Reviewerly/exHarmony huggingface dataset:
Using graph neural networks for program termination

Termination analyses investigate the termination behavior of programs, intending to detect nontermination, which is known to cause a variety of program bugs (e.g. hanging programs, denial-of-service vulnerabilities). Beyond formal approaches, various attempts have been made to estimate the termination behavior of programs using neural networks. However, the majority of these approaches continue to rely on formal methods to provide strong soundness guarantees and consequently suffer from similar limitations. In this paper, we move away from formal methods and embrace the stochastic nature of machine learning models. Instead of aiming for rigorous guarantees that can be interpreted by solvers, our objective is to provide an estimation of a program's termination behavior and of the likely reason for nontermination (when applicable) that a programmer can use for debugging purposes. Compared to previous approaches using neural networks for program termination, we also take advantage of the graph representation of programs by employing Graph Neural Networks. To further assist programmers in understanding and debugging nontermination bugs, we adapt the notions of attention and semantic segmentation, previously used for other application domains, to programs. Overall, we designed and implemented classifiers for program termination based on Graph Convolutional Networks and Graph Attention Networks, as well as a semantic segmentation Graph Neural Network that localizes AST nodes likely to cause nontermination. We also illustrated how the information provided by semantic segmentation can be combined with program slicing to further aid debugging.
Microwalk-CI

Secret-dependent timing behavior in cryptographic implementations has resulted in exploitable vulnerabilities, undermining their security. Over the years, numerous tools to automatically detect timing leakage or even to prove their absence have been proposed. However, a recent study at IEEE S&P 2022 showed that, while many developers are aware of one or more analysis tools, they have major difficulties integrating these into their workflow, as existing tools are tedious to use and mapping discovered leakages to their originating code segments requires expert knowledge. In addition, existing tools focus on compiled languages like C, or analyze binaries, while the industry and open-source community moved to interpreted languages, most notably JavaScript. In this work, we introduce Microwalk-CI, a novel side-channel analysis framework for easy integration into a JavaScript development workflow. First, we extend existing dynamic approaches with a new analysis algorithm, that allows efficient localization and quantification of leakages, making it suitable for use in practical development. We then present a technique for generating execution traces from JavaScript applications, which can be further analyzed with our and other algorithms originally designed for binary analysis. Finally, we discuss how Microwalk-CI can be integrated into a continuous integration (CI) pipeline for efficient and ongoing monitoring. We evaluate our analysis framework by conducting a thorough evaluation of several popular JavaScript cryptographic libraries, and uncover a number of critical leakages.
Zapper

Privacy concerns prevent the adoption of smart contracts in sensitive domains incompatible with the public nature of shared ledgers.
Laconic Private Set-Intersection From Pairings

Private set-intersection (PSI) is one of the most practically relevant special-purpose secure multiparty computation tasks, as it is motivated by many real-world applications. In this paper we present a new private set-intersection protocol which is laconic, meaning that the protocol only has two rounds and that the first message is independent of the set sizes. Laconic PSI can be useful in applications, where servers with large sets would like to learn the intersection of their set with smaller sets owned by resource-constrained clients and where multiple rounds of interactions are not possible.
NTRU-v-um

NTRUEncrypt is one of the first lattice-based encryption schemes. Furthermore, the earliest fully homomorphic encryption (FHE) schemes rely on the NTRU problem. Currently, NTRU is one of the leading candidates in the NIST post-quantum standardization competition. What makes NTRU appealing is the age of the cryptosystem and relatively good performance.
How to better utilize code graphs in semantic code search?

Semantic code search greatly facilitates software reuse, which enables users to find code snippets highly matching user-specified natural language queries. Due to the rich expressive power of code graphs (e.g., control-flow graph and program dependency graph), both of the two mainstream research works (i.e., multi-modal models and pre-trained models) have attempted to incorporate code graphs for code modelling. However, they still have some limitations: First, there is still much room for improvement in terms of search effectiveness. Second, they have not fully considered the unique features of code graphs.
NMTSloth: understanding and testing efficiency degradation of neural machine translation systems

