OpenRaiser/Intern-Atlas huggingface dataset:
Impact of Distributed Rate Limiting on Load Distribution in a Latency-sensitive Messaging Service
Venue: arXiv cs.NI 2021
Year: 2021

The cloud's flexibility and promise of seamless auto-scaling notwithstanding, its ability to meet service level objectives (SLOs) typically calls for some form of control in resource usage. This seemingly traditional problem gives rise to new challenges in a cloud setting, and in particular a subtle yet significant trade-off involving load-distribution decisions (the distribution of workload across available cloud resources to optimize performance), and rate limiting (the capping of individual workloads to prevent global over-commitment). This paper investigates that trade-off through the design and implementation of a real-time messaging system motivated by Internet-of-Things (IoT) applications, and demonstrates a solution capable of realizing an effective compromise. The paper's contributions are in both explicating the source of this trade-off, and in demonstrating a possible solution.
Supervised Transfer Learning at Scale for Medical Imaging
Venue: arXiv cs.CV 2021
Year: 2021

Transfer learning is a standard technique to improve performance on tasks with limited data. However, for medical imaging, the value of transfer learning is less clear. This is likely due to the large domain mismatch between the usual natural-image pre-training (e.g. ImageNet) and medical images. However, recent advances in transfer learning have shown substantial improvements from scale. We investigate whether modern methods can change the fortune of transfer learning for medical imaging. For this, we study the class of large-scale pre-trained networks presented by Kolesnikov et al. on three diverse imaging tasks: chest radiography, mammography, and dermatology. We study both transfer performance and critical properties for the deployment in the medical domain, including: out-of-distribution generalization, data-efficiency, sub-group fairness, and uncertainty estimation. Interestingly, we find that for some of these properties transfer from natural to medical images is indeed extremely effective, but only when performed at sufficient scale.
A Dataset of State-Censored Tweets
Venue: arXiv cs.SI 2021
Year: 2021

Many governments impose traditional censorship methods on social media platforms. Instead of removing it completely, many social media companies, including Twitter, only withhold the content from the requesting country. This makes such content still accessible outside of the censored region, allowing for an excellent setting in which to study government censorship on social media. We mine such content using the Internet Archive's Twitter Stream Grab. We release a dataset of 583,437 tweets by 155,715 users that were censored between 2012-2020 July. We also release 4,301 accounts that were censored in their entirety. Additionally, we release a set of 22,083,759 supplemental tweets made up of all tweets by users with at least one censored tweet as well as instances of other users retweeting the censored user. We provide an exploratory analysis of this dataset. Our dataset will not only aid in the study of government censorship but will also aid in studying hate speech detection and the effect of censorship on social media users. The dataset is publicly available at https://doi.org/10.5281/zenodo.4439509
Dynamic DNN Decomposition for Lossless Synergistic Inference
Venue: arXiv cs.DC 2021
Year: 2021

Deep neural networks (DNNs) sustain high performance in today's data processing applications. DNN inference is resource-intensive thus is difficult to fit into a mobile device. An alternative is to offload the DNN inference to a cloud server. However, such an approach requires heavy raw data transmission between the mobile device and the cloud server, which is not suitable for mission-critical and privacy-sensitive applications such as autopilot. To solve this problem, recent advances unleash DNN services using the edge computing paradigm. The existing approaches split a DNN into two parts and deploy the two partitions to computation nodes at two edge computing tiers. Nonetheless, these methods overlook collaborative device-edge-cloud computation resources. Besides, previous algorithms demand the whole DNN re-partitioning to adapt to computation resource changes and network dynamics. Moreover, for resource-demanding convolutional layers, prior works do not give a parallel processing strategy without loss of accuracy at the edge side. To tackle these issues, we propose D3, a dynamic DNN decomposition system for synergistic inference without precision loss. The proposed system introduces a heuristic algorithm named horizontal partition algorithm to split a DNN into three parts. The algorithm can partially adjust the partitions at run time according to processing time and network conditions. At the edge side, a vertical separation module separates feature maps into tiles that can be independently run on different edge nodes in parallel. Extensive quantitative evaluation of five popular DNNs illustrates that D3 outperforms the state-of-the-art counterparts up to 3.4 times in end-to-end DNN inference time and reduces backbone network communication overhead up to 3.68 times.
The Multimodal Sentiment Analysis in Car Reviews (MuSe-CaR) Dataset: Collection, Insights and Improvements
Venue: arXiv cs.MM 2021
Year: 2021

