability of the complex fermentation process as well as the long-term sustainability and economic viability of the renewable energy approach in the sector of waste treatment.
The (moral) language of hate

Humans use language toward hateful ends, inciting violence and genocide, intimidating and denigrating others based on their identity. Despite efforts to better address the language of hate in the public sphere, the psychological processes involved in hateful language remain unclear. In this work, we hypothesize that morality and hate are concomitant in language. In a series of studies, we find evidence in support of this hypothesis using language from a diverse array of contexts, including the use of hateful language in propaganda to inspire genocide (Study 1), hateful slurs as they occur in large text corpora across a multitude of languages (Study 2), and hate speech on social-media platforms (Study 3). In post hoc analyses focusing on particular moral concerns, we found that the type of moral content invoked through hate speech varied by context, with Purity language prominent in hateful propaganda and online hate speech and Loyalty language invoked in hateful slurs across languages. Our findings provide a new psychological lens for understanding hateful language and points to further research into the intersection of morality and hate, with practical implications for mitigating hateful rhetoric online.
Linguistic Steganalysis via Fusing Multi-Granularity Attentional Text Features

Deep learning based language models have improved generation-based linguistic steganography, posing a huge challenge for linguistic steganalysis.The existing neural-network-based linguistic steganalysis methods are incompetent to deal with complicated text because they only extract single-granularity features such as global or local text features.To fuse multi-granularity text features, we present a novel linguistic steganalysis method based on attentional bidirectional long-shortterm-memory (BiLSTM) and short-cut dense convolutional neural network (CNN).The BiLSTM equipped with the scaled dot-product attention mechanism is used to capture the long dependency representations of the input sentence.The CNN with the short-cut and dense connection is exploited to extract sufficient local semantic features from the word embedding matrix.We connect two structures in parallel, concatenate the long dependency representations and the local semantic features, and classify the stego and cover texts.The results of comparative experiments demonstrate that the proposed method is superior to the state-of-the-art linguistic steganalysis.
Near-real-time estimation of global horizontal irradiance from Himawari-8 satellite data

Accurate estimation of global horizontal irradiance (GHI) is not only an essential requirement of setting up a photoelectric power generation system but also critical information for terrestrial ecological models. Current methods to estimate GHI are mostly focused on hourly or daily scales and very often also require additional meteorological data. In comparison, few works have ever estimated GHI on the near-real-time scale, as the dynamically changing clouds pose great challenges for instantaneously estimating it. In this study, we adopt the Himawari-8 satellite data as the sole input without any supplementary meteorological parameters, to estimate the near-real-time GHI based on four machine learning algorithms and their ensemble. All models achieved similarly good performance, with R2 being about 0.81, and nRMSE being within the range of 25.22%–26.34%. Ground validations revealed that our result outperform the official Himawari-8 shortwave radiance product. Further analyses revealed that different machine learning models behave differently under different weather conditions, while all performed badly under overcast conditions, suggesting an in-depth investigation is required to improve the model performance. Even so, we foresee that this efficient way, which relies solely on the Himawari-8 geostationary satellite data, can be applied widely to estimate near-real-time GHI in the future.
A Side-Channel Attack on a Hardware Implementation of CRYSTALS-Kyber

CRYSTALS-Kyber has been recently selected by the NIST as a new public-key encryption and key-establishment algorithm to be standardized. This makes it important to assess how well CRYSTALS-Kyber implementations withstand side-channel attacks. Software implementations of CRYSTALS-Kyber have already been analyzed and the discovered vulnerabilities were patched in the subsequently released versions. In this paper, we present a profiling side-channel attack on a hardware implementation of CRYSTALS-Kyber. Since hardware implementations carry out computations in parallel, they are typically more difficult to break than their software counterparts. We demonstrate a successful message (session key) recovery attack on a Xilinx Artix-7 FPGA implementation of CRYSTALS-Kyber by deep learning-based power analysis. Our results indicate that currently available hardware implementations of CRYSTALS-Kyber need better protection against side-channel attacks.
Control and Data Flow in Security Smell Detection for Infrastructure as Code: Is It Worth the Effort?