Neural Machine Translation (NMT) systems have received much recent attention due to their human-level accuracy. While existing works mostly focus on either improving accuracy or testing accuracy robustness, the computation efficiency of NMT systems, which is of paramount importance due to often vast translation demands and real-time requirements, has surprisingly received little attention. In this paper, we make the first attempt to understand and test potential computation efficiency robustness in state-of-the-art NMT systems. By analyzing the working mechanism and implementation of 1455 public-accessible NMT systems, we observe a fundamental property in NMT systems that could be manipulated in an adversarial manner to reduce computation efficiency significantly. Our interesting observation is that the output length determines the computation efficiency of NMT systems instead of the input, where the output length depends on two factors: an often sufficiently large yet pessimistic pre-configured threshold controlling the max number of iterations and a runtime generated end of sentence (EOS) token. Our key motivation is to generate test inputs that could sufficiently delay the generation of EOS such that NMT systems would have to go through enough iterations to satisfy the pre-configured threshold. We present NMTSloth, which develops a gradient-guided technique that searches for a minimal and unnoticeable perturbation at character-level, token-level, and structure-level, which sufficiently delays the appearance of EOS and forces these inputs to reach the naturally-unreachable threshold. To demonstrate the effectiveness of NMTSloth, we conduct a systematic evaluation on three public-available NMT systems: Google T5, AllenAI WMT14, and Helsinki-NLP translators. Experimental results show that NMTSloth can increase NMT systems' response latency and energy consumption by 85% to 3153% and 86% to 3052%, respectively, by perturbing just one character or token in the input sentence. Our case study shows that inputs generated by NMTSloth significantly affect the battery power in real-world mobile devices (i.e., drain more than 30 times battery power than normal inputs).
Understanding why we cannot model how long a code review will take: an industrial case study

Code review is an effective practice for finding defects, but because it is manually intensive it can slow down the continuous integration of changes. Our goal was to understand the factors that influenced the time a change, ie a diff at Meta, would spend in review. A developer survey showed that diff reviews start to feel slow after they have been waiting for around 24 hour review. We built a review time predictor model to identify potential factors that may be causing reviews to take longer, which we could use to predict when would be the best time to nudge reviewers or to identify diff-related factors that we may need to address.
Federated Boosted Decision Trees with Differential Privacy

There is great demand for scalable, secure, and efficient privacy-preserving machine learning models that can be trained over distributed data. While deep learning models typically achieve the best results in a centralized non-secure setting, different models can excel when privacy and communication constraints are imposed. Instead, tree-based approaches such as XGBoost have attracted much attention for their high performance and ease of use; in particular, they often achieve state-of-the-art results on tabular data. Consequently, several recent works have focused on translating Gradient Boosted Decision Tree (GBDT) models like XGBoost into federated settings, via cryptographic mechanisms such as Homomorphic Encryption (HE) and Secure Multi-Party Computation (MPC). However, these do not always provide formal privacy guarantees, or consider the full range of hyperparameters and implementation settings. In this work, we implement the GBDT model under Differential Privacy (DP). We propose a general framework that captures and extends existing approaches for differentially private decision trees. Our framework of methods is tailored to the federated setting, and we show that with a careful choice of techniques it is possible to achieve very high utility while maintaining strong levels of privacy.
SpecDoctor

Transient execution vulnerabilities have critical security impacts to software systems since those break the fundamental security assumptions guaranteed by the CPU. Detecting these critical vulnerabilities in the RTL development stage is particularly important, as it offers a chance to fix the vulnerability early before reaching the chip manufacturing stage.
Automatic Detection of Speculative Execution Combinations

Modern processors employ different speculation mechanisms to speculate over different kinds of instructions. Attackers can exploit these mechanisms simultaneously in order to trigger leaks of speculatively-accessed data. Thus, sound reasoning about such speculative leaks requires accounting for all potential speculation mechanisms. Unfortunately, existing formal models only support reasoning about fixed, hard-coded speculation mechanisms, with no simple support to extend said reasoning to new mechanisms.
Classifying edits to variability in source code

For highly configurable software systems, such as the Linux kernel, maintaining and evolving variability information along changes to source code poses a major challenge. While source code itself may be edited, also feature-to-code mappings may be introduced, removed, or changed. In practice, such edits are often conducted ad-hoc and without proper documentation. To support the maintenance and evolution of variability, it is desirable to understand the impact of each edit on the variability. We propose the first complete and unambiguous classification of edits to variability in source code by means of a catalog of edit classes. This catalog is based on a scheme that can be used to build classifications that are complete and unambiguous by construction. To this end, we introduce a complete and sound model for edits to variability. In about 21.5ms per commit, we validate the correctness and suitability of our classification by classifying each edit in 1.7 million commits in the change histories of 44 open-source software systems automatically. We are able to classify all edits with syntactically correct feature-to-code mappings and find that all our edit classes occur in practice.
Hecate

