OpenRaiser/Intern-Atlas huggingface dataset:
Social Value Orientation and Integral Emotions in Multi-Agent Systems
Venue: arXiv cs.MA 2023
Year: 2023

Human social behavior is influenced by individual differences in social preferences. Social value orientation (SVO) is a measurable personality trait which indicates the relative importance an individual places on their own and on others' welfare when making decisions. SVO and other individual difference variables are strong predictors of human behavior and social outcomes. However, there are transient changes human behavior associated with emotions that are not captured by individual differences alone. Integral emotions, the emotions which arise in direct response to a decision-making scenario, have been linked to temporary shifts in decision-making preferences. In this work, we investigated the effects of moderating social preferences with integral emotions in multi-agent societies. We developed Svoie, a method for designing agents which make decisions based on established SVO policies, as well as alternative integral emotion policies in response to task outcomes. We conducted simulation experiments in a resource-sharing task environment, and compared societies of Svoie agents with societies of agents with fixed SVO policies. We find that societies of agents which adapt their behavior through integral emotions achieved similar collective welfare to societies of agents with fixed SVO policies, but with significantly reduced inequality between the welfare of agents with different SVO traits. We observed that by allowing agents to change their policy in response to task outcomes, agents can moderate their behavior to achieve greater social equality. \end{abstract}
Behaviour Driven Development: A Systematic Mapping Study
Venue: arXiv cs.SE 2023
Year: 2023

Context: Behaviour Driven Development (BDD) uses scenarios written in semi-structured natural language to express software requirements in a way that can be understood by all stakeholders. The resulting natural language specifications can also be executed to reveal correct and problematic parts of a software. Although BDD was introduced about two decades ago, there is a lack of secondary studies in peer-reviewed scientific literature. Objective: To understand the current state of BDD research by conducting a systematic mapping study that covers studies published from 2006 to 2021. Method: By following the guidelines for conducting systematic mapping studies in software engineering, we sought to answer research questions on types of venues in which BDD papers have been published, research, contributions, studied topics and their evolution, and evaluation methods used in published BDD research. Results: The study identified 166 papers which were mapped. Key results include the following: the dominance of conference papers; scarcity of research with insights from the industry; shortage of philosophical papers on BDD; acute shortage of metrics for measuring various aspects of BDD specifications and the processes for producing BDD specifications; the dominance of studies on using BDD for facilitating various software development endeavours, improving the BDD process and associated artefacts, and applying BDD in different contexts; scarcity of studies on using BDD alongside other software techniques and technologies; increase in diversity of studied BDD topics; and notable use of case studies and experiments to study different BDD aspects. Conclusion: The paper improves our understanding of the state of the art of BDD, and highlights important areas of focus for future BDD research.
Fashion CUT: Unsupervised domain adaptation for visual pattern classification in clothes using synthetic data and pseudo-labels
Venue: arXiv cs.CV 2023
Year: 2023

Accurate product information is critical for e-commerce stores to allow customers to browse, filter, and search for products. Product data quality is affected by missing or incorrect information resulting in poor customer experience. While machine learning can be used to correct inaccurate or missing information, achieving high performance on fashion image classification tasks requires large amounts of annotated data, but it is expensive to generate due to labeling costs. One solution can be to generate synthetic data which requires no manual labeling. However, training a model with a dataset of solely synthetic images can lead to poor generalization when performing inference on real-world data because of the domain shift. We introduce a new unsupervised domain adaptation technique that converts images from the synthetic domain into the real-world domain. Our approach combines a generative neural network and a classifier that are jointly trained to produce realistic images while preserving the synthetic label information. We found that using real-world pseudo-labels during training helps the classifier to generalize in the real-world domain, reducing the synthetic bias. We successfully train a visual pattern classification model in the fashion domain without real-world annotations. Experiments show that our method outperforms other unsupervised domain adaptation algorithms.
Group Activity Recognition via Dynamic Composition and Interaction
Venue: arXiv cs.CV 2023
Year: 2023

Previous group activity recognition approaches were limited to reasoning using human relations or finding important subgroups and tended to ignore indispensable group composition and human-object interactions. This absence makes a partial interpretation of the scene and increases the interference of irrelevant actions on the results. Therefore, we propose our DynamicFormer with Dynamic composition Module (DcM) and Dynamic interaction Module (DiM) to model relations and locations of persons and discriminate the contribution of participants, respectively. Our findings on group composition and human-object interaction inspire our core idea. Group composition tells us the location of people and their relations inside the group, while interaction reflects the relation between humans and objects outside the group. We utilize spatial and temporal encoders in DcM to model our dynamic composition and build DiM to explore interaction with a novel GCN, which has a transformer inside to consider the temporal neighbors of human/object. Also, a Multi-level Dynamic Integration is employed to integrate features from different levels. We conduct extensive experiments on two public datasets and show that our method achieves state-of-the-art.
An Exploration of Encoder-Decoder Approaches to Multi-Label Classification for Legal and Biomedical Text
Venue: arXiv cs.CL 2023
Year: 2023

