OpenRaiser/Intern-Atlas huggingface dataset:
A System-Level Framework for Analytical and Empirical Reliability Exploration of STT-MRAM Caches
Venue: arXiv cs.AR 2022
Year: 2022

Spin-Transfer Torque Magnetic RAM (STT-MRAM) is known as the most promising replacement for SRAM technology in large Last-Level Caches (LLCs). Despite its high-density, non-volatility, near-zero leakage power, and immunity to radiation as the major advantages, STT-MRAM-based cache suffers from high error rates mainly due to retention failure, read disturbance, and write failure. Existing studies are limited to estimating the rate of only one or two of these error types for STT-MRAM cache. However, the overall vulnerability of STT-MRAM caches, which its estimation is a must to design cost-efficient reliable caches, has not been offered in any of previous studies. In this paper, we propose a system-level framework for reliability exploration and characterization of errors behavior in STT-MRAM caches. To this end, we formulate the cache vulnerability considering the inter-correlation of the error types including all three errors as well as the dependency of error rates to workloads behavior and Process Variations (PVs). Our analysis reveals that STT-MRAM cache vulnerability is highly workload-dependent and varies by orders of magnitude in different cache access patterns. Our analytical study also shows that this vulnerability divergence significantly increases by process variations in STT-MRAM cells. To evaluate the framework, we implement the error types in the gem5 full-system simulator, and the experimental results show that the total error rate in a shared LLC varies by 32.0x for different workloads. A further 6.5x vulnerability variation is observed when considering PVs in the STT-MRAM cells. In addition, the contribution of each error type in total LLC vulnerability highly varies in different cache access patterns and moreover, error rates are differently affected by PVs.
Horizontal DPA Attacks against ECC: Impact of Implemented Field Multiplication Formula
Venue: arXiv cs.CR 2022
Year: 2022

Due to the nature of applications such as critical infrastructure and the Internet of Things etc. side channel analysis attacks are becoming a serious threat. Side channel analysis attacks take advantage from the fact that the behavior of crypto implementations can be observed and provides hints that simplify revealing keys. A new type of SCA are the so called horizontal SCAs. Well known randomization based countermeasures are effective means against vertical DPA attacks but they are not effective against horizontal DPA attacks. In this paper we investigate how the formula used to implement the multiplication of $GF(2^n)$-elements influences the results of horizontal DPA attacks against a Montgomery kP implementation. We implemented 5 designs with different partial multipliers, i.e. based on different multiplication formulae. We used two different technologies, i.e. a 130 and a 250 nm technology, to simulate power traces for our analysis. We show that the implemented multiplication formula influences the success of horizontal attacks significantly, but we also learned that its impact differs from technology to technology. Our analysis also reveals that the use of different multiplication formulae as the single countermeasure is not sufficient to protect cryptographic designs against horizontal DPA attacks.
Horizontal Attacks against ECC: from Simulations to ASIC
Venue: arXiv cs.CR 2022
Year: 2022

In this paper we analyse the impact of different compile options on the success rate of side-channel analysis attacks. We run horizontal differential side-channel attacks against simulated power traces for the same $kP$ design synthesized using two different compile options after synthesis and after layout. As we are interested in the effect on the produced ASIC we also run the same attack against measured power traces after manufacturing the ASIC. We found that the compile_ultra option reduces the success rate significantly from 5 key candidates with a correctness of between 75 and 90 per cent down to 3 key candidates with a maximum success rate of 72 per cent compared to the simple compile option. Also the success rate after layout shows a very high correlation with the one obtained attacking the measured power and electromagnetic traces, i.e. the simulations are a good indicator of the resistance of the ASIC.
Phocus: Picking Valuable Research from a Sea of Citations
Venue: arXiv cs.DL 2022
Year: 2022