Truly real-life data presents a strong, but exciting challenge for sentiment and emotion research. The high variety of possible `in-the-wild' properties makes large datasets such as these indispensable with respect to building robust machine learning models. A sufficient quantity of data covering a deep variety in the challenges of each modality to force the exploratory analysis of the interplay of all modalities has not yet been made available in this context. In this contribution, we present MuSe-CaR, a first of its kind multimodal dataset. The data is publicly available as it recently served as the testing bed for the 1st Multimodal Sentiment Analysis Challenge, and focused on the tasks of emotion, emotion-target engagement, and trustworthiness recognition by means of comprehensively integrating the audio-visual and language modalities. Furthermore, we give a thorough overview of the dataset in terms of collection and annotation, including annotation tiers not used in this year's MuSe 2020. In addition, for one of the sub-challenges - predicting the level of trustworthiness - no participant outperformed the baseline model, and so we propose a simple, but highly efficient Multi-Head-Attention network that exceeds using multimodal fusion the baseline by around 0.2 CCC (almost 50 % improvement).
Motorcycle System for Optimum Road Safety with Anti-theft Capability
Venue: arXiv cs.CY 2021
Year: 2021

Due to road traffic accidents, 6941 Filipinos died in 2010, and thousands more were wounded or disabled. Head and neck injuries are the main cause of death, severe injury, and motorcycle users disabilities. Motorcycle users make up a large proportion of those on the road who were killed. The main purpose of the study is to develop an MCU Based Motorcycle System for Optimum Road Safety with Anti-theft Capability that will help motorcycle riders to be safe while travelling in national roads. The researchers will be using the prototyping methodology where in a prototype is built according to the initial requirements gathered from the motorists themselves. The expected result of the proposed methodology is the system will be utilizing the different function of each modules to ensure that the riders will be able to detect and avoid possible danger while on the road. As a result of different literature in relation to each module, the system is expected to provide a new leap to ensure the safety of all riders here in the Philippines. Future studies will ensure the development of the system, provide testing and improve the functionality of the system depending on the test result. Due to the high increase in the number of cars and motorcycle travelling on national road, the percentage of accidents also is getting higher. In line with that, the proposed system is expected to lessen the percentage of accident by avoiding the possible cause of it.
Is the Chen-Sbert Divergence a Metric?
Venue: arXiv cs.IT 2021
Year: 2021

Recently, Chen and Sbert proposed a general divergence measure. This report presents some interim findings about the question whether the divergence measure is a metric or not. It has been postulated that (i) the measure might be a metric when (0 < k <= 1), and (ii) the k-th root of the measure might be a metric when (k > 1). The report shows that for a 2-letter alphabet, postulation (i) can be proved. The possible pathway for obtaining a proof for (i) in n-letter cases is also discussed. The authors hope that the report may stimulate more scholarly effort to study the mathematical properties of this divergence measure.
Bridging the Gap: the case for an Incompletely Theorized Agreement on AI policy
Venue: arXiv cs.CY 2021
Year: 2021

Recent progress in artificial intelligence (AI) raises a wide array of ethical and societal concerns. Accordingly, an appropriate policy approach is needed today. While there has been a wave of scholarship in this field, the research community at times appears divided amongst those who emphasize near-term concerns, and those focusing on long-term concerns and corresponding policy measures. In this paper, we seek to map and critically examine this alleged gulf, with a view to understanding the practical space for inter-community collaboration on AI policy. This culminates in a proposal to make use of the legal notion of an incompletely theorized agreement. We propose that on certain issue areas, scholars working with near-term and long-term perspectives can converge and cooperate on selected mutually beneficial AI policy projects all the while maintaining divergent perspectives.
The Impact of Post-editing and Machine Translation on Creativity and Reading Experience
Venue: arXiv cs.CL 2021
Year: 2021

This article presents the results of a study involving the translation of a fictional story from English into Catalan in three modalities: machine-translated (MT), post-edited (MTPE) and translated without aid (HT). Each translation was analysed to evaluate its creativity. Subsequently, a cohort of 88 Catalan participants read the story in a randomly assigned modality and completed a survey. The results show that HT presented a higher creativity score if compared to MTPE and MT. HT also ranked higher in narrative engagement, and translation reception, while MTPE ranked marginally higher in enjoyment. HT and MTPE show no statistically significant differences in any category, whereas MT does in all variables tested. We conclude that creativity is highest when professional translators intervene in the process, especially when working without any aid. We hypothesize that creativity in translation could be the factor that enhances reading engagement and the reception of translated literary texts.
Teaming up with information agents
Venue: arXiv cs.HC 2021
Year: 2021

