OpenRaiser/Intern-Atlas huggingface dataset:
Using Developer Discussions to Guide Fixing Bugs in Software
Venue: arXiv cs.SE 2022
Year: 2022

Automatically fixing software bugs is a challenging task. While recent work showed that natural language context is useful in guiding bug-fixing models, the approach required prompting developers to provide this context, which was simulated through commit messages written after the bug-fixing code changes were made. We instead propose using bug report discussions, which are available before the task is performed and are also naturally occurring, avoiding the need for any additional information from developers. For this, we augment standard bug-fixing datasets with bug report discussions. Using these newly compiled datasets, we demonstrate that various forms of natural language context derived from such discussions can aid bug-fixing, even leading to improved performance over using commit messages corresponding to the oracle bug-fixing commits.
A Secure Future for Open-Source Computational Science and Engineering
Venue: arXiv cs.SE 2022
Year: 2022

Journalists, public policy analysts, and economists have called attention to the growing importance that high-performance and scientific computing have to national security and industrial leadership. As computing continues to power scientific advances in virtually every discipline, so too does it improve our economic productivity and quality of life. The increasing social, political, and economic importance of research software, however, has also brought the question of software security to the fore. Just as unintentional software errors can threaten the integrity of scientific studies, malicious actors could leverage vulnerabilities to alter results, exfiltrate data, and sabotage computing resources. In this editorial, the authors argue for the need to incorporate security practices and perspectives throughout the research software lifecycle, and they propose directions for future work in this space.
Analysis of Male and Female Speakers' Word Choices in Public Speeches
Venue: arXiv cs.CL 2022
Year: 2022

The extent to which men and women use language differently has been questioned previously. Finding clear and consistent gender differences in language is not conclusive in general, and the research is heavily influenced by the context and method employed to identify the difference. In addition, the majority of the research was conducted in written form, and the sample was collected in writing. Therefore, we compared the word choices of male and female presenters in public addresses such as TED lectures. The frequency of numerous types of words, such as parts of speech (POS), linguistic, psychological, and cognitive terms were analyzed statistically to determine how male and female speakers use words differently. Based on our data, we determined that male speakers use specific types of linguistic, psychological, cognitive, and social words in considerably greater frequency than female speakers.
An Agent-based Approach to Automated Game Testing: an Experience Report
Venue: arXiv cs.SE 2022
Year: 2022

Computer games are very challenging to handle for traditional automated testing algorithms. In this paper we will look at intelligent agents as a solution. Agents are suitable for testing games, since they are reactive and able to reason about their environment to decide the action they want to take. This paper presents the experience of using an agent-based automated testing framework called \ivxr\ to test computer games. Three games will be discussed, including a sophisticated 3D game called Space Engineers. We will show how the framework can be used in different ways, either directly to drive a test agent, or as an intelligent functionality that can be driven by a traditional automated testing algorithm such as a random algorithm or a model based testing algorithm.
Cross-Platform and Cross-Domain Abusive Language Detection with Supervised Contrastive Learning
Venue: arXiv cs.CL 2022
Year: 2022

The prevalence of abusive language on different online platforms has been a major concern that raises the need for automated cross-platform abusive language detection. However, prior works focus on concatenating data from multiple platforms, inherently adopting Empirical Risk Minimization (ERM) method. In this work, we address this challenge from the perspective of domain generalization objective. We design SCL-Fish, a supervised contrastive learning integrated meta-learning algorithm to detect abusive language on unseen platforms. Our experimental analysis shows that SCL-Fish achieves better performance over ERM and the existing state-of-the-art models. We also show that SCL-Fish is data-efficient and achieves comparable performance with the large-scale pre-trained models upon finetuning for the abusive language detection task.
A Discrete and Bounded Locally Envy-Free Cake Cutting Protocol on Trees
Venue: arXiv cs.GT 2022
Year: 2022