Standard methods for multi-label text classification largely rely on encoder-only pre-trained language models, whereas encoder-decoder models have proven more effective in other classification tasks. In this study, we compare four methods for multi-label classification, two based on an encoder only, and two based on an encoder-decoder. We carry out experiments on four datasets -- two in the legal domain and two in the biomedical domain, each with two levels of label granularity -- and always depart from the same pre-trained model, T5. Our results show that encoder-decoder methods outperform encoder-only methods, with a growing advantage on more complex datasets and labeling schemes of finer granularity. Using encoder-decoder models in a non-autoregressive fashion, in particular, yields the best performance overall, so we further study this approach through ablations to better understand its strengths.
Predicting Cardiovascular Disease Risk using Photoplethysmography and Deep Learning
Venue: arXiv cs.CV 2023
Year: 2023

Cardiovascular diseases (CVDs) are responsible for a large proportion of premature deaths in low- and middle-income countries. Early CVD detection and intervention is critical in these populations, yet many existing CVD risk scores require a physical examination or lab measurements, which can be challenging in such health systems due to limited accessibility. Here we investigated the potential to use photoplethysmography (PPG), a sensing technology available on most smartphones that can potentially enable large-scale screening at low cost, for CVD risk prediction. We developed a deep learning PPG-based CVD risk score (DLS) to predict the probability of having major adverse cardiovascular events (MACE: non-fatal myocardial infarction, stroke, and cardiovascular death) within ten years, given only age, sex, smoking status and PPG as predictors. We compared the DLS with the office-based refit-WHO score, which adopts the shared predictors from WHO and Globorisk scores (age, sex, smoking status, height, weight and systolic blood pressure) but refitted on the UK Biobank (UKB) cohort. In UKB cohort, DLS's C-statistic (71.1%, 95% CI 69.9-72.4) was non-inferior to office-based refit-WHO score (70.9%, 95% CI 69.7-72.2; non-inferiority margin of 2.5%, p<0.01). The calibration of the DLS was satisfactory, with a 1.8% mean absolute calibration error. Adding DLS features to the office-based score increased the C-statistic by 1.0% (95% CI 0.6-1.4). DLS predicts ten-year MACE risk comparable with the office-based refit-WHO score. It provides a proof-of-concept and suggests the potential of a PPG-based approach strategies for community-based primary prevention in resource-limited regions.
Using Knowledge Units of Programming Languages to Recommend Reviewers for Pull Requests: An Empirical Study
Venue: arXiv cs.SE 2023
Year: 2023

Code review is a key element of quality assurance in software development. Determining the right reviewer for a given code change requires understanding the characteristics of the changed code, identifying the skills of each potential reviewer (expertise profile), and finding a good match between the two. To facilitate this task, we design a code reviewer recommender that operates on the knowledge units (KUs) of a programming language. We define a KU as a cohesive set of key capabilities that are offered by one or more building blocks of a given programming language. We operationalize our KUs using certification exams for the Java programming language. We detect KUs from 10 actively maintained Java projects from GitHub, spanning 290K commits and 65K pull requests (PRs). Next, we generate developer expertise profiles based on the detected KUs. Finally, these KU-based expertise profiles are used to build a code reviewer recommender (KUREC). In RQ1, we observe that KUREC performs as well as the top-performing baseline recommender (RF). From a practical standpoint, we highlight that KUREC's performance is more stable (lower interquartile range) than that of RF, thus making it more consistent and potentially more trustworthy. Next, in RQ2 we design three new recommenders by combining KUREC with our baseline recommenders. These new combined recommenders outperform both KUREC and the individual baselines. Finally, in RQ3 we evaluate how reasonable the recommendations from KUREC and the combined recommenders are when those deviate from the ground truth. Taking together the results from all RQs, we conclude that KUREC and one of the combined recommenders (AD_FREQ) are overall superior to the baseline recommenders that we studied. Future work in the area should thus (i) consider KU-based recommenders as baselines and (ii) experiment with combined recommenders.
An Evaluation and Ranking of Different Voting Schemes for Improved Visual Place Recognition
Venue: arXiv cs.CV 2023
Year: 2023

