OpenRaiser/Intern-Atlas huggingface dataset:
An Analysis of Various Design Pathways Towards Multi-Terabit Photonic On-Interposer Interconnects
Venue: arXiv cs.ET 2023
Year: 2023

In the wake of dwindling Moore's Law, to address the rapidly increasing complexity and cost of fabricating large-scale, monolithic systems-on-chip (SoCs), the industry has adopted dis-aggregation as a solution, wherein a large monolithic SoC is partitioned into multiple smaller chiplets that are then assembled into a large system-in-package (SiP) using advanced packaging substrates such as silicon interposer. For such interposer-based SiPs, there is a push to realize on-interposer inter-chiplet communication bandwidth of multi-Tb/s and end-to-end communication latency of no more than 10ns. This push comes as the natural progression from some recent prior works on SiP design, and is driven by the proliferating bandwidth demand of modern data-intensive workloads. To meet this bandwidth and latency goal, prior works have focused on a potential solution of using the silicon photonic interposer (SiPhI) for integrating and interconnecting a large number of chiplets into an SiP. Despite the early promise, the existing designs of on-SiPhI interconnects still have to evolve by leaps and bounds to meet the goal of multi-Tb/s bandwidth. However, the possible design pathways, upon which such an evolution can be achieved, have not been explored in any prior works yet. In this paper, we have identified several design pathways that can help evolve on-SiPhI interconnects to achieve multi-Tb/s aggregate bandwidth. We perform an extensive link-level and system-level analysis in which we explore these design pathways in isolation and in different combinations of each other. From our link-level analysis, we have observed that the design pathways that simultaneously enhance the spectral range and optical power budget available for wavelength multiplexing can render aggregate bandwidth of up to 4Tb/s per on-SiPhI link.
Referring to Screen Texts with Voice Assistants
Venue: arXiv cs.HC 2023
Year: 2023

Voice assistants help users make phone calls, send messages, create events, navigate, and do a lot more. However, assistants have limited capacity to understand their users' context. In this work, we aim to take a step in this direction. Our work dives into a new experience for users to refer to phone numbers, addresses, email addresses, URLs, and dates on their phone screens. Our focus lies in reference understanding, which becomes particularly interesting when multiple similar texts are present on screen, similar to visual grounding. We collect a dataset and propose a lightweight general-purpose model for this novel experience. Due to the high cost of consuming pixels directly, our system is designed to rely on the extracted text from the UI. Our model is modular, thus offering flexibility, improved interpretability, and efficient runtime memory utilization.
Impact of Experiencing Misrecognition by Teachable Agents on Learning and Rapport
Venue: arXiv cs.HC 2023
Year: 2023

While speech-enabled teachable agents have some advantages over typing-based ones, they are vulnerable to errors stemming from misrecognition by automatic speech recognition (ASR). These errors may propagate, resulting in unexpected changes in the flow of conversation. We analyzed how such changes are linked with learning gains and learners' rapport with the agents. Our results show they are not related to learning gains or rapport, regardless of the types of responses the agents should have returned given the correct input from learners without ASR errors. We also discuss the implications for optimal error-recovery policies for teachable agents that can be drawn from these findings.
Making forecasting self-learning and adaptive -- Pilot forecasting rack
Venue: arXiv cs.LG 2023
Year: 2023

Retail sales and price projections are typically based on time series forecasting. For some product categories, the accuracy of demand forecasts achieved is low, negatively impacting inventory, transport, and replenishment planning. This paper presents our findings based on a proactive pilot exercise to explore ways to help retailers to improve forecast accuracy for such product categories. We evaluated opportunities for algorithmic interventions to improve forecast accuracy based on a sample product category, Knitwear. The Knitwear product category has a current demand forecast accuracy from non-AI models in the range of 60%. We explored how to improve the forecast accuracy using a rack approach. To generate forecasts, our decision model dynamically selects the best algorithm from an algorithm rack based on performance for a given state and context. Outcomes from our AI/ML forecasting model built using advanced feature engineering show an increase in the accuracy of demand forecast for Knitwear product category by 20%, taking the overall accuracy to 80%. Because our rack comprises algorithms that cater to a range of customer data sets, the forecasting model can be easily tailored for specific customer contexts.
Are Software Updates Useless Against Advanced Persistent Threats?
Venue: arXiv cs.SE 2023
Year: 2023

A dilemma worth Shakespeare's Hamlet is increasingly haunting companies and security researchers: ``to update or not to update, this is the question``. From the perspective of recommended common practices by software vendors the answer is unambiguous: you should keep your software up-to-date. But is common sense always good sense? We argue it is not.
Intelligent Multi-channel Meta-imagers for Accelerating Machine Vision
Venue: arXiv cs.CV 2023
Year: 2023