Despite the recent exponential growth in cloud adoption, businesses that handle sensitive data (e.g., health and financial sectors) are hesitant to migrate their on-premises IT infrastructure to the public cloud due to the lack of trust on the cloud provider. Confidential computing aims to move the cloud provider out of the trusted computing base. New hardware features such as AMD's SEV-SNP can run a full virtual machine (VM) with confidentiality and integrity protection against the cloud. However, there exist challenges in supporting legacy operating systems and enforcing security policies (e.g., firewalls) in confidential VMs.
Sentimental Analysis on Student Feedback using NLP &amp; POS Tagging

Sentiment analysis has been extensively used in a variety of fields in the previous decade, including business, social media, and education. The usage of sentiment analysis is rising, but it remains difficult, particularly in the education area, where the nature of the language utilized by pupils and a sizeable amount of information, dealing with their processing thoughts is a difficult task. Several studies of the literature illustrate the current state of sentiment analysis application which include sector from various perspectives and circumstances. In the teaching analysis system, the qualifier sentimental words for student feedback aren’t thought of the result of teaching analysis through student feedback isn’t displayed whether positive or negative opinion. To efficiently utilize sentimental analysis for the teaching system through student feedback, this paper proposes to analyze the scholar’s text feedback automatically using NLP and post-tagging based approach to conclude the extent of teaching work. Here, the main concentration is applying sentimental analysis for students’ feedback, collected through online mode. The use of ML and the NLP approach to examine the feelings of the students included in the text document is used for classification based on students’ comments to predict the polarity (positive/ negative). A total of 2200 student feedback are collected and analyzed, which results in 60 percentage positive and 40 percentage negative through Post Tagging.
Incentives to Learn: A Location-based Federated Learning Model

Federated Learning (FL) effectiveness depends, among others, on the quality and quantity of the training data and process realized at the end computing nodes. In this paper, we introduce a novel location-based federated learning model, enabled by a low-cost and fast deployable Reconfigurable Intelligent Surfaces (RIS) - based approach that allows to accurately determine the distributed computing nodes' positions. Furthermore, in order to train a global model to support different types of smart city applications, while considering two types of servers, offering a prime and common service, respectively, under different costs, the proposed location-based FL model is complemented by an appropriate incentivization mechanism. The latter is based on the theory of Colonel Blotto games and aims at designing the optimal rewards that should be provided to the computing nodes by the servers, in order the former to be properly motivated to exploit a large amount of their raw data towards improving the FL training performance. The outcome of this process depends on the available budget of each server and on each node's criticality - determined by its position and available data. The performance evaluation of the proposed location-based federated learning model is obtained via modeling and simulation using a real dataset.
Unsupervised quantum circuit learning in high energy physics

Unsupervised training of generative models is a machine learning task that has many applications in scientific computing. In this work we evaluate the efficacy of using quantum circuit-based generative models to generate synthetic data of high energy physics processes. We use non-adversarial, gradient-based training of quantum circuit Born machines to generate joint distributions over 2 and 3 variables.
Learning multi-organ segmentation via partial- and mutual-prior from single-organ datasets

Automatic multi-organ segmentation in medical images is crucial for many clinical applications. The art methods have reported promising results but rely on massive annotated data. However, such data is hard to obtain due to the need for considerable expertise. In contrast, obtaining a single-organ dataset is relatively easier, and many well-annotated ones are publicly available. To this end, this work raises the partially supervised problem: can we use these single-organ datasets to learn a multi-organ segmentation model? In this paper, we propose the Partial- and Mutual-Prior incorporated framework (PRIMP) to learn a robust multi-organ segmentation model by deriving knowledge from single-organ datasets. Unlike existing methods that largely ignore the organs’ anatomical prior knowledge, our PRIMP is designed with two key prior shared across different subjects and datasets: (1) partial-prior, each organ has its own character (e.g., size and shape) and (2) mutual-prior, the relative position between different organs follows the comparatively fixed anatomical structure. Specifically, we propose to incorporate partial-prior of each organ by learning from the single-organ statistics, and inject mutual-prior of organs by learning from the multi-organ statistics. By doing so, the model is encouraged to capture organs’ anatomical invariance across different subjects and datasets, thus guaranteeing the anatomical reasonableness of the predictions, narrowing down the problem of domain gaps, capturing spatial information among different slices, thereby improving organs’ segmentation performance. Experiments on four publicly available datasets (LiTS, Pancreas, KiTS, BTCV) show that our PRIMP can improve the performance on both the multi-organ and single-organ datasets (17.40% and 3.06% above the baseline model on DSC, respectively) and can surpass the comparative approaches.
MSRAformer: Multiscale spatial reverse attention network for polyp segmentation

