OpenRaiser/Intern-Atlas huggingface dataset:
Ethical Implementation of Artificial Intelligence to Select Embryos in In Vitro Fertilization
Venue: arXiv cs.AI 2021
Year: 2021

AI has the potential to revolutionize many areas of healthcare. Radiology, dermatology, and ophthalmology are some of the areas most likely to be impacted in the near future, and they have received significant attention from the broader research community. But AI techniques are now also starting to be used in in vitro fertilization (IVF), in particular for selecting which embryos to transfer to the woman. The contribution of AI to IVF is potentially significant, but must be done carefully and transparently, as the ethical issues are significant, in part because this field involves creating new people. We first give a brief introduction to IVF and review the use of AI for embryo selection. We discuss concerns with the interpretation of the reported results from scientific and practical perspectives. We then consider the broader ethical issues involved. We discuss in detail the problems that result from the use of black-box methods in this context and advocate strongly for the use of interpretable models. Importantly, there have been no published trials of clinical effectiveness, a problem in both the AI and IVF communities, and we therefore argue that clinical implementation at this point would be premature. Finally, we discuss ways for the broader AI community to become involved to ensure scientifically sound and ethically responsible development of AI in IVF.
Explaining a Series of Models by Propagating Shapley Values
Venue: arXiv cs.LG 2021
Year: 2021

Local feature attribution methods are increasingly used to explain complex machine learning models. However, current methods are limited because they are extremely expensive to compute or are not capable of explaining a distributed series of models where each model is owned by a separate institution. The latter is particularly important because it often arises in finance where explanations are mandated. Here, we present DeepSHAP, a tractable method to propagate local feature attributions through complex series of models based on a connection to the Shapley value. We evaluate DeepSHAP across biological, health, and financial datasets to show that it provides equally salient explanations an order of magnitude faster than existing model-agnostic attribution techniques and demonstrate its use in an important distributed series of models setting.
Targeting the Weakest Link: Social Engineering Attacks in Ethereum Smart Contracts
Venue: arXiv cs.CR 2021
Year: 2021

Ethereum holds multiple billions of U.S. dollars in the form of Ether cryptocurrency and ERC-20 tokens, with millions of deployed smart contracts algorithmically operating these funds. Unsurprisingly, the security of Ethereum smart contracts has been under rigorous scrutiny. In recent years, numerous defense tools have been developed to detect different types of smart contract code vulnerabilities. When opportunities for exploiting code vulnerabilities diminish, the attackers start resorting to social engineering attacks, which aim to influence humans -- often the weakest link in the system. The only known class of social engineering attacks in Ethereum are honeypots, which plant hidden traps for attackers attempting to exploit existing vulnerabilities, thereby targeting only a small population of potential victims. In this work, we explore the possibility and existence of new social engineering attacks beyond smart contract honeypots. We present two novel classes of Ethereum social engineering attacks - Address Manipulation and Homograph - and develop six zero-day social engineering attacks. To show how the attacks can be used in popular programming patterns, we conduct a case study of five popular smart contracts with combined market capitalization exceeding $29 billion, and integrate our attack patterns in their source codes without altering their existing functionality. Moreover, we show that these attacks remain dormant during the test phase but activate their malicious logic only at the final production deployment. We further analyze 85,656 open-source smart contracts, and discover that 1,027 of them can be used for the proposed social engineering attacks. We conduct a professional opinion survey with experts from seven smart contract auditing firms, corroborating that the exposed social engineering attacks bring a major threat to the smart contract systems.
Science as a Public Good: Public Use and Funding of Science
Venue: arXiv cs.DL 2021
Year: 2021

Knowledge of how science is consumed in public domains is essential for a deeper understanding of the role of science in human society. While science is heavily supported by public funding, common depictions suggest that scientific research remains an isolated or 'ivory tower' activity, with weak connectivity to public use, little relationship between the quality of research and its public use, and little correspondence between the funding of science and its public use. This paper introduces a measurement framework to examine public good features of science, allowing us to study public uses of science, the public funding of science, and how use and funding relate. Specifically, we integrate five large-scale datasets that link scientific publications from all scientific fields to their upstream funding support and downstream public uses across three public domains - government documents, the news media, and marketplace invention. We find that the public uses of science are extremely diverse, with different public domains drawing distinctively across scientific fields. Yet amidst these differences, we find key forms of alignment in the interface between science and society. First, despite concerns that the public does not engage high-quality science, we find universal alignment, in each scientific field and public domain, between what the public consumes and what is highly impactful within science. Second, despite myriad factors underpinning the public funding of science, the resulting allocation across fields presents a striking alignment with the field's collective public use. Overall, public uses of science present a rich landscape of specialized consumption, yet collectively science and society interface with remarkable, quantifiable alignment between scientific use, public use, and funding.
Emotion Recognition of the Singing Voice: Toward a Real-Time Analysis Tool for Singers
Venue: arXiv cs.SD 2021
Year: 2021