Despite the intricacies involved in designing a computer as a teampartner, we can observe patterns in team behavior which allow us to describe at a general level how AI systems are to collaborate with humans. Whereas most work on human-machine teaming has focused on physical agents (e.g. robotic systems), our aim is to study how humans can collaborate with information agents. We propose some appropriate team design patterns, and test them using our Collaborative Intelligence Analysis (CIA) tool.
Operationalizing Framing to Support Multiperspective Recommendations of Opinion Pieces
Venue: arXiv cs.IR 2021
Year: 2021

Diversity in personalized news recommender systems is often defined as dissimilarity, and based on topic diversity (e.g., corona versus farmers strike). Diversity in news media, however, is understood as multiperspectivity (e.g., different opinions on corona measures), and arguably a key responsibility of the press in a democratic society. While viewpoint diversity is often considered synonymous with source diversity in communication science domain, in this paper, we take a computational view. We operationalize the notion of framing, adopted from communication science. We apply this notion to a re-ranking of topic-relevant recommended lists, to form the basis of a novel viewpoint diversification method. Our offline evaluation indicates that the proposed method is capable of enhancing the viewpoint diversity of recommendation lists according to a diversity metric from literature. In an online study, on the Blendle platform, a Dutch news aggregator platform, with more than 2000 users, we found that users are willing to consume viewpoint diverse news recommendations. We also found that presentation characteristics significantly influence the reading behaviour of diverse recommendations. These results suggest that future research on presentation aspects of recommendations can be just as important as novel viewpoint diversification methods to truly achieve multiperspectivity in online news environments.
Hierarchical Width-Based Planning and Learning
Venue: arXiv cs.AI 2021
Year: 2021

Width-based search methods have demonstrated state-of-the-art performance in a wide range of testbeds, from classical planning problems to image-based simulators such as Atari games. These methods scale independently of the size of the state-space, but exponentially in the problem width. In practice, running the algorithm with a width larger than 1 is computationally intractable, prohibiting IW from solving higher width problems. In this paper, we present a hierarchical algorithm that plans at two levels of abstraction. A high-level planner uses abstract features that are incrementally discovered from low-level pruning decisions. We illustrate this algorithm in classical planning PDDL domains as well as in pixel-based simulator domains. In classical planning, we show how IW(1) at two levels of abstraction can solve problems of width 2. For pixel-based domains, we show how in combination with a learned policy and a learned value function, the proposed hierarchical IW can outperform current flat IW-based planners in Atari games with sparse rewards.
A Random Algorithm for Profit Maximization with Multiple Adoptions in Online Social Networks
Venue: arXiv cs.SI 2021
Year: 2021

Online social networks have been one of the most effective platforms for marketing and advertising. Through "word of mouth" effects, information or product adoption could spread from some influential individuals to millions of users in social networks. Given a social network $G$ and a constant $k$, the influence maximization problem seeks for $k$ nodes in $G$ that can influence the largest number of nodes. This problem has found important applications, and a large amount of works have been devoted to identifying the few most influential users. But most of existing works only focus on the diffusion of a single idea or product in social networks. However, in reality, one company may produce multiple kinds of products and one user may also have multiple adoptions. Given multiple kinds of different products with different activation costs and profits, it is crucial for the company to distribute the limited budget among multiple products in order to achieve profit maximization. Profit Maximization with Multiple Adoptions (PM$^{2}$A) problem aims to seek for a seed set within the budget to maximize the overall profit. In this paper, a Randomized Modified Greedy (RMG) algorithm based on the Reverse Influence Sampling (RIS) technique is presented for the PM$^{2}$A problem, which could achieve a $(1-1/e-\varepsilon)$-approximate solution with high probability. Compared with the algorithm proposed in [16] that achieves a $\frac{1}{2}(1-1/e^{2})$-approximate solution, our algorithm provides a better performance ratio which is also the best performance ratio of the PM$^{2}$A problem. Comprehensive experiments on three real-world social networks are conducted, and the results demonstrate that our RMG algorithm outperforms the algorithm proposed in [16] and other heuristics in terms of profit maximization, and could better allocate the budget.
Enhancing Security via Deliberate Unpredictability of Solutions in Optimisation
Venue: arXiv cs.DM 2021
Year: 2021

The main aim of decision support systems is to find solutions that satisfy user requirements. Often, this leads to predictability of those solutions, in the sense that having the input data and the model, an adversary or enemy can predict to a great extent the solution produced by your decision support system. Such predictability can be undesirable, for example, in military or security timetabling, or applications that require anonymity. In this paper, we discuss the notion of solution predictability and introduce potential mechanisms to intentionally avoid it.
Boosting performance for software defined networks from traffic engineering perspective
Venue: arXiv cs.NI 2021
Year: 2021

