Reviewerly/exHarmony huggingface dataset:
Unity is Strength: Cross-Task Knowledge Distillation to Improve Code Review Generation

Code review is a fundamental process in software development that plays a critical role in ensuring code quality and reducing the likelihood of errors and bugs. However, code review might be complex, subjective, and time-consuming. Comment generation and code refinement are two key tasks of this process and their automation has traditionally been addressed separately in the literature using different approaches. In this paper, we propose a novel deep-learning architecture, DISCOREV, based on cross-task knowledge distillation that addresses these two tasks simultaneously. In our approach, the fine-tuning of the comment generation model is guided by the code refinement model. We implemented this guidance using two strategies, feedback-based learning objective and embedding alignment objective. We evaluated our approach based on cross-task knowledge distillation by comparing it to the state-of-the-art methods that are based on independent training and fine-tuning. Our results show that our approach generates better review comments as measured by the BLEU score.
The Role of Communication and Reference Songs in the Mixing Process: Insights from Professional Mix Engineers

Effective music mixing requires technical and creative finesse, but clear communication with the client is crucial. The mixing engineer must grasp the client's expectations, and preferences, and collaborate to achieve the desired sound. The tacit agreement for the desired sound of the mix is often established using guides like reference songs and demo mixes exchanged between the artist and the engineer and sometimes verbalised using semantic terms. This paper presents the findings of a two-phased exploratory study aimed at understanding how professional mixing engineers interact with clients and use their feedback to guide the mixing process. For phase one, semi-structured interviews were conducted with five mixing engineers with the aim of gathering insights about their communication strategies, creative processes, and decision-making criteria. Based on the inferences from these interviews, an online questionnaire was designed and administered to a larger group of 22 mixing engineers during the second phase. The results of this study shed light on the importance of collaboration, empathy, and intention in the mixing process, and can inform the development of smart multi-track mixing systems that better support these practices. By highlighting the significance of these findings, this paper contributes to the growing body of research on the collaborative nature of music production and provides actionable recommendations for the design and implementation of innovative mixing tools.
Assume but Verify: Deductive Verification of Leaked Information in Concurrent Applications (Extended Version)

We consider the problem of specifying and proving the security of non-trivial, concurrent programs that intentionally leak information. We present a method that decomposes the problem into (a) proving that the program only leaks information it has declassified via assume annotations already widely used in deductive program verification; and (b) auditing the declassifications against a declarative security policy. We show how condition (a) can be enforced by an extension of the existing program logic SecCSL, and how (b) can be checked by proving a set of simple entailments. Part of the challenge is to define respective semantic soundness criteria and to formally connect these to the logic rules and policy audit. We support our methodology in an auto-active program verifier, which we apply to verify the implementations of various case study programs against a range of declassification policies.
Multi-Modality Guidance Network For Missing Modality Inference

Multimodal models have gained significant success in recent years. Standard multimodal approaches often assume unchanged modalities from training stage to inference stage. In practice, however, many scenarios fail to satisfy such assumptions with missing modalities during inference, leading to limitations on where multimodal models can be applied. While existing methods mitigate the problem through reconstructing the missing modalities, it increases unnecessary computational cost, which could be just as critical, especially for large, deployed systems. To solve the problem from both sides, we propose a novel guidance network that promotes knowledge sharing during training, taking advantage of the multimodal representations to train better single-modality models for inference. Real-life experiment in violence detection shows that our proposed framework trains single-modality models that significantly outperform its traditionally trained counterparts while maintaining the same inference cost.
Perceptual Quality Assessment of 360$^\circ$ Images Based on Generative Scanpath Representation

Despite substantial efforts dedicated to the design of heuristic models for omnidirectional (i.e., 360$^\circ$) image quality assessment (OIQA), a conspicuous gap remains due to the lack of consideration for the diversity of viewing behaviors that leads to the varying perceptual quality of 360$^\circ$ images. Two critical aspects underline this oversight: the neglect of viewing conditions that significantly sway user gaze patterns and the overreliance on a single viewport sequence from the 360$^\circ$ image for quality inference. To address these issues, we introduce a unique generative scanpath representation (GSR) for effective quality inference of 360$^\circ$ images, which aggregates varied perceptual experiences of multi-hypothesis users under a predefined viewing condition. More specifically, given a viewing condition characterized by the starting point of viewing and exploration time, a set of scanpaths consisting of dynamic visual fixations can be produced using an apt scanpath generator. Following this vein, we use the scanpaths to convert the 360$^\circ$ image into the unique GSR, which provides a global overview of gazed-focused contents derived from scanpaths. As such, the quality inference of the 360$^\circ$ image is swiftly transformed to that of GSR. We then propose an efficient OIQA computational framework by learning the quality maps of GSR. Comprehensive experimental results validate that the predictions of the proposed framework are highly consistent with human perception in the spatiotemporal domain, especially in the challenging context of locally distorted 360$^\circ$ images under varied viewing conditions. The code will be released at https://github.com/xiangjieSui/GSR
Cross-Image Context Matters for Bongard Problems