Visual Place Recognition has recently seen a surge of endeavours utilizing different ensemble approaches to improve VPR performance. Ideas like multi-process fusion or switching involve combining different VPR techniques together, utilizing different strategies. One major aspect often common to many of these strategies is voting. Voting is widely used in many ensemble methods, so it is potentially a relevant subject to explore in terms of its application and significance for improving VPR performance. This paper attempts to looks into detail and analyze a variety of voting schemes to evaluate which voting technique is optimal for an ensemble VPR set up. We take inspiration from a variety of voting schemes that exist and are widely employed in other research fields such as politics and sociology. The idea is inspired by an observation that different voting methods result in different outcomes for the same type of data and each voting scheme is utilized for specific cases in different academic fields. Some of these voting schemes include Condorcet voting, Broda Count and Plurality voting. Voting employed in any aspect requires that a fair system be established, that outputs the best and most favourable results which in our case would involve improving VPR performance. We evaluate some of these voting techniques in a standardized testing of different VPR techniques, using a variety of VPR data sets. We aim to determine whether a single optimal voting scheme exists or, much like in other fields of research, the selection of a voting technique is relative to its application and environment. We also aim to propose a ranking of these different voting methods from best to worst according to our results as this will allow for better selection of voting schemes.
A Systematic Literature Review on Hardware Reliability Assessment Methods for Deep Neural Networks
Venue: arXiv cs.LG 2023
Year: 2023

Artificial Intelligence (AI) and, in particular, Machine Learning (ML) have emerged to be utilized in various applications due to their capability to learn how to solve complex problems. Over the last decade, rapid advances in ML have presented Deep Neural Networks (DNNs) consisting of a large number of neurons and layers. DNN Hardware Accelerators (DHAs) are leveraged to deploy DNNs in the target applications. Safety-critical applications, where hardware faults/errors would result in catastrophic consequences, also benefit from DHAs. Therefore, the reliability of DNNs is an essential subject of research. In recent years, several studies have been published accordingly to assess the reliability of DNNs. In this regard, various reliability assessment methods have been proposed on a variety of platforms and applications. Hence, there is a need to summarize the state of the art to identify the gaps in the study of the reliability of DNNs. In this work, we conduct a Systematic Literature Review (SLR) on the reliability assessment methods of DNNs to collect relevant research works as much as possible, present a categorization of them, and address the open challenges. Through this SLR, three kinds of methods for reliability assessment of DNNs are identified including Fault Injection (FI), Analytical, and Hybrid methods. Since the majority of works assess the DNN reliability by FI, we characterize different approaches and platforms of the FI method comprehensively. Moreover, Analytical and Hybrid methods are propounded. Thus, different reliability assessment methods for DNNs have been elaborated on their conducted DNN platforms and reliability evaluation metrics. Finally, we highlight the advantages and disadvantages of the identified methods and address the open challenges in the research area.
On the Number of $t$-Lee-Error-Correcting Codes
Venue: arXiv cs.IT 2023
Year: 2023

We consider $t$-Lee-error-correcting codes of length $n$ over the residue ring $\mathbb{Z}_m := \mathbb{Z}/m\mathbb{Z}$ and determine upper and lower bounds on the number of $t$-Lee-error-correcting codes. We use two different methods, namely estimating isolated nodes on bipartite graphs and the graph container method. The former gives density results for codes of fixed size and the latter for any size. This confirms some recent density results for linear Lee metric codes and provides new density results for nonlinear codes. To apply a variant of the graph container algorithm we also investigate some geometrical properties of the balls in the Lee metric.
Instant-NeRF: Instant On-Device Neural Radiance Field Training via Algorithm-Accelerator Co-Designed Near-Memory Processing
Venue: arXiv cs.CV 2023
Year: 2023

Instant on-device Neural Radiance Fields (NeRFs) are in growing demand for unleashing the promise of immersive AR/VR experiences, but are still limited by their prohibitive training time. Our profiling analysis reveals a memory-bound inefficiency in NeRF training. To tackle this inefficiency, near-memory processing (NMP) promises to be an effective solution, but also faces challenges due to the unique workloads of NeRFs, including the random hash table lookup, random point processing sequence, and heterogeneous bottleneck steps. Therefore, we propose the first NMP framework, Instant-NeRF, dedicated to enabling instant on-device NeRF training. Experiments on eight datasets consistently validate the effectiveness of Instant-NeRF.
Upgrade error detection to prediction with GRAND
Venue: arXiv cs.IT 2023
Year: 2023

Guessing Random Additive Noise Decoding (GRAND) is a family of hard- and soft-detection error correction decoding algorithms that provide accurate decoding of any moderate redundancy code of any length. Here we establish a method through which any soft-input GRAND algorithm can provide soft output in the form of an accurate a posteriori estimate of the likelihood that a decoding is correct or, in the case of list decoding, the likelihood that the correct decoding is an element of the list. Implementing the method adds negligible additional computation and memory to the existing decoding process. The output permits tuning the balance between undetected errors and block errors for arbitrary moderate redundancy codes including CRCs
Comprehensive Dataset of Synthetic and Manipulated Overhead Imagery for Development and Evaluation of Forensic Tools
Venue: arXiv cs.CV 2023
Year: 2023