Infrastructure as Code is the practice of developing and maintaining computing infrastructure through executable source code. Unfortunately, IaC has also brought about new cyber attack vectors. Prior work has therefore proposed static analyses that detect security smells in Infrastructure as Code files. However, they have so far remained at a shallow level, disregarding the control and data flow of the scripts under analysis, and may lack awareness of specific syntactic constructs. These limitations inhibit the quality of their results. To address these limitations, in this paper, we present GASEL, a novel security smell detector for the Ansible IaC language. It uses graph queries on program dependence graphs to detect 7 security smells. Our evaluation on an oracle of 243 real-world security smells and comparison against two state-of-the-art security smell detectors shows that awareness of syntax, control flow, and data flow enables our approach to substantially improve both precision and recall. We further question whether the additional effort required to develop and run such an approach is justified in practice. To this end, we investigate the prevalence of indirection through control and data flow in security smells across more than 15 000 Ansible scripts. We find that over 55% of security smells contain data-flow indirection, and over 32% require a whole-project analysis to detect. These findings motivate the need for deeper static analysis tools to detect security vulnerabilities in IaC.
High-resolution analysis of rooftop photovoltaic potential based on hourly generation simulations and load profiles

Rooftop photovoltaics (PV) are playing an increasingly important role in building a clean and decarbonized energy system. For such distributed resources, formulating scientific development plans and incentives tailored to local conditions requires a comprehensive potential assessment at high spatial and temporal resolutions. Here, we evaluate the resource volume, power generation potential, economic feasibility, and market returns on electricity sales of rooftop PV in Jiangsu Province, China at hourly and 500-m resolutions by combining remote sensing survey, PV output simulation and load dispatching. The province's annual rooftop PV generation meets approximately 30% of the total social electricity consumption, and the entire region has reached both plant-side and user-side grid parity. Based on the case study, we investigate the suitable development scale of rooftop PV subject to different owners, as well as the impact of grid's system flexibility and energy storage on rooftop PV curtailment. For household use, the installation of a 3-kW rooftop PV is suitable, while for grid power supply, rooftop PV development needs to be sized to accommodate the grid. We find that increasing system flexibility significantly reduces PV curtailment for the same penetration rate, and that the use of energy storage helps increasing PV penetration under the curtailment constraint. This study represents a relatively extreme case of high penetration of variable solar PV generations, high uncertainty of PV outputs, and high variability in user-side loads, providing a reliable reference for rooftop PV development in other China's provinces.
A New Edge Perturbation Mechanism for Privacy-Preserving Data Collection in IOT

A growing amount of data containing the sensitive information of users is being collected by emerging smart connected devices to the center server in Internet of things (IoT) era, which raises serious privacy concerns for millions of users. However, existing perturbation methods are not effective because of increased disclosure risk and reduced data utility, especially for small data sets. To overcome this issue, we propose a new edge perturbation mechanism based on the concept of global sensitivity to protect the sensitive information in IoT data collection. The edge server is used to mask users' sensitive data, which can not only avoid the data leakage caused by centralized perturbation, but also achieve better data utility than local perturbation. In addition, we present a global noise generation algorithm based on edge perturbation. Each edge server utilizes the global noise generated by the center server to perturb users' sensitive data. It can minimize the disclosure risk while ensuring that the results of commonly performed statistical analyses are identical and equal for both the raw and the perturbed data. Finally, theoretical and experimental evaluations indicate that the proposed mechanism is private and accurate for small data sets.
Towards Evaluating the Robustness of Adversarial Attacks Against Image Scaling Transformation

The robustness of adversarial examples to image scaling transformation is usually ignored when most existing adversarial attacks are proposed. In contrast, image scaling is often the first step of the model to transfer various sizes of input images into fixed ones. We evaluate the impact of image scaling on the robustness of adversarial examples applied to image classification tasks. We set up an image scaling system to provide a basis for robustness evaluation and conduct experiments in different situations to explore the relationship between image scaling and the robustness of adversarial examples. Experiment results show that various scaling algorithms have a similar impact on the robustness of adversarial examples, but the scaling ratio significantly impacts it.
Investigating the direct and indirect effects of Information and Communication Technology on economic growth in the emerging economies: role of financial development, foreign direct investment, innovation, and institutional quality

The growing demand for digitalization in different economic sectors has garnered considerable attention from researchers to explore the effects of Information and Communication Technology (ICT) on economic growth. In this study, we explore the direct and indirect effects of ICT on economic growth, through its interaction with institutional quality, financial development, research and development (R&D) expenditures, and foreign direct investment (FDI), for 13 emerging economies from 2000 to 2020. We find that ICT use has a significantly positive effect on economic growth. It is found that the interaction effects of ICT with financial development and R&D expenditures are favorable to economic growth, whereas ICT does not act synergistically with institutional quality and FDI. This implies that the emerging economies should increase R&D expenditures on ICT services and increase digitalization of the financial sector, along with advancing their ICT infrastructure, strengthening the institutional quality, and encouraging more FDI inflows to maintain their growth momentum.Abbreviations: ICT: nformation and Communication Technology; IMD: International Institute for Management Development; SDGs: Sustainable Development Goals; OECD: Organization for Economic Cooperation and Development; SAARC: South Asian Association for Regional Cooperation; SSA: Sub-Saharan Africa; MNCs: Multinational Companies; MENA: Middle East and North Africa; UNESCO: United Nations Educational, Scientific and Cultural Organization; ITU: International Telecommunication Union; IIEP: International Institute for Educational Planning; MSCI: Morgan Stanley Capital International; WDI: World Development Indicator; R&D: Research and Development; FD: Financial development; FDI: Foreign Direct Investment; IUIT: Individuals Using Internet; MBCS: Mobile Cellular Subscriptions; TO: Trade Openness; IQ: Institutional Quality
Generating synthetic clinical data that capture class imbalanced distributions with generative adversarial networks: Example using antiretroviral therapy for HIV