Current machine learning methods struggle to solve Bongard problems, which are a type of IQ test that requires deriving an abstract "concept" from a set of positive and negative "support" images, and then classifying whether or not a new query image depicts the key concept. On Bongard-HOI, a benchmark for natural-image Bongard problems, existing methods have only reached 66% accuracy (where chance is 50%). Low accuracy is often attributed to neural nets' lack of ability to find human-like symbolic rules. In this work, we point out that many existing methods are forfeiting accuracy due to a much simpler problem: they do not incorporate information contained in the support set as a whole, and rely instead on information extracted from individual supports. This is a critical issue, because unlike in few-shot learning tasks concerning object classification, the "key concept" in a typical Bongard problem can only be distinguished using multiple positives and multiple negatives. We explore a variety of simple methods to take this cross-image context into account, and demonstrate substantial gains over prior methods, leading to new state-of-the-art performance on Bongard-LOGO (75.3%) and Bongard-HOI (72.45%) and strong performance on the original Bongard problem set (60.84%).
Evaluating Deep Learning-based Melanoma Classification using Immunohistochemistry and Routine Histology: A Three Center Study

Pathologists routinely use immunohistochemical (IHC)-stained tissue slides against MelanA in addition to hematoxylin and eosin (H&E)-stained slides to improve their accuracy in diagnosing melanomas. The use of diagnostic Deep Learning (DL)-based support systems for automated examination of tissue morphology and cellular composition has been well studied in standard H&E-stained tissue slides. In contrast, there are few studies that analyze IHC slides using DL. Therefore, we investigated the separate and joint performance of ResNets trained on MelanA and corresponding H&E-stained slides. The MelanA classifier achieved an area under receiver operating characteristics curve (AUROC) of 0.82 and 0.74 on out of distribution (OOD)-datasets, similar to the H&E-based benchmark classification of 0.81 and 0.75, respectively. A combined classifier using MelanA and H&E achieved AUROCs of 0.85 and 0.81 on the OOD datasets. DL MelanA-based assistance systems show the same performance as the benchmark H&E classification and may be improved by multi stain classification to assist pathologists in their clinical routine.
Text2Control3D: Controllable 3D Avatar Generation in Neural Radiance Fields using Geometry-Guided Text-to-Image Diffusion Model

Recent advances in diffusion models such as ControlNet have enabled geometrically controllable, high-fidelity text-to-image generation. However, none of them addresses the question of adding such controllability to text-to-3D generation. In response, we propose Text2Control3D, a controllable text-to-3D avatar generation method whose facial expression is controllable given a monocular video casually captured with hand-held camera. Our main strategy is to construct the 3D avatar in Neural Radiance Fields (NeRF) optimized with a set of controlled viewpoint-aware images that we generate from ControlNet, whose condition input is the depth map extracted from the input video. When generating the viewpoint-aware images, we utilize cross-reference attention to inject well-controlled, referential facial expression and appearance via cross attention. We also conduct low-pass filtering of Gaussian latent of the diffusion model in order to ameliorate the viewpoint-agnostic texture problem we observed from our empirical analysis, where the viewpoint-aware images contain identical textures on identical pixel positions that are incomprehensible in 3D. Finally, to train NeRF with the images that are viewpoint-aware yet are not strictly consistent in geometry, our approach considers per-image geometric variation as a view of deformation from a shared 3D canonical space. Consequently, we construct the 3D avatar in a canonical space of deformable NeRF by learning a set of per-image deformation via deformation field table. We demonstrate the empirical results and discuss the effectiveness of our method.
Trinary Decision Trees for handling missing data

This paper introduces the Trinary decision tree, an algorithm designed to improve the handling of missing data in decision tree regressors and classifiers. Unlike other approaches, the Trinary decision tree does not assume that missing values contain any information about the response. Both theoretical calculations on estimator bias and numerical illustrations using real data sets are presented to compare its performance with established algorithms in different missing data scenarios (Missing Completely at Random (MCAR), and Informative Missingness (IM)). Notably, the Trinary tree outperforms its peers in MCAR settings, especially when data is only missing out-of-sample, while lacking behind in IM settings. A hybrid model, the TrinaryMIA tree, which combines the Trinary tree and the Missing In Attributes (MIA) approach, shows robust performance in all types of missingness. Despite the potential drawback of slower training speed, the Trinary tree offers a promising and more accurate method of handling missing data in decision tree algorithms.
Cybercrime Bitcoin Revenue Estimations: Quantifying the Impact of Methodology and Coverage