Current computational-emotion research has focused on applying acoustic properties to analyze how emotions are perceived mathematically or used in natural language processing machine learning models. While recent interest has focused on analyzing emotions from the spoken voice, little experimentation has been performed to discover how emotions are recognized in the singing voice -- both in noiseless and noisy data (i.e., data that is either inaccurate, difficult to interpret, has corrupted/distorted/nonsense information like actual noise sounds in this case, or has a low ratio of usable/unusable information). Not only does this ignore the challenges of training machine learning models on more subjective data and testing them with much noisier data, but there is also a clear disconnect in progress between advancing the development of convolutional neural networks and the goal of emotionally cognizant artificial intelligence. By training a new model to include this type of information with a rich comprehension of psycho-acoustic properties, not only can models be trained to recognize information within extremely noisy data, but advancement can be made toward more complex biofeedback applications -- including creating a model which could recognize emotions given any human information (language, breath, voice, body, posture) and be used in any performance medium (music, speech, acting) or psychological assistance for patients with disorders such as BPD, alexithymia, autism, among others. This paper seeks to reflect and expand upon the findings of related research and present a stepping-stone toward this end goal.
lr-Multisemigroups and Modal Convolution Algebras
Venue: arXiv cs.LO 2021
Year: 2021

We show how modal quantales arise as convolution algebras of functions from lr-multisemigroups that is, multisemigroups with a source map l and a target map r, into modal quantales which can be seen as weight or value algebras. In the tradition of boolean algebras with operators we study modal correspondences between algebraic laws in the three algebras. The class of lr-multisemigroups introduced in this article generalises Schweizer and Sklar's function systems and object-free categories to a setting isomorphic to algebras of ternary relations as used in boolean algebras with operators and in substructural logics. Our results provide a generic construction recipe for weighted modal quantales from such multisemigroups. This is illustrated by many examples, ranging from modal algebras of weighted relations as used in fuzzy mathematics, category quantales in the tradition of category algebras or group rings, incidence algebras over partial orders, discrete and continuous weighted path algebras, weighted languages of pomsets with interfaces, and weighted languages associated with presimplicial and precubical sets. We also discuss how these results can be combined with previous ones for concurrent quantales and generalised to a setting that supports reasoning with stochastic matrices or probabilistic predicate transformers.
Lane Graph Estimation for Scene Understanding in Urban Driving
Venue: arXiv cs.CV 2021
Year: 2021

Lane-level scene annotations provide invaluable data in autonomous vehicles for trajectory planning in complex environments such as urban areas and cities. However, obtaining such data is time-consuming and expensive since lane annotations have to be annotated manually by humans and are as such hard to scale to large areas. In this work, we propose a novel approach for lane geometry estimation from bird's-eye-view images. We formulate the problem of lane shape and lane connections estimation as a graph estimation problem where lane anchor points are graph nodes and lane segments are graph edges. We train a graph estimation model on multimodal bird's-eye-view data processed from the popular NuScenes dataset and its map expansion pack. We furthermore estimate the direction of the lane connection for each lane segment with a separate model which results in a directed lane graph. We illustrate the performance of our LaneGraphNet model on the challenging NuScenes dataset and provide extensive qualitative and quantitative evaluation. Our model shows promising performance for most evaluated urban scenes and can serve as a step towards automated generation of HD lane annotations for autonomous driving.
Can we aggregate human intelligence? an approach for human centric aggregation using ordered weighted averaging operators
Venue: arXiv cs.IR 2021
Year: 2021

The primary objective of this paper is to present an approach for recommender systems that can assimilate ranking to the voters or rankers so that recommendation can be made by giving priority to experts suggestion over usual recommendation. To accomplish this, we have incorporated the concept of human-centric aggregation via Ordered Weighted Aggregation (OWA). Here, we are advocating ranked recommendation where rankers are assigned weights according to their place in the ranking. Further, the recommendation process which is presented here for the recommendation of books to university students exploits linguistic data summaries and Ordered Weighted Aggregation (OWA) technique. In the suggested approach, the weights are assigned in a way that it associates higher weights to best ranked university. The approach has been evaluated over eight different parameters. The superiority of the proposed approach is evident from the evaluation results. We claim that proposed scheme saves storage spaces required in traditional recommender systems as well as it does not need users prior preferences and hence produce a solution for cold start problem. This envisaged that the proposed scheme can be very useful in decision making problems, especially for recommender systems. In addition, it emphasizes on how human-centric aggregation can be useful in recommendation researches, and also it gives a new direction about how various human specific tasks can be numerically aggregated.
Enhancing Fine-Grained Classification for Low Resolution Images
Venue: arXiv cs.CV 2021
Year: 2021