Rapid developments in machine vision have led to advances in a variety of industries, from medical image analysis to autonomous systems. These achievements, however, typically necessitate digital neural networks with heavy computational requirements, which are limited by high energy consumption and further hinder real-time decision-making when computation resources are not accessible. Here, we demonstrate an intelligent meta-imager that is designed to work in concert with a digital back-end to off-load computationally expensive convolution operations into high-speed and low-power optics. In this architecture, metasurfaces enable both angle and polarization multiplexing to create multiple information channels that perform positive and negatively valued convolution operations in a single shot. The meta-imager is employed for object classification, experimentally achieving 98.6% accurate classification of handwritten digits and 88.8% accuracy in classifying fashion images. With compactness, high speed, and low power consumption, this approach could find a wide range of applications in artificial intelligence and machine vision applications.
4DHumanOutfit: a multi-subject 4D dataset of human motion sequences in varying outfits exhibiting large displacements
Venue: arXiv cs.CV 2023
Year: 2023

This work presents 4DHumanOutfit, a new dataset of densely sampled spatio-temporal 4D human motion data of different actors, outfits and motions. The dataset is designed to contain different actors wearing different outfits while performing different motions in each outfit. In this way, the dataset can be seen as a cube of data containing 4D motion sequences along 3 axes with identity, outfit and motion. This rich dataset has numerous potential applications for the processing and creation of digital humans, e.g. augmented reality, avatar creation and virtual try on. 4DHumanOutfit is released for research purposes at https://kinovis.inria.fr/4dhumanoutfit/. In addition to image data and 4D reconstructions, the dataset includes reference solutions for each axis. We present independent baselines along each axis that demonstrate the value of these reference solutions for evaluation tasks.
Textual Augmentation Techniques Applied to Low Resource Machine Translation: Case of Swahili
Venue: arXiv cs.CL 2023
Year: 2023

In this work we investigate the impact of applying textual data augmentation tasks to low resource machine translation. There has been recent interest in investigating approaches for training systems for languages with limited resources and one popular approach is the use of data augmentation techniques. Data augmentation aims to increase the quantity of data that is available to train the system. In machine translation, majority of the language pairs around the world are considered low resource because they have little parallel data available and the quality of neural machine translation (NMT) systems depend a lot on the availability of sizable parallel corpora. We study and apply three simple data augmentation techniques popularly used in text classification tasks; synonym replacement, random insertion and contextual data augmentation and compare their performance with baseline neural machine translation for English-Swahili (En-Sw) datasets. We also present results in BLEU, ChrF and Meteor scores. Overall, the contextual data augmentation technique shows some improvements both in the $EN \rightarrow SW$ and $SW \rightarrow EN$ directions. We see that there is potential to use these methods in neural machine translation when more extensive experiments are done with diverse datasets.
Accountability Infrastructure: How to implement limits on platform optimization to protect population health
Venue: arXiv cs.CY 2023
Year: 2023

Attention capitalism has generated design processes and product development decisions that prioritize platform growth over all other considerations. To the extent limits have been placed on these incentives, interventions have primarily taken the form of content moderation. While moderation is important for what we call "acute harms," societal-scale harms -- such as negative effects on mental health and social trust -- require new forms of institutional transparency and scientific investigation, which we group under the term accountability infrastructure. This is not a new problem. In fact, there are many conceptual lessons and implementation approaches for accountability infrastructure within the history of public health. After reviewing these insights, we reinterpret the societal harms generated by technology platforms through reference to public health. To that end, we present a novel mechanism design framework and practical measurement methods for that framework. The proposed approach is iterative and built into the product design process, and is applicable for both internally-motivated (i.e. self regulation by companies) and externally-motivated (i.e. government regulation) interventions for a range of societal problems, including mental health. We aim to help shape a research agenda of principles for the design of mechanisms around problem areas on which there is broad consensus and a firm base of support. We offer constructive examples and discussion of potential implementation methods related to these topics, as well as several new data illustrations for potential effects of exposure to online content.
Democratizing LEO Satellite Network Measurement
Venue: arXiv cs.NI 2023
Year: 2023