Clinical data's confidential nature often limits the development of machine learning models in healthcare. Generative adversarial networks (GANs) can synthesise realistic datasets, but suffer from mode collapse, resulting in low diversity and bias towards majority demographics and common clinical practices. This work proposes an extension to the classic GAN framework that includes a variational autoencoder (VAE) and an external memory mechanism to overcome these limitations and generate synthetic data accurately describing imbalanced class distributions commonly found in clinical variables.The proposed method generated a synthetic dataset related to antiretroviral therapy for human immunodeficiency virus (ART for HIV). We evaluated it based on five metrics: (1) accurately representing imbalanced class distribution; (2) the realism of the individual variables; (3) the realism among variables; (4) patient disclosure risk; and (5) the utility of the generated dataset for developing downstream machine learning models.The proposed method overcomes the issue of mode collapse and generates a synthetic dataset that accurately describes imbalanced class distributions commonly found in clinical variables. The generated data has a patient disclosure risk of 0.095%, lower than the 9% threshold stated by Health Canada and the European Medicines Agency, making it suitable for distribution to the research community with high security. The generated data also has high utility, indicating the potential of the proposed method to enable the development of downstream machine learning algorithms for healthcare applications using synthetic data.Our proposed extension to the classic GAN framework, which includes a VAE and an external memory mechanism, represents a promising approach towards generating synthetic data that accurately describe imbalanced class distributions commonly found in clinical variables. This method overcomes the limitations of GANs and creates more realistic datasets with higher patient cohort diversity, facilitating the development of downstream machine learning algorithms for healthcare applications.
Beware of the Unexpected: Bimodal Taint Analysis

Static analysis is a powerful tool for detecting security vulnerabilities and other programming problems. Global taint tracking, in particular, can spot vulnerabilities arising from complicated data flow across multiple functions. However, precisely identifying which flows are problematic is challenging, and sometimes depends on factors beyond the reach of pure program analysis, such as conventions and informal knowledge. For example, learning that a parameter name of an API function locale ends up in a file path is surprising and potentially problematic. In contrast, it would be completely unsurprising to find that a parameter command passed to an API function execaCommand is eventually interpreted as part of an operating-system command. This paper presents Fluffy, a bimodal taint analysis that combines static analysis, which reasons about data flow, with machine learning, which probabilistically determines which flows are potentially problematic. The key idea is to let machine learning models predict from natural language information involved in a taint flow, such as API names, whether the flow is expected or unexpected, and to inform developers only about the latter. We present a general framework and instantiate it with four learned models, which offer different trade-offs between the need to annotate training data and the accuracy of predictions. We implement Fluffy on top of the CodeQL analysis framework and apply it to 250K JavaScript projects. Evaluating on five common vulnerability types, we find that Fluffy achieves an F1 score of 0.85 or more on four of them across a variety of datasets.
ItyFuzz: Snapshot-Based Fuzzer for Smart Contract

Smart contracts are critical financial instruments, and their security is of utmost importance. However, smart contract programs are difficult to fuzz due to the persistent blockchain state behind all transactions. Mutating sequences of transactions are complex and often lead to a suboptimal exploration for both input and program spaces. In this paper, we introduce a novel snapshot-based fuzzer ItyFuzz for testing smart contracts. In ItyFuzz, instead of storing sequences of transactions and mutating from them, we snapshot states and singleton transactions. To explore interesting states, ItyFuzz introduces a dataflow waypoint mechanism to identify states with more potential momentum. ItyFuzz also incorporates comparison waypoints to prune the space of states. By maintaining snapshots of the states, ItyFuzz can synthesize concrete exploits like reentrancy attacks quickly. Because ItyFuzz has second-level response time to test a smart contract, it can be used for on-chain testing, which has many benefits compared to local development testing. Finally, we evaluate ItyFuzz on real-world smart contracts and some hacked on-chain DeFi projects. ItyFuzz outperforms existing fuzzers in terms of instructional coverage and can find and generate realistic exploits for on-chain projects quickly.