Low resolution fine-grained classification has widespread applicability for applications where data is captured at a distance such as surveillance and mobile photography. While fine-grained classification with high resolution images has received significant attention, limited attention has been given to low resolution images. These images suffer from the inherent challenge of limited information content and the absence of fine details useful for sub-category classification. This results in low inter-class variations across samples of visually similar classes. In order to address these challenges, this research proposes a novel attribute-assisted loss, which utilizes ancillary information to learn discriminative features for classification. The proposed loss function enables a model to learn class-specific discriminative features, while incorporating attribute-level separability. Evaluation is performed on multiple datasets with different models, for four resolutions varying from 32x32 to 224x224. Different experiments demonstrate the efficacy of the proposed attributeassisted loss for low resolution fine-grained classification.
A Master Key Backdoor for Universal Impersonation Attack against DNN-based Face Verification
Venue: arXiv cs.CV 2021
Year: 2021

We introduce a new attack against face verification systems based on Deep Neural Networks (DNN). The attack relies on the introduction into the network of a hidden backdoor, whose activation at test time induces a verification error allowing the attacker to impersonate any user. The new attack, named Master Key backdoor attack, operates by interfering with the training phase, so to instruct the DNN to always output a positive verification answer when the face of the attacker is presented at its input. With respect to existing attacks, the new backdoor attack offers much more flexibility, since the attacker does not need to know the identity of the victim beforehand. In this way, he can deploy a Universal Impersonation attack in an open-set framework, allowing him to impersonate any enrolled users, even those that were not yet enrolled in the system when the attack was conceived. We present a practical implementation of the attack targeting a Siamese-DNN face verification system, and show its effectiveness when the system is trained on VGGFace2 dataset and tested on LFW and YTF datasets. According to our experiments, the Master Key backdoor attack provides a high attack success rate even when the ratio of poisoned training data is as small as 0.01, thus raising a new alarm regarding the use of DNN-based face verification systems in security-critical applications.
Lite-FPN for Keypoint-based Monocular 3D Object Detection
Venue: arXiv cs.CV 2021
Year: 2021

3D object detection with a single image is an essential and challenging task for autonomous driving. Recently, keypoint-based monocular 3D object detection has made tremendous progress and achieved great speed-accuracy trade-off. However, there still exists a huge gap with LIDAR-based methods in terms of accuracy. To improve their performance without sacrificing efficiency, we propose a sort of lightweight feature pyramid network called Lite-FPN to achieve multi-scale feature fusion in an effective and efficient way, which can boost the multi-scale detection capability of keypoint-based detectors. Besides, the misalignment between classification score and localization precision is further relieved by introducing a novel regression loss named attention loss. With the proposed loss, predictions with high confidence but poor localization are treated with more attention during the training phase. Comparative experiments based on several state-of-the-art keypoint-based detectors on the KITTI dataset show that our proposed methods manage to achieve significant improvements in both accuracy and frame rate. The code and pretrained models will be released at \url{https://github.com/yanglei18/Lite-FPN}.
A Single-Layer Asymmetric RNN: Potential Low Hardware Complexity Linear Equation Solver
Venue: arXiv cs.NE 2021
Year: 2021

A single layer neural network for the solution of linear equations is presented. The proposed circuit is based on the standard Hopfield model albeit with the added flexibility that the interconnection weight matrix need not be symmetric. This results in an asymmetric Hopfield neural network capable of solving linear equations. PSPICE simulation results are given which verify the theoretical predictions. Experimental results for circuits set up to solve small problems further confirm the operation of the proposed circuit.
Online Domination: The Value of Getting to Know All your Neighbors
Venue: arXiv cs.DS 2021
Year: 2021