We study the classic problem of \emph{fairly} dividing a heterogeneous and divisible resource -- modeled as a line segment $[0,1]$ and typically called as a \emph{cake} -- among $n$ agents. This work considers an interesting variant of the problem where agents are embedded on a graph. The graphical constraint entails that each agent evaluates her allocated share only against her neighbors' share. Given a graph, the goal is to efficiently find a \emph{locally envy-free} allocation where every agent values her share of the cake to be at least as much as that of any of her neighbors' share. The most significant contribution of this work is a bounded protocol that finds a locally envy-free allocation among $n$ agents on a tree graph using $n^{O(n)}$ queries under the standard Robertson-Webb (RW) query model. The query complexity of our proposed protocol, though exponential, significantly improves the currently best known hyper-exponential query complexity bound of Aziz and Mackenzie [AM16] for complete graphs. In particular, we also show that if the underlying tree graph has a depth of at most two, one can find a locally envy-free allocation with $O(n^4 \log n)$ RW queries. This is the first and the only known locally envy-free cake cutting protocol with polynomial query complexity for a non-trivial graph structure. Interestingly, our discrete protocols are simple and easy to understand, as opposed to highly involved protocol of [AM16]. This simplicity can be attributed to their recursive nature and the use of a single agent as a designated \emph{cutter}. We believe that these results will help us improve our algorithmic understanding of the arguably challenging problem of envy-free cake-cutting by uncovering the bottlenecks in its query complexity and its relation to the underlying graph structures.
High-Level Synthesis for Packet-Processing Pipelines
Venue: arXiv cs.NI 2022
Year: 2022

Compiling high-level programs to target high-speed packet-processing pipelines is a challenging combinatorial optimization problem. The compiler must configure the pipeline's resources to match the high-level semantics of the program, while packing all of the program's computation into the pipeline's limited resources. State of the art approaches tackle individual aspects of this problem. Yet, they miss opportunities to efficiently produce globally high-quality outcomes. We argue that High-Level Synthesis (HLS), previously applied to ASIC/FPGA design, is the right framework to decompose the compilation problem for pipelines into smaller pieces with modular solutions. We design an HLS-based compiler that works in three phases. Transformation rewrites programs to use more abundant pipeline resources, avoiding scarce ones. Synthesis breaks complex transactional code into configurations of pipelined compute units. Allocation maps the program's compute and memory to the hardware resources. We prototype these ideas in a compiler, CaT, which targets the Tofino pipeline and a cycle-accurate simulator of a Verilog hardware model of an RMT pipeline. CaT can handle programs that existing compilers cannot currently run on pipelines, generating code faster than existing compilers, while using fewer pipeline resources.
Calculating Cognitive Augmentation, A Case Study
Venue: arXiv cs.HC 2022
Year: 2022

We are entering an era in which humans will increasingly work in partnership and collaboration with artificially intelligent entities. For millennia, tools have augmented human physical and mental performance but in the coming era of cognitive systems, human cognitive performance will be augmented. We are only just now beginning to define the fundamental concepts and metrics to describe, characterize, and measure augmented and collaborative cognition. In this paper, the results of a cognitive augmentation experiment are discussed and we calculate the increase in cognitive accuracy and cognitive precision. In the case study, cognitively augmented problem solvers show an increase of 74% in cognitive accuracy (the ability to synthesize desired answers) and a 27% increase in cognitive precision (the ability to synthesize only desired answers). We offer a formal treatment of the case study results and propose cognitive accuracy and cognitive precision as standard metrics to describe and measure human cognitive augmentation.
On automorphism group of a possible short algorithm for multiplication of $3\times3$ matrices
Venue: arXiv cs.CC 2022
Year: 2022

Studying algorithms admitting nontrivial symmetries is a prospective way of constructing new short algorithms of matrix multiplication. The main result of the article is that if there exists an algorithm of multiplicative length $l\leq22$ for multuplication of $3\times3$ matrices then its automorphism group is isomorphic to a subgroup of $S_l\times S_3$.
A Non-Volatile All-Spin Non-Binary Matrix Multiplier: An Efficient Hardware Accelerator for Machine Learning
Venue: arXiv cs.ET 2022
Year: 2022

We propose and analyze a compact and non-volatile nanomagnetic (all-spin) non-binary matrix multiplier performing the multiply-and-accumulate (MAC) operation using two magnetic tunnel junctions - one activated by strain to act as the multiplier, and the other activated by spin-orbit torque pulses to act as a domain wall synapse that performs the operation of the accumulator. It has two advantages over the usual crossbar-based electronic non-binary matrix multiplier. First, while the crossbar architecture requires N3 devices to multiply two matrices, we require only 2N2 devices. Second, our matrix multiplier is non-volatile and retains the information about the product matrix after being powered off. Here, we present an example where each MAC operation can be performed in ~5 ns and the maximum energy dissipated per operation is ~60Nmax aJ, where Nmax is the largest matrix size. This provides a very useful hardware accelerator for machine learning and artificial intelligence tasks which involve the multiplication of large matrices. The non-volatility allows the matrix multiplier to be embedded in powerful non-von-Neumann architectures, including processor-in-memory. It also allows much of the computing to be done at the edge (of internet-of-things) while reducing the need to access the cloud, thereby making artificial intelligence more resilient against cyberattacks.
Rethinking Data-driven Networking with Foundation Models: Challenges and Opportunities
Venue: arXiv cs.NI 2022
Year: 2022