The deluge of new papers has significantly blocked the development of academics, which is mainly caused by author-level and publication-level evaluation metrics that only focus on quantity. Those metrics have resulted in several severe problems that trouble scholars focusing on the important research direction for a long time and even promote an impetuous academic atmosphere. To solve those problems, we propose Phocus, a novel academic evaluation mechanism for authors and papers. Phocus analyzes the sentence containing a citation and its contexts to predict the sentiment towards the corresponding reference. Combining others factors, Phocus classifies citations coarsely, ranks all references within a paper, and utilizes the results of the classifier and the ranking model to get the local influential factor of a reference to the citing paper. The global influential factor of the reference to the citing paper is the product of the local influential factor and the total influential factor of the citing paper. Consequently, an author's academic influential factor is the sum of his contributions to each paper he co-authors.
Using a Nature-based Virtual Reality Environment for Improving Mood States and Cognitive Engagement in Older Adults: A Mixed-method Feasibility Study
Venue: arXiv cs.HC 2022
Year: 2022

Engaging with natural environments and representations of nature has been shown to improve mood states and reduce cognitive decline in older adults. The current study evaluated the use of virtual reality (VR) for presenting immersive 360 degree nature videos and a digitally designed interactive garden for this purpose. Fifty participants (age 60 plus), with varied cognitive and physical abilities, were recruited. Data were collected through pre/post-intervention surveys, standardized observations during the interventions, and post-intervention semi structured interviews. The results indicated significant improvements in attitudes toward VR and in some aspects of mood and engagement. The responses to the environment did not significantly differ among participants with different cognitive abilities; however, those with physical disabilities expressed stronger positive reactions on some metrics compared to participants without disabilities. Almost no negative impacts (cybersickness, task frustration) were found. In the interviews some participants expressed resistance to the technology, in particular the digital garden, indicating that it felt cartoonish or unappealing and that it could not substitute for real nature. However, the majority felt that the VR experiences could be a beneficial activity in situations when real-world contact with nature was not immediately feasible.
Open-Set Recognition of Breast Cancer Treatments
Venue: arXiv cs.LG 2022
Year: 2022

Open-set recognition generalizes a classification task by classifying test samples as one of the known classes from training or "unknown." As novel cancer drug cocktails with improved treatment are continually discovered, predicting cancer treatments can naturally be formulated in terms of an open-set recognition problem. Drawbacks, due to modeling unknown samples during training, arise from straightforward implementations of prior work in healthcare open-set learning. Accordingly, we reframe the problem methodology and apply a recent existing Gaussian mixture variational autoencoder model, which achieves state-of-the-art results for image datasets, to breast cancer patient data. Not only do we obtain more accurate and robust classification results, with a 24.5% average F1 increase compared to a recent method, but we also reexamine open-set recognition in terms of deployability to a clinical setting.
Performance Analysis and Power Allocation of Joint Communication and Sensing Towards Future Communication Networks
Venue: arXiv cs.IT 2022
Year: 2022

To mitigate the radar and communication frequency overlapping caused by massive devices access, we propose a novel joint communication and sensing (JCS) system in this paper, where a micro base station (MiBS) can realize target sensing and cooperative communication simultaneously. Concretely, the MiBS, as the sensing equipment, can also serve as a full-duplex (FD) decode-and-forward (DF) relay to assist the end-to-end communication. To further improve the spectrum utilization, non-orthogonal multiple access (NOMA) is adopted such that the communication between the macro base station (MaBS) and the Internet-of-Things (IoT) devices. To facilitate the performance evaluation, the exact and asymptotic outage probabilities, ergodic rates, sensing probability of the system are characterized. Subsequently, two optimal power allocation (OPA) problems of maximizing the received signal-to-interference-plus-noise ratio of sensing signal and maximizing the sum rate for communication are designed that are solved by means of the Lagrangian method and function monotonicity. The simulation results demonstrate that: 1) the proposed JCS NOMA system can accomplish both communication enhancement and sensing function under the premise of the same power consumption as non-cooperative NOMA; 2) the proposed OPA schemes manifest superiorities over a random power allocation scheme.
Zero-Shot and Few-Shot Classification of Biomedical Articles in Context of the COVID-19 Pandemic
Venue: arXiv cs.CL 2022
Year: 2022