We study the dominating set problem in an online setting. An algorithm is required to guarantee competitiveness against an adversary that reveals the input graph one node at a time. When a node is revealed, the algorithm learns about the entire neighborhood of the node (including those nodes that have not yet been revealed). Furthermore, the adversary is required to keep the revealed portion of the graph connected at all times. We present an algorithm that achieves 2-competitiveness on trees and prove that this competitive ratio cannot be improved by any other algorithm. We also present algorithms that achieve 2.5-competitiveness on cactus graphs, $(t-1)$-competitiveness on $K_{1,t}$-free graphs, and $Θ(\sqrtΔ)$ for maximum degree $Δ$ graphs. We show that all of those competitive ratios are tight. Then, we study several more general classes of graphs, such as threshold, bipartite planar, and series-parallel graphs, and show that they do not admit competitive algorithms (that is, when competitive ratio is independent of the input size). Previously, the dominating set problem was considered in a slightly different input model, where a vertex is revealed alongside its restricted neighborhood: those neighbors that are among already revealed vertices. Thus, conceptually, our results quantify the value of knowing the entire neighborhood at the time a vertex is revealed as compared to the restricted neighborhood. For instance, it was known in the restricted neighborhood model that 3-competitiveness is optimal for trees, whereas knowing the neighbors allows us to improve it to 2-competitiveness.
Online Fashion Commerce: Modelling Customer Promise Date
Venue: arXiv cs.LG 2021
Year: 2021

In the e-commerce space, accurate prediction of delivery dates plays a major role in customer experience as well as in optimizing the supply chain operations. Predicting a date later than the actual delivery date might sometimes result in the customer not placing the order (lost sales) while promising a date earlier than the actual delivery date would lead to a bad customer experience and consequent customer churn. In this paper, we present a machine learning-based approach for penalizing incorrect predictions differently using non-conventional loss functions, while working under various uncertainties involved in making successful deliveries such as traffic disruptions, weather conditions, supply chain, and logistics. We examine statistical, deep learning, and conventional machine learning approaches, and we propose an approach that outperformed the pre-existing rule-based models. The proposed model is deployed internally for Fashion e-Commerce and is operational.
Privacy and Integrity Preserving Training Using Trusted Hardware
Venue: arXiv cs.CR 2021
Year: 2021

Privacy and security-related concerns are growing as machine learning reaches diverse application domains. The data holders want to train with private data while exploiting accelerators, such as GPUs, that are hosted in the cloud. However, Cloud systems are vulnerable to attackers that compromise the privacy of data and integrity of computations. This work presents DarKnight, a framework for large DNN training while protecting input privacy and computation integrity. DarKnight relies on cooperative execution between trusted execution environments (TEE) and accelerators, where the TEE provides privacy and integrity verification, while accelerators perform the computation heavy linear algebraic operations.
Binarized Aggregated Network with Quantization: Flexible Deep Learning Deployment for CSI Feedback in Massive MIMO System
Venue: arXiv cs.IT 2021
Year: 2021

Massive multiple-input multiple-output (MIMO) is one of the key techniques to achieve better spectrum and energy efficiency in 5G system. The channel state information (CSI) needs to be fed back from the user equipment to the base station in frequency division duplexing (FDD) mode. However, the overhead of the direct feedback is unacceptable due to the large antenna array in massive MIMO system. Recently, deep learning is widely adopted to the compressed CSI feedback task and proved to be effective. In this paper, a novel network named aggregated channel reconstruction network (ACRNet) is designed to boost the feedback performance with network aggregation and parametric rectified linear unit (PReLU) activation. The practical deployment of the feedback network in the communication system is also considered. Specifically, the elastic feedback scheme is proposed to flexibly adapt the network to meet different resource limitations. Besides, the network binarization technique is combined with the feature quantization for lightweight and practical deployment. Experiments show that the proposed ACRNet outperforms loads of previous state-of-the-art networks, providing a neat feedback solution with high performance, low cost and impressive flexibility.
RADDet: Range-Azimuth-Doppler based Radar Object Detection for Dynamic Road Users
Venue: arXiv cs.CV 2021
Year: 2021

Object detection using automotive radars has not been explored with deep learning models in comparison to the camera based approaches. This can be attributed to the lack of public radar datasets. In this paper, we collect a novel radar dataset that contains radar data in the form of Range-Azimuth-Doppler tensors along with the bounding boxes on the tensor for dynamic road users, category labels, and 2D bounding boxes on the Cartesian Bird-Eye-View range map. To build the dataset, we propose an instance-wise auto-annotation method. Furthermore, a novel Range-Azimuth-Doppler based multi-class object detection deep learning model is proposed. The algorithm is a one-stage anchor-based detector that generates both 3D bounding boxes and 2D bounding boxes on Range-Azimuth-Doppler and Cartesian domains, respectively. Our proposed algorithm achieves 56.3% AP with IOU of 0.3 on 3D bounding box predictions, and 51.6% with IOU of 0.5 on 2D bounding box prediction. Our dataset and the code can be found at https://github.com/ZhangAoCanada/RADDet.git.