Colon polyp is an important reference basis in the diagnosis of colorectal cancer(CRC). In routine diagnosis, the polyp area is segmented from the colorectal enteroscopy image, and the obtained pathological information is used to assist in the diagnosis of the disease and surgery. It is always a challenging task for accurate segmentation of polyps in colonoscopy images. There are great differences in shape, size, color and texture of the same type of polyps, and it is difficult to distinguish the polyp region from the mucosal boundary. In recent years, convolutional neural network(CNN) has achieved some results in the task of medical image segmentation. However, CNNs focus on the extraction of local features and be short of the extracting ability of global feature information. This paper presents a Multiscale Spatial Reverse Attention Network called MSRAformer with high performance in medical segmentation, which adopts the Swin Transformer encoder with pyramid structure to extract the features of four different stages, and extracts the multi-scale feature information through the multi-scale channel attention module, which enhances the global feature extraction ability and generalization of the network, and preliminarily aggregates a pre-segmentation result. This paper proposes a spatial reverse attention mechanism module to gradually supplement the edge structure and detail information of the polyp region. Extensive experiments on MSRAformer proved that the segmentation effect on the colonoscopy polyp dataset is better than most state-of-the-art(SOTA) medical image segmentation methods, with better generalization performance. Reference implementation of MSRAformer is available at https://github.com/ChengLong1222/MSRAformer-main.
Performance Evaluation of Optimizers in the Classification of Marble Surface Quality Using CNN

The recital of a convolutional neural network (CNN) is dependent on several things (i.e., optimization, weight initialization, network topology, batches and epochs and activation/loss function and learning rate), as well as on the quality of the input data and specific blend of these model characteristics. When dealing with a classification challenge, relying on a single optimizer is deemed insufficient testing or validity, unless the choice of an optimizer is supported by a compelling reason. As a result, optimizer selection techniques are critical for validating the use of a single optimizer to solve various choice issues. The research begins by evaluating the effectiveness of traditional machine learning algorithms on a dataset of defective/good marble surfaces. The classification was then benchmarked against prominent CNN optimizers to determine where it may be improved. We compare four distinct state-of-the-art gradient descent-based optimizers for CNNs, namely stochastic gradient descent (SGD), adaptive max pooling (Adamax), root mean square propagation (RMS Prop), and Nesterov adaptive momentum (Nadam). The RMS Prop optimizer achieved the highest accuracy of 81% in terms of increasing CNN's classification capabilities.
Impact of Annotator Demographics on Sentiment Dataset Labeling

As machine learning methods become more powerful and capture more nuances of human behavior, biases in the dataset can shape what the model learns and is evaluated on. This paper explores and attempts to quantify the uncertainties and biases due to annotator demographics when creating sentiment analysis datasets. We ask &gt;1000 crowdworkers to provide their demographic information and annotations for multimodal sentiment data and its component modalities. We show that demographic differences among annotators impute a significant effect on their ratings, and that these effects also occur in each component modality. We compare predictions of different state-of-the-art multimodal machine learning algorithms against annotations provided by different demographic groups, and find that changing annotator demographics can cause &gt;4.5 in accuracy difference when determining positive versus negative sentiment. Our findings underscore the importance of accounting for crowdworker attributes, such as demographics, when building datasets, evaluating algorithms, and interpreting results for sentiment analysis.
Instance-wise multi-view representation learning

Multi-view representation learning aims to integrate multiple data information from different views to improve the task performance. The information contained in multi-view data is usually complex. Not only do different views contain different information, but also different samples of the same view contain different information. In the multi-view representation learning, most existing methods either simply treat each view/sample with equal importance, or set fixed or dynamic weights for different views/samples, which is not accurate enough to capture the information of dimensions of each sample and causes information redundancy, especially for high-dimensional samples. In this paper, we propose a novel unsupervised multi-view representation learning method based on instance-wise feature selection. A main advantage of instance-wise feature selection in this paper is that one can dynamically select dimensions that favor both view-specific representation learning and view-shared representation learning for each sample, thereby improving the performance from the perspective of model input. The proposed method consists of selector network, view-specific network and view-shared network. Specifically, selector network is used to obtain the selection template, which selects different number of dimensions conducive to representation learning from different samples to solve the sample heterogeneity problem; the view-specific network and view-shared network are used to extract the view-specific and view-shared representations, respectively. The selector network, view-shared network, and view-specific network are optimized alternately. Extensive experiments on various multi-view datasets with clustering and multi-label classification tasks demonstrate that the proposed method outperforms the state-of-the-art multi-view learning methods.