MeSH (Medical Subject Headings) is a large thesaurus created by the National Library of Medicine and used for fine-grained indexing of publications in the biomedical domain. In the context of the COVID-19 pandemic, MeSH descriptors have emerged in relation to articles published on the corresponding topic. Zero-shot classification is an adequate response for timely labeling of the stream of papers with MeSH categories. In this work, we hypothesise that rich semantic information available in MeSH has potential to improve BioBERT representations and make them more suitable for zero-shot/few-shot tasks. We frame the problem as determining if MeSH term definitions, concatenated with paper abstracts are valid instances or not, and leverage multi-task learning to induce the MeSH hierarchy in the representations thanks to a seq2seq task. Results establish a baseline on the MedLine and LitCovid datasets, and probing shows that the resulting representations convey the hierarchical relations present in MeSH.
Discriminant Analysis in Contrasting Dimensions for Polycystic Ovary Syndrome Prognostication
Venue: arXiv cs.LG 2022
Year: 2022

A lot of prognostication methodologies have been formulated for early detection of Polycystic Ovary Syndrome also known as PCOS using Machine Learning. PCOS is a binary classification problem. Dimensionality Reduction methods impact the performance of Machine Learning to a greater extent and using a Supervised Dimensionality Reduction method can give us a new edge to tackle this problem. In this paper we present Discriminant Analysis in different dimensions with Linear and Quadratic form for binary classification along with metrics. We were able to achieve good accuracy and less variation with Discriminant Analysis as compared to many commonly used classification algorithms with training accuracy reaching 97.37% and testing accuracy of 95.92% using Quadratic Discriminant Analysis. Paper also gives the analysis of data with visualizations for deeper understanding of problem.
Locality-Preserving Hashing for Shifts with Connections to Cryptography
Venue: arXiv cs.DS 2022
Year: 2022

Can we sense our location in an unfamiliar environment by taking a sublinear-size sample of our surroundings? Can we efficiently encrypt a message that only someone physically close to us can decrypt? To solve this kind of problems, we introduce and study a new type of hash functions for finding shifts in sublinear time. A function $h:\{0,1\}^n\to \mathbb{Z}_n$ is a $(d,δ)$ {\em locality-preserving hash function for shifts} (LPHS) if: (1) $h$ can be computed by (adaptively) querying $d$ bits of its input, and (2) $\Pr [ h(x) \neq h(x \ll 1) + 1 ] \leq δ$, where $x$ is random and $\ll 1$ denotes a cyclic shift by one bit to the left. We make the following contributions. * Near-optimal LPHS via Distributed Discrete Log: We establish a general two-way connection between LPHS and algorithms for distributed discrete logarithm in the generic group model. Using such an algorithm of Dinur et al. (Crypto 2018), we get LPHS with near-optimal error of $δ=\tilde O(1/d^2)$. This gives an unusual example for the usefulness of group-based cryptography in a post-quantum world. We extend the positive result to non-cyclic and worst-case variants of LPHS. * Multidimensional LPHS: We obtain positive and negative results for a multidimensional extension of LPHS, making progress towards an optimal 2-dimensional LPHS. * Applications: We demonstrate the usefulness of LPHS by presenting cryptographic and algorithmic applications. In particular, we apply multidimensional LPHS to obtain an efficient "packed" implementation of homomorphic secret sharing and a sublinear-time implementation of location-sensitive encryption whose decryption requires a significantly overlapping view.
Applying Artificial Intelligence for Age Estimation in Digital Forensic Investigations
Venue: arXiv cs.CV 2022
Year: 2022