Multiple works have leveraged the public Bitcoin ledger to estimate the revenue cybercriminals obtain from their victims. Estimations focusing on the same target often do not agree, due to the use of different methodologies, seed addresses, and time periods. These factors make it challenging to understand the impact of their methodological differences. Furthermore, they underestimate the revenue due to the (lack of) coverage on the target's payment addresses, but how large this impact remains unknown. In this work, we perform the first systematic analysis on the estimation of cybercrime bitcoin revenue. We implement a tool that can replicate the different estimation methodologies. Using our tool we can quantify, in a controlled setting, the impact of the different methodology steps. In contrast to what is widely believed, we show that the revenue is not always underestimated. There exist methodologies that can introduce huge overestimation. We collect 30,424 payment addresses and use them to compare the financial impact of 6 cybercrimes (ransomware, clippers, sextortion, Ponzi schemes, giveaway scams, exchange scams) and of 141 cybercriminal groups. We observe that the popular multi-input clustering fails to discover addresses for 40% of groups. We quantify, for the first time, the impact of the (lack of) coverage on the estimation. For this, we propose two techniques to achieve high coverage, possibly nearly complete, on the DeadBolt server ransomware. Our expanded coverage enables estimating DeadBolt's revenue at $2.47M, 39 times higher than the estimation using two popular Internet scan engines.
Understanding Self-Supervised Learning of Speech Representation via Invariance and Redundancy Reduction

Self-supervised learning (SSL) has emerged as a promising paradigm for learning flexible speech representations from unlabeled data. By designing pretext tasks that exploit statistical regularities, SSL models can capture useful representations that are transferable to downstream tasks. This study provides an empirical analysis of Barlow Twins (BT), an SSL technique inspired by theories of redundancy reduction in human perception. On downstream tasks, BT representations accelerated learning and transferred across domains. However, limitations exist in disentangling key explanatory factors, with redundancy reduction and invariance alone insufficient for factorization of learned latents into modular, compact, and informative codes. Our ablations study isolated gains from invariance constraints, but the gains were context-dependent. Overall, this work substantiates the potential of Barlow Twins for sample-efficient speech encoding. However, challenges remain in achieving fully hierarchical representations. The analysis methodology and insights pave a path for extensions incorporating further inductive priors and perceptual principles to further enhance the BT self-supervision framework.
DropPos: Pre-Training Vision Transformers by Reconstructing Dropped Positions

As it is empirically observed that Vision Transformers (ViTs) are quite insensitive to the order of input tokens, the need for an appropriate self-supervised pretext task that enhances the location awareness of ViTs is becoming evident. To address this, we present DropPos, a novel pretext task designed to reconstruct Dropped Positions. The formulation of DropPos is simple: we first drop a large random subset of positional embeddings and then the model classifies the actual position for each non-overlapping patch among all possible positions solely based on their visual appearance. To avoid trivial solutions, we increase the difficulty of this task by keeping only a subset of patches visible. Additionally, considering there may be different patches with similar visual appearances, we propose position smoothing and attentive reconstruction strategies to relax this classification problem, since it is not necessary to reconstruct their exact positions in these cases. Empirical evaluations of DropPos show strong capabilities. DropPos outperforms supervised pre-training and achieves competitive results compared with state-of-the-art self-supervised alternatives on a wide range of downstream benchmarks. This suggests that explicitly encouraging spatial reasoning abilities, as DropPos does, indeed contributes to the improved location awareness of ViTs. The code is publicly available at https://github.com/Haochen-Wang409/DropPos.
Interpretable Visual Question Answering via Reasoning Supervision

Transformer-based architectures have recently demonstrated remarkable performance in the Visual Question Answering (VQA) task. However, such models are likely to disregard crucial visual cues and often rely on multimodal shortcuts and inherent biases of the language modality to predict the correct answer, a phenomenon commonly referred to as lack of visual grounding. In this work, we alleviate this shortcoming through a novel architecture for visual question answering that leverages common sense reasoning as a supervisory signal. Reasoning supervision takes the form of a textual justification of the correct answer, with such annotations being already available on large-scale Visual Common Sense Reasoning (VCR) datasets. The model's visual attention is guided toward important elements of the scene through a similarity loss that aligns the learned attention distributions guided by the question and the correct reasoning. We demonstrate both quantitatively and qualitatively that the proposed approach can boost the model's visual perception capability and lead to performance increase, without requiring training on explicit grounding annotations.
Medoid Silhouette clustering with automatic cluster number selection