Foundational models have caused a paradigm shift in the way artificial intelligence (AI) systems are built. They have had a major impact in natural language processing (NLP), and several other domains, not only reducing the amount of required labeled data or even eliminating the need for it, but also significantly improving performance on a wide range of tasks. We argue foundation models can have a similar profound impact on network traffic analysis, and management. More specifically, we show that network data shares several of the properties that are behind the success of foundational models in linguistics. For example, network data contains rich semantic content, and several of the networking tasks (e.g., traffic classification, generation of protocol implementations from specification text, anomaly detection) can find similar counterparts in NLP (e.g., sentiment analysis, translation from natural language to code, out-of-distribution). However, network settings also present unique characteristics and challenges that must be overcome. Our contribution is in highlighting the opportunities and challenges at the intersection of foundation models and networking.
On the robustness of non-intrusive speech quality model by adversarial examples
Venue: arXiv cs.SD 2022
Year: 2022

It has been shown recently that deep learning based models are effective on speech quality prediction and could outperform traditional metrics in various perspectives. Although network models have potential to be a surrogate for complex human hearing perception, they may contain instabilities in predictions. This work shows that deep speech quality predictors can be vulnerable to adversarial perturbations, where the prediction can be changed drastically by unnoticeable perturbations as small as $-30$ dB compared with speech inputs. In addition to exposing the vulnerability of deep speech quality predictors, we further explore and confirm the viability of adversarial training for strengthening robustness of models.
Rewards Encoding Environment Dynamics Improves Preference-based Reinforcement Learning
Venue: arXiv cs.LG 2022
Year: 2022

Preference-based reinforcement learning (RL) algorithms help avoid the pitfalls of hand-crafted reward functions by distilling them from human preference feedback, but they remain impractical due to the burdensome number of labels required from the human, even for relatively simple tasks. In this work, we demonstrate that encoding environment dynamics in the reward function (REED) dramatically reduces the number of preference labels required in state-of-the-art preference-based RL frameworks. We hypothesize that REED-based methods better partition the state-action space and facilitate generalization to state-action pairs not included in the preference dataset. REED iterates between encoding environment dynamics in a state-action representation via a self-supervised temporal consistency task, and bootstrapping the preference-based reward function from the state-action representation. Whereas prior approaches train only on the preference-labelled trajectory pairs, REED exposes the state-action representation to all transitions experienced during policy training. We explore the benefits of REED within the PrefPPO [1] and PEBBLE [2] preference learning frameworks and demonstrate improvements across experimental conditions to both the speed of policy learning and the final policy performance. For example, on quadruped-walk and walker-walk with 50 preference labels, REED-based reward functions recover 83% and 66% of ground truth reward policy performance and without REED only 38\% and 21\% are recovered. For some domains, REED-based reward functions result in policies that outperform policies trained on the ground truth reward.
Explainable Artificial Intelligence in Construction: The Content, Context, Process, Outcome Evaluation Framework
Venue: arXiv cs.AI 2022
Year: 2022

Explainable artificial intelligence is an emerging and evolving concept. Its impact on construction, though yet to be realised, will be profound in the foreseeable future. Still, XAI has received limited attention in construction. As a result, no evaluation frameworks have been propagated to enable construction organisations to understand the what, why, how, and when of XAI. Our paper aims to fill this void by developing a content, context, process, and outcome evaluation framework that can be used to justify the adoption and effective management of XAI. After introducing and describing this novel framework, we discuss its implications for future research. While our novel framework is conceptual, it provides a frame of reference for construction organisations to make headway toward realising XAI business value and benefits.
MSLKANet: A Multi-Scale Large Kernel Attention Network for Scene Text Removal
Venue: arXiv cs.CV 2022
Year: 2022

Scene text removal aims to remove the text and fill the regions with perceptually plausible background information in natural images. It has attracted increasing attention due to its various applications in privacy protection, scene text retrieval, and text editing. With the development of deep learning, the previous methods have achieved significant improvements. However, most of the existing methods seem to ignore the large perceptive fields and global information. The pioneer method can get significant improvements by only changing training data from the cropped image to the full image. In this paper, we present a single-stage multi-scale network MSLKANet for scene text removal in full images. For obtaining large perceptive fields and global information, we propose multi-scale large kernel attention (MSLKA) to obtain long-range dependencies between the text regions and the backgrounds at various granularity levels. Furthermore, we combine the large kernel decomposition mechanism and atrous spatial pyramid pooling to build a large kernel spatial pyramid pooling (LKSPP), which can perceive more valid pixels in the spatial dimension while maintaining large receptive fields and low cost of computation. Extensive experimental results indicate that the proposed method achieves state-of-the-art performance on both synthetic and real-world datasets and the effectiveness of the proposed components MSLKA and LKSPP.
Innovative Drug-like Molecule Generation from Flow-based Generative Model
Venue: arXiv cs.LG 2022
Year: 2022