The precise age estimation of child sexual abuse and exploitation (CSAE) victims is one of the most significant digital forensic challenges. Investigators often need to determine the age of victims by looking at images and interpreting the sexual development stages and other human characteristics. The main priority - safeguarding children -- is often negatively impacted by a huge forensic backlog, cognitive bias and the immense psychological stress that this work can entail. This paper evaluates existing facial image datasets and proposes a new dataset tailored to the needs of similar digital forensic research contributions. This small, diverse dataset of 0 to 20-year-old individuals contains 245 images and is merged with 82 unique images from the FG-NET dataset, thus achieving a total of 327 images with high image diversity and low age range density. The new dataset is tested on the Deep EXpectation (DEX) algorithm pre-trained on the IMDB-WIKI dataset. The overall results for young adolescents aged 10 to 15 and older adolescents/adults aged 16 to 20 are very encouraging -- achieving MAEs as low as 1.79, but also suggest that the accuracy for children aged 0 to 10 needs further work. In order to determine the efficacy of the prototype, valuable input of four digital forensic experts, including two forensic investigators, has been taken into account to improve age estimation results. Further research is required to extend datasets both concerning image density and the equal distribution of factors such as gender and racial diversity.
ImageSubject: A Large-scale Dataset for Subject Detection
Venue: arXiv cs.CV 2022
Year: 2022

Main subjects usually exist in the images or videos, as they are the objects that the photographer wants to highlight. Human viewers can easily identify them but algorithms often confuse them with other objects. Detecting the main subjects is an important technique to help machines understand the content of images and videos. We present a new dataset with the goal of training models to understand the layout of the objects and the context of the image then to find the main subjects among them. This is achieved in three aspects. By gathering images from movie shots created by directors with professional shooting skills, we collect the dataset with strong diversity, specifically, it contains 107\,700 images from 21\,540 movie shots. We labeled them with the bounding box labels for two classes: subject and non-subject foreground object. We present a detailed analysis of the dataset and compare the task with saliency detection and object detection. ImageSubject is the first dataset that tries to localize the subject in an image that the photographer wants to highlight. Moreover, we find the transformer-based detection model offers the best result among other popular model architectures. Finally, we discuss the potential applications and conclude with the importance of the dataset.
Brain-Inspired Modelling and Decision-making for Human-Like Autonomous Driving in Mixed Traffic Environment
Venue: arXiv cs.RO 2022
Year: 2022

In this paper, a human-like driving framework is designed for autonomous vehicles (AVs), which aims to make AVs better integrate into the transportation ecology of human driving and eliminate the misunderstanding and incompatibility of human drivers to autonomous driving. Based on the analysis of the real world INTERACTION dataset, a driving aggressiveness estimation model is established with the fuzzy inference approach. Then, a human-like driving model, which integrates the brain emotional learning circuit model (BELCM) with the two-point preview model, is designed. In the human-like lane-change decision-making algorithm, the cost function is designed comprehensively considering driving safety and travel efficiency. Based on the cost function and multi-constraint, the dynamic game algorithm is applied to modelling the interaction and decision making between AV and human driver. Additionally, to guarantee the lane-change safety of AVs, an artificial potential field model is built for collision risk assessment. Finally, the proposed algorithm is evaluated through human-in-the-loop experiments on a driving simulator, and the results demonstrated the feasibility and effectiveness of the proposed method.
Quantifying Gender Bias in Consumer Culture
Venue: arXiv cs.CL 2022
Year: 2022

Cultural items like songs have an important impact in creating and reinforcing stereotypes, biases, and discrimination. But the actual nature of such items is often less transparent. Take songs, for example. Are lyrics biased against women? And how have any such biases changed over time? Natural language processing of a quarter of a million songs over 50 years quantifies misogyny. Women are less likely to be associated with desirable traits (i.e., competence), and while this bias has decreased, it persists. Ancillary analyses further suggest that song lyrics may help drive shifts in societal stereotypes towards women, and that lyrical shifts are driven by male artists (as female artists were less biased to begin with). Overall, these results shed light on cultural evolution, subtle measures of bias and discrimination, and how natural language processing and machine learning can provide deeper insight into stereotypes and cultural change.
A Simulation Platform for Multi-tenant Machine Learning Services on Thousands of GPUs
Venue: arXiv cs.DC 2022
Year: 2022