The evaluation of clustering results is difficult, highly dependent on the evaluated data set and the perspective of the beholder. There are many different clustering quality measures, which try to provide a general measure to validate clustering results. A very popular measure is the Silhouette. We discuss the efficient medoid-based variant of the Silhouette, perform a theoretical analysis of its properties, provide two fast versions for the direct optimization, and discuss the use to choose the optimal number of clusters. We combine ideas from the original Silhouette with the well-known PAM algorithm and its latest improvements FasterPAM. One of the versions guarantees equal results to the original variant and provides a run speedup of $O(k^2)$. In experiments on real data with 30000 samples and $k$=100, we observed a 10464$\times$ speedup compared to the original PAMMEDSIL algorithm. Additionally, we provide a variant to choose the optimal number of clusters directly.
Three Hardness Results for Graph Similarity Problems

Notions of graph similarity provide alternative perspective on the graph isomorphism problem and vice-versa. In this paper, we consider measures of similarity arising from mismatch norms as studied in Gervens and Grohe: the edit distance $\delta_{\mathcal{E}}$, and the metrics arising from $\ell_p$-operator norms, which we denote by $\delta_p$ and $\delta_{|p|}$. We address the following question: can these measures of similarity be used to design polynomial-time approximation algorithms for graph isomorphism? We show that computing an optimal value of $\delta_{\mathcal{E}}$ is \NP-hard on pairs of graphs with the same number of edges. In addition, we show that computing optimal values of $\delta_p$ and $\delta_{|p|}$ is \NP-hard even on pairs of $1$-planar graphs with the same degree sequence and bounded degree. These two results improve on previous known ones, which did not examine the restricted case where the pairs of graphs are required to have the same number of edges. Finally, we study similarity problems on strongly regular graphs and prove some near optimal inequalities with interesting consequences on the computational complexity of graph and group isomorphism.
Adversarially Robust Deep Learning with Optimal-Transport-Regularized Divergences

We introduce the $ARMOR_D$ methods as novel approaches to enhancing the adversarial robustness of deep learning models. These methods are based on a new class of optimal-transport-regularized divergences, constructed via an infimal convolution between an information divergence and an optimal-transport (OT) cost. We use these as tools to enhance adversarial robustness by maximizing the expected loss over a neighborhood of distributions, a technique known as distributionally robust optimization. Viewed as a tool for constructing adversarial samples, our method allows samples to be both transported, according to the OT cost, and re-weighted, according to the information divergence. We demonstrate the effectiveness of our method on malware detection and image recognition applications and find that, to our knowledge, it outperforms existing methods at enhancing the robustness against adversarial attacks. $ARMOR_D$ yields the robustified accuracy of $98.29\%$ against $FGSM$ and $98.18\%$ against $PGD^{40}$ on the MNIST dataset, reducing the error rate by more than $19.7\%$ and $37.2\%$ respectively compared to prior methods. Similarly, in malware detection, a discrete (binary) data domain, $ARMOR_D$ improves the robustified accuracy under $rFGSM^{50}$ attack compared to the previous best-performing adversarial training methods by $37.0\%$ while lowering false negative and false positive rates by $51.1\%$ and $57.53\%$, respectively.
TaleStream: Supporting Story Ideation with Trope Knowledge

Story ideation is a critical part of the story-writing process. It is challenging to support computationally due to its exploratory and subjective nature. Tropes, which are recurring narrative elements across stories, are essential in stories as they shape the structure of narratives and our understanding of them. In this paper, we propose to use tropes as an intermediate representation of stories to approach story ideation. We present TaleStream, a canvas system that uses tropes as building blocks of stories while providing steerable suggestions of story ideas in the form of tropes. Our trope suggestion methods leverage data from the tvtropes.org wiki. We find that 97% of the time, trope suggestions generated by our methods provide better story ideation materials than random tropes. Our system evaluation suggests that TaleStream can support writers’ creative flow and greatly facilitates story development. Tropes, as a rich lexicon of narratives with available examples, play a key role in TaleStream and hold promise for story-creation support systems.
Mixtures of Gaussians are Privately Learnable with a Polynomial Number of Samples

We study the problem of estimating mixtures of Gaussians under the constraint of differential privacy (DP). Our main result is that $\tilde{O}(k^2 d^4 \log(1/\delta) / \alpha^2 \varepsilon)$ samples are sufficient to estimate a mixture of $k$ Gaussians up to total variation distance $\alpha$ while satisfying $(\varepsilon, \delta)$-DP. This is the first finite sample complexity upper bound for the problem that does not make any structural assumptions on the GMMs. To solve the problem, we devise a new framework which may be useful for other tasks. On a high level, we show that if a class of distributions (such as Gaussians) is (1) list decodable and (2) admits a "locally small'' cover (Bun et al., 2021) with respect to total variation distance, then the class of its mixtures is privately learnable. The proof circumvents a known barrier indicating that, unlike Gaussians, GMMs do not admit a locally small cover (Aden-Ali et al., 2021b).