Low Earth Orbit (LEO) satellite networks are quickly gaining traction with promises of impressively low latency, high bandwidth, and global reach. However, the research community knows relatively little about their operation and performance in practice. The obscurity is largely due to the high barrier of entry for measuring LEO networks, which requires deploying specialized hardware or recruiting large numbers of satellite Internet customers. In this paper, we introduce HitchHiking, a methodology that democratizes global visibility into LEO satellite networks. HitchHiking builds on the observation that Internet-exposed services that use LEO Internet can reveal satellite network architecture and performance, bypassing the need for specialized hardware. We evaluate HitchHiking against ground truth measurements and prior methods, showing that it provides more coverage and accuracy. With HitchHiking, we complete the largest study to date of Starlink network latency, measuring over 2,400 users across 13 countries. We uncover unexpected patterns in latency that surface how LEO routing is more complex than previously understood. Finally, we conclude with recommendations for future research on LEO networks.
Multi-objective Molecular Optimization for Opioid Use Disorder Treatment Using Generative Network Complex
Venue: arXiv cs.LG 2023
Year: 2023

Opioid Use Disorder (OUD) has emerged as a significant global public health issue, with complex multifaceted conditions. Due to the lack of effective treatment options for various conditions, there is a pressing need for the discovery of new medications. In this study, we propose a deep generative model that combines a stochastic differential equation (SDE)-based diffusion modeling with the latent space of a pretrained autoencoder model. The molecular generator enables efficient generation of molecules that are effective on multiple targets, specifically the mu, kappa, and delta opioid receptors. Furthermore, we assess the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of the generated molecules to identify drug-like compounds. To enhance the pharmacokinetic properties of some lead compounds, we employ a molecular optimization approach. We obtain a diverse set of drug-like molecules. We construct binding affinity predictors by integrating molecular fingerprints derived from autoencoder embeddings, transformer embeddings, and topological Laplacians with advanced machine learning algorithms. Further experimental studies are needed to evaluate the pharmacological effects of these drug-like compounds for OUD treatment. Our machine learning platform serves as a valuable tool in designing and optimizing effective molecules for addressing OUD.
SoK: Decoding the Super App Enigma: The Security Mechanisms, Threats, and Trade-offs in OS-alike Apps
Venue: arXiv cs.CR 2023
Year: 2023

The super app paradigm, exemplified by platforms such as WeChat and AliPay, has revolutionized the mobile app landscape by enabling third-party developers to deploy add-ons within these apps. These add-ons, known as miniapps, leverage user data hosted by the super app platforms to provide a wide range of services, such as shopping and gaming. With the rise of miniapps, super apps have transformed into "operating systems", offering encapsulated APIs to miniapp developers as well as in-app miniapp stores for users to explore and download miniapps. In this paper, we provide the first systematic study to consolidate the current state of knowledge in this field from the security perspective: the security measures, threats, and trade-offs of this paradigm. Specifically, we summarize 13 security mechanisms and 10 security threats in super app platforms, followed by a root cause analysis revealing that the security assumptions still may be violated due to issues in underlying systems, implementation of isolation, and vetting. Additionally, we also systematize open problems and trade-offs that need to be addressed by future works to help enhance the security and privacy of this new paradigm.
A Survey on Video Moment Localization
Venue: arXiv cs.CV 2023
Year: 2023

Video moment localization, also known as video moment retrieval, aiming to search a target segment within a video described by a given natural language query. Beyond the task of temporal action localization whereby the target actions are pre-defined, video moment retrieval can query arbitrary complex activities. In this survey paper, we aim to present a comprehensive review of existing video moment localization techniques, including supervised, weakly supervised, and unsupervised ones. We also review the datasets available for video moment localization and group results of related work. In addition, we discuss promising future directions for this field, in particular large-scale datasets and interpretable video moment localization models.
Weight hierarchies of 3-weight linear codes from two $p$-ary quadratic functions
Venue: arXiv cs.IT 2023
Year: 2023

The weight hierarchy of a linear code has been an important research topic in coding theory since Wei's original work in 1991. Choosing $ D=\Big\{(x,y)\in \Big(\F_{p^{s_1}}\times\F_{p^{s_2}}\Big)\Big\backslash\{(0,0)\}: f(x)+g(y)=0\Big\}$ as a defining set , where $f(x),g(y)$ are quadratic forms over $\mathbb{F}_{p^{s_i}},i=1,2$, respectively, with values in $\F_p$, we construct a family of 3-weight $p$-ary linear codes and determine their weight distributions and weight hierarchies completely. Most of the codes can be used in secret sharing schemes.
Intrusion Detection: A Deep Learning Approach
Venue: arXiv cs.CR 2023
Year: 2023