Multi-tenant machine learning services have become emerging data-intensive workloads in data centers with heavy usage of GPU resources. Due to the large scale, many tuning parameters and heavy resource usage, it is usually impractical to evaluate and benchmark those machine learning services on real clusters. In this demonstration, we present AnalySIM, a cluster simulator that allows efficient design explorations for multi-tenant machine learning services. Specifically, by trace-driven cluster workload simulation, AnalySIM can easily test and analyze various scheduling policies in a number of performance metrics such as GPU resource utilization. AnalySIM simulates the cluster computational resource based on both physical topology and logical partition. The tool has been used in SenseTime to understand the impact of different scheduling policies with the trace from a real production cluster of over 1000 GPUs. We find that preemption and migration are able to significantly reduce average job completion time and mitigate the resource fragmentation problem.
Writing Style Aware Document-level Event Extraction
Venue: arXiv cs.CL 2022
Year: 2022

Event extraction, the technology that aims to automatically get the structural information from documents, has attracted more and more attention in many fields. Most existing works discuss this issue with the token-level multi-label classification framework by distinguishing the tokens as different roles while ignoring the writing styles of documents. The writing style is a special way of content organizing for documents and it is relative fixed in documents with a special field (e.g. financial, medical documents, etc.). We argue that the writing style contains important clues for judging the roles for tokens and the ignorance of such patterns might lead to the performance degradation for the existing works. To this end, we model the writing style in documents as a distribution of argument roles, i.e., Role-Rank Distribution, and propose an event extraction model with the Role-Rank Distribution based Supervision Mechanism to capture this pattern through the supervised training process of an event extraction task. We compare our model with state-of-the-art methods on several real-world datasets. The empirical results show that our approach outperforms other alternatives with the captured patterns. This verifies the writing style contains valuable information that could improve the performance of the event extraction task.
Fusing Blockchain and AI with Metaverse: A Survey
Venue: arXiv cs.CY 2022
Year: 2022

Metaverse as the latest buzzword has attracted great attention from both industry and academia. Metaverse seamlessly integrates the real world with the virtual world and allows avatars to carry out rich activities including creation, display, entertainment, social networking, and trading. Thus, it is promising to build an exciting digital world and to transform a better physical world through the exploration of the metaverse. In this survey, we dive into the metaverse by discussing how Blockchain and Artificial Intelligence (AI) fuse with it through investigating the state-of-the-art studies across the metaverse components, digital currencies, AI applications in the virtual world, and blockchain-empowered technologies. Further exploitation and interdisciplinary research on the fusion of AI and Blockchain towards metaverse will definitely require collaboration from both academia and industries. We wish that our survey can help researchers, engineers, and educators build an open, fair, and rational future metaverse.
Neural Networks to solve Partial Differential Equations: a Comparison with Finite Elements
Venue: arXiv cs.CE 2022
Year: 2022

We compare the Finite Element Method (FEM) simulation of a standard Partial Differential Equation thermal problem of a plate with a hole with a Neural Network (NN) simulation. The largest deviation from the true solution obtained from FEM ($0.015$ for a solution on the order of unity) is easily achieved with NN too without much tuning of the hyperparameters. Accuracies below $0.01$ instead require refinement with an alternative optimizer to reach a similar performance with NN. A rough comparison between the Floating Point Operations values, as a machine-independent quantification of the computational performance, suggests a significant difference between FEM and NN in favour of the former. This also strongly holds for computation time: for an accuracy on the order of $10^{-5}$, FEM and NN require $54$ and $1100$ seconds, respectively. A detailed analysis of the effect of varying different hyperparameters shows that accuracy and computational time only weakly depend on the major part of them. Accuracies below $0.01$ cannot be achieved with the "adam'' optimizers and it looks as though accuracies below $10^{-5}$ cannot be achieved at all. In conclusion, the present work shows that for the concrete case of solving a steady-state 2D heat equation, the performance of a FEM algorithm is significantly better than the solution via networks.