Paths selection algorithms and rate adaptation objective functions are usually studied separately. In contrast, this paper evaluates some traffic engineering (TE) systems for software defined networking obtained by combining path selection techniques with average delay and load balancing, the two most popular TE objective functions. Based on TE simulation results, the best TE system suitable for software defined networks is a system where the paths are calculated using an oblivious routing model and its adaptation rate calculated using an average delay objective function. Thus, we propose the RACKE+AD system combining path sets computed using Racke's oblivious routing and traffic splitting objective function using average delay. This model outperforms current state-of-the-art models, maximizes throughput, achieves better network resource utilization, and minimizes delay. The proposed system outperformed SMORE and SWAN by 4.2% and 9.6% respectively, achieving 27% better utilization and delivering 34% more traffic with 50% less latency compared with both systems on a GEANT network.
Grid Search Hyperparameter Benchmarking of BERT, ALBERT, and LongFormer on DuoRC
Venue: arXiv cs.CL 2021
Year: 2021

The purpose of this project is to evaluate three language models named BERT, ALBERT, and LongFormer on the Question Answering dataset called DuoRC. The language model task has two inputs, a question, and a context. The context is a paragraph or an entire document while the output is the answer based on the context. The goal is to perform grid search hyperparameter fine-tuning using DuoRC. Pretrained weights of the models are taken from the Huggingface library. Different sets of hyperparameters are used to fine-tune the models using two versions of DuoRC which are the SelfRC and the ParaphraseRC. The results show that the ALBERT (pretrained using the SQuAD1 dataset) has an F1 score of 76.4 and an accuracy score of 68.52 after fine-tuning on the SelfRC dataset. The Longformer model (pretrained using the SQuAD and SelfRC datasets) has an F1 score of 52.58 and an accuracy score of 46.60 after fine-tuning on the ParaphraseRC dataset. The current results outperformed the results from the previous model by DuoRC.
Attention Based Video Summaries of Live Online Zoom Classes
Venue: arXiv cs.MM 2021
Year: 2021

This paper describes a system developed to help University students get more from their online lectures, tutorials, laboratory and other live sessions. We do this by logging their attention levels on their laptops during live Zoom sessions and providing them with personalised video summaries of those live sessions. Using facial attention analysis software we create personalised video summaries composed of just the parts where a student's attention was below some threshold. We can also factor in other criteria into video summary generation such as parts where the student was not paying attention while others in the class were, and parts of the video that other students have replayed extensively which a given student has not. Attention and usage based video summaries of live classes are a form of personalised content, they are educational video segments recommended to highlight important parts of live sessions, useful in both topic understanding and in exam preparation. The system also allows a Professor to review the aggregated attention levels of those in a class who attended a live session and logged their attention levels. This allows her to see which parts of the live activity students were paying most, and least, attention to. The Help-Me-Watch system is deployed and in use at our University in a way that protects student's personal data, operating in a GDPR-compliant way.
SEDAT:Security Enhanced Device Attestation with TPM2.0
Venue: arXiv cs.CR 2021
Year: 2021

Remote attestation is one of the ways to verify the state of an untrusted device. Earlier research has attempted remote verification of a devices' state using hardware, software, or hybrid approaches. Majority of them have used Attestation Key as a hardware root of trust, which does not detect hardware modification or counterfeit issues. In addition, they do not have a secure communication channel between verifier and prover, which makes them susceptible to modern security attacks. This paper presents SEDAT, a novel methodology for remote attestation of the device via a security enhanced communication channel. SEDAT performs hardware, firmware, and software attestation. SEDAT enhances the communication protocol security between verifier and prover by using the Single Packet Authorization (SPA) technique, which provides replay and Denial of Service (DoS) protection. SEDAT provides a way for verifier to get on-demand device integrity and authenticity status via a secure channel. It also enables the verifier to detect counterfeit hardware, change in firmware, and software code on the device. SEDAT validates the manufacturers` root CA certificate, platform certificate, endorsement certificate (EK), and attributes certificates to perform platform hardware attestation. SEDAT is the first known tool that represents firmware, and Integrity Measurement Authority (IMA) event logs in the Canonical Event Logs (CEL) format (recommended by Trusted Computing Group). SEDAT is the first implementation, to the best of our knowledge, that showcases end to end hardware, firmware, and software remote attestation using Trusted Platform Module (TPM2.0) which is resilient to DoS and replay attacks. SEDAT is the first remote verifier that is capable of retrieving a TPM2.0 quote from prover and validate it after regeneration, using a software TPM2.0 quote check.