Network intrusions are a significant problem in all industries today. A critical part of the solution is being able to effectively detect intrusions. With recent advances in artificial intelligence, current research has begun adopting deep learning approaches for intrusion detection. Current approaches for multi-class intrusion detection include the use of a deep neural network. However, it fails to take into account spatial relationships between the data objects and long term dependencies present in the dataset. The paper proposes a novel architecture to combat intrusion detection that has a Convolutional Neural Network (CNN) module, along with a Long Short Term Memory(LSTM) module and with a Support Vector Machine (SVM) classification function. The analysis is followed by a comparison of both conventional machine learning techniques and deep learning methodologies, which highlights areas that could be further explored.
Revisiting and Advancing Adversarial Training Through A Simple Baseline
Venue: arXiv cs.CV 2023
Year: 2023

In this paper, we delve into the essential components of adversarial training which is a pioneering defense technique against adversarial attacks. We indicate that some factors such as the loss function, learning rate scheduler, and data augmentation, which are independent of the model architecture, will influence adversarial robustness and generalization. When these factors are controlled for, we introduce a simple baseline approach, termed SimpleAT, that performs competitively with recent methods and mitigates robust overfitting. We conduct extensive experiments on CIFAR-10/100 and Tiny-ImageNet, which validate the robustness of SimpleAT against state-of-the-art adversarial attackers such as AutoAttack. Our results also demonstrate that SimpleAT exhibits good performance in the presence of various image corruptions, such as those found in the CIFAR-10-C. In addition, we empirically show that SimpleAT is capable of reducing the variance in model predictions, which is considered the primary contributor to robust overfitting. Our results also reveal the connections between SimpleAT and many advanced state-of-the-art adversarial defense methods.
Economical Accommodations for Neurodivergent Students in Software Engineering Education: Experiences from an Intervention in Four Undergraduate Courses
Venue: arXiv cs.SE 2023
Year: 2023

Neurodiversity is an umbrella term that describes variation in brain function among individuals, including conditions such as Attention deficit hyperactivity disorder (ADHD), or dyslexia. Neurodiversity is common in the general population, with an estimated 5.0% to 7.1% and 7% of the world population being diagnosed with ADHD and dyslexia respectively. Neurodivergent (ND) individuals often experience challenges in specific tasks, such as difficulties in communication or a reduced attention span in comparison to neurotypical (NT) individuals. However, they also exhibit specific strengths, such as high creativity or attention to detail. Therefore, improving the inclusion of ND individuals is desirable for economic, ethical, and for talent reasons. In higher education, struggles of ND students are well-documented. Common issues in this area are a lack of awareness among other students and staff, forms of assessment that are particularly challenging for some students, and a lack of offered accommodations. These factors commonly lead to stress, anxiety, and ultimately a risk of dropping out of the studies. Accommodations for ND students can require substantial effort. However, smaller changes in course material can already have major impact. In this chapter, we summarise the lessons learned from an intervention in four courses in undergraduate computer science programmes at Reykjavik University, Iceland, over a period of two terms. Following accessibility guidelines produced by interest groups for different ND conditions, we created course material in the form of slides and assignments specifically tailored to ND audiences. We focused on small, economical changes that could be replicated by educators with a minimal investment of time. We evaluated the success of our intervention through two surveys, showing an overall positive response among ND students and NT students.
Automating Microservices Test Failure Analysis using Kubernetes Cluster Logs
Venue: arXiv cs.SE 2023
Year: 2023

Kubernetes is a free, open-source container orchestration system for deploying and managing Docker containers that host microservices. Kubernetes cluster logs help in determining the reason for the failure. However, as systems become more complex, identifying failure reasons manually becomes more difficult and time-consuming. This study aims to identify effective and efficient classification algorithms to automatically determine the failure reason. We compare five classification algorithms, Support Vector Machines, K-Nearest Neighbors, Random Forest, Gradient Boosting Classifier, and Multilayer Perceptron. Our results indicate that Random Forest produces good accuracy while requiring fewer computational resources than other algorithms.
Rethink the Effectiveness of Text Data Augmentation: An Empirical Analysis
Venue: arXiv cs.CL 2023
Year: 2023

In recent years, language models (LMs) have made remarkable progress in advancing the field of natural language processing (NLP). However, the impact of data augmentation (DA) techniques on the fine-tuning (FT) performance of these LMs has been a topic of ongoing debate. In this study, we evaluate the effectiveness of three different FT methods in conjugation with back-translation across an array of 7 diverse NLP tasks, including classification and regression types, covering single-sentence and sentence-pair tasks. Contrary to prior assumptions that DA does not contribute to the enhancement of LMs' FT performance, our findings reveal that continued pre-training on augmented data can effectively improve the FT performance of the downstream tasks. In the most favourable case, continued pre-training improves the performance of FT by more than 10% in the few-shot learning setting. Our finding highlights the potential of DA as a powerful tool for bolstering LMs' performance.