We present a first of its kind dataset of overhead imagery for development and evaluation of forensic tools. Our dataset consists of real, fully synthetic and partially manipulated overhead imagery generated from a custom diffusion model trained on two sets of different zoom levels and on two sources of pristine data. We developed our model to support controllable generation of multiple manipulation categories including fully synthetic imagery conditioned on real and generated base maps, and location. We also support partial in-painted imagery with same conditioning options and with several types of manipulated content. The data consist of raw images and ground truth annotations describing the manipulation parameters. We also report benchmark performance on several tasks supported by our dataset including detection of fully and partially manipulated imagery, manipulation localization and classification.
Opportunistic Mutual Exclusion
Venue: arXiv cs.DC 2023
Year: 2023

Mutual exclusion is an important problem in the context of shared resource usage, where only one process can be using the shared resource at any given time. A mutual exclusion protocol that does not use information on the duration for which each process uses the resource can lead to sub-optimal utilization times. We consider a simple two-process mutual exclusion problem with a central server that provides access to the shared resource. We show that even in the absence of a clock, under certain conditions, the server can opportunistically grant early access to a client based on timing information. We call our new protocol opportunistic mutual exclusion. Our approach requires an extra request signal on each channel between client and server to convey extra information, and the server can grant early access based only on the order of events rather than through measuring time. We derive the handshaking specification and production rules for our protocol, and report on the energy and delay of the circuits in a 65nm process.
Stochastic Texture Filtering
Venue: arXiv cs.GR 2023
Year: 2023

2D texture maps and 3D voxel arrays are widely used to add rich detail to the surfaces and volumes of rendered scenes, and filtered texture lookups are integral to producing high-quality imagery. We show that filtering textures after evaluating lighting, rather than before BSDF evaluation as is current practice, gives a more accurate solution to the rendering equation. These benefits are not merely theoretical, but are apparent in common cases. We further show that stochastically sampling texture filters is crucial for enabling this approach, which has not been possible previously except in limited cases. Stochastic texture filtering offers additional benefits, including efficient implementation of high-quality texture filters and efficient filtering of textures stored in compressed and sparse data structures, including neural representations. We demonstrate applications in both real-time and offline rendering and show that the additional stochastic error is minimal. Furthermore, this error is handled well by either spatiotemporal denoising or moderate pixel sampling rates.
Towards an Automatic Optimisation Model Generator Assisted with Generative Pre-trained Transformer
Venue: arXiv cs.NE 2023
Year: 2023

This article presents a framework for generating optimisation models using a pre-trained generative transformer. The framework involves specifying the features that the optimisation model should have and using a language model to generate an initial version of the model. The model is then tested and validated, and if it contains build errors, an automatic edition process is triggered. An experiment was performed using MiniZinc as the target language and two GPT-3.5 language models for generation and debugging. The results show that the use of language models for the generation of optimisation models is feasible, with some models satisfying the requested specifications, while others require further refinement. The study provides promising evidence for the use of language models in the modelling of optimisation problems and suggests avenues for future research.
Fundamental Limits of Multiple Sequence Reconstruction from Substrings
Venue: arXiv cs.IT 2023
Year: 2023

The problem of reconstructing a sequence from the set of its length-$k$ substrings has received considerable attention due to its various applications in genomics. We study an uncoded version of this problem where multiple random sources are to be simultaneously reconstructed from the union of their $k$-mer sets. We consider an asymptotic regime where $m = n^α$ i.i.d. source sequences of length $n$ are to be reconstructed from the set of their substrings of length $k=β\log n$, and seek to characterize the $(α,β)$ pairs for which reconstruction is information-theoretically feasible. We show that, as $n \to \infty$, the source sequences can be reconstructed if $β> \max(2α+1,α+2)$ and cannot be reconstructed if $β< \max( 2α+1, α+ \tfrac32)$, characterizing the feasibility region almost completely. Interestingly, our result shows that there are feasible $(α,β)$ pairs where repeats across the source strings abound, and non-trivial reconstruction algorithms are needed to achieve the fundamental limit.
Causal Information Splitting: Engineering Proxy Features for Robustness to Distribution Shifts
Venue: arXiv cs.LG 2023
Year: 2023

Statistical prediction models are often trained on data from different probability distributions than their eventual use cases. One approach to proactively prepare for these shifts harnesses the intuition that causal mechanisms should remain invariant between environments. Here we focus on a challenging setting in which the causal and anticausal variables of the target are unobserved. Leaning on information theory, we develop feature selection and engineering techniques for the observed downstream variables that act as proxies. We identify proxies that help to build stable models and moreover utilize auxiliary training tasks to answer counterfactual questions that extract stability-enhancing information from proxies. We demonstrate the effectiveness of our techniques on synthetic and real data.