To design a drug given a biological molecule by using deep learning methods, there are many successful models published recently. People commonly used generative models to design new molecules given certain protein. LiGAN was regarded as the baseline of deep learning model which was developed on convolutional neural networks. Recently, GraphBP showed its ability to predict innovative "real" chemicals that the binding affinity outperformed with traditional molecular docking methods by using a flow-based generative model with a graph neural network and multilayer perception. However, all those methods regarded proteins as rigid bodies and only include a very small part of proteins related to binding. However, the dynamics of proteins are essential for drug binding. Based on GraphBP, we proposed to generate more solid work derived from protein data bank. The results will be evaluated by validity and binding affinity by using a computational chemistry algorithm.
Generating Textual Adversaries with Minimal Perturbation
Venue: arXiv cs.CL 2022
Year: 2022

Many word-level adversarial attack approaches for textual data have been proposed in recent studies. However, due to the massive search space consisting of combinations of candidate words, the existing approaches face the problem of preserving the semantics of texts when crafting adversarial counterparts. In this paper, we develop a novel attack strategy to find adversarial texts with high similarity to the original texts while introducing minimal perturbation. The rationale is that we expect the adversarial texts with small perturbation can better preserve the semantic meaning of original texts. Experiments show that, compared with state-of-the-art attack approaches, our approach achieves higher success rates and lower perturbation rates in four benchmark datasets.
On the High Symmetry of Neural Network Functions
Venue: arXiv cs.LG 2022
Year: 2022

Training neural networks means solving a high-dimensional optimization problem. Normally the goal is to minimize a loss function that depends on what is called the network function, or in other words the function that gives the network output given a certain input. This function depends on a large number of parameters, also known as weights, that depends on the network architecture. In general the goal of this optimization problem is to find the global minimum of the network function. In this paper it is discussed how due to how neural networks are designed, the neural network function present a very large symmetry in the parameter space. This work shows how the neural network function has a number of equivalent minima, in other words minima that give the same value for the loss function and the same exact output, that grows factorially with the number of neurons in each layer for feed forward neural network or with the number of filters in a convolutional neural networks. When the number of neurons and layers is large, the number of equivalent minima grows extremely fast. This will have of course consequences for the study of how neural networks converges to minima during training. This results is known, but in this paper for the first time a proper mathematical discussion is presented and an estimate of the number of equivalent minima is derived.
Modular Clinical Decision Support Networks (MoDN) -- Updatable, Interpretable, and Portable Predictions for Evolving Clinical Environments
Venue: arXiv cs.LG 2022
Year: 2022

Data-driven Clinical Decision Support Systems (CDSS) have the potential to improve and standardise care with personalised probabilistic guidance. However, the size of data required necessitates collaborative learning from analogous CDSS's, which are often unsharable or imperfectly interoperable (IIO), meaning their feature sets are not perfectly overlapping. We propose Modular Clinical Decision Support Networks (MoDN) which allow flexible, privacy-preserving learning across IIO datasets, while providing interpretable, continuous predictive feedback to the clinician. MoDN is a novel decision tree composed of feature-specific neural network modules. It creates dynamic personalised representations of patients, and can make multiple predictions of diagnoses, updatable at each step of a consultation. The modular design allows it to compartmentalise training updates to specific features and collaboratively learn between IIO datasets without sharing any data.
Privacy-Preserving Credit Card Fraud Detection using Homomorphic Encryption
Venue: arXiv cs.CR 2022
Year: 2022

Credit card fraud is a problem continuously faced by financial institutions and their customers, which is mitigated by fraud detection systems. However, these systems require the use of sensitive customer transaction data, which introduces both a lack of privacy for the customer and a data breach vulnerability to the card provider. This paper proposes a system for private fraud detection on encrypted transactions using homomorphic encryption. Two models, XGBoost and a feedforward classifier neural network, are trained as fraud detectors on plaintext data. They are then converted to models which use homomorphic encryption for private inference. Latency, storage, and detection results are discussed, along with use cases and feasibility of deployment. The XGBoost model has better performance, with an encrypted inference as low as 6ms, compared to 296ms for the neural network. However, the neural network implementation may still be preferred, as it is simpler to deploy securely. A codebase for the system is also provided, for simulation and further development.