OpenRaiser/Intern-Atlas huggingface dataset:
DP-PSI: Private and Secure Set Intersection
Venue: arXiv cs.CR 2022
Year: 2022

One way to classify private set intersection (PSI) for secure 2-party computation is whether the intersection is (a) revealed to both parties or (b) hidden from both parties while only the computing function of the matched payload is exposed. Both aim to provide cryptographic security while avoiding exposing the unmatched elements of the other. They may, however, be insufficient to achieve security and privacy in one practical scenario: when the intersection is required and the information leaked through the function's output must be considered for legal, ethical, and competitive reasons. Two parties, such as the advertiser and the ads supplier, hold sets of users for PSI computation, for example, to reveal common users to the ads supplier in joint marketing applications. In addition to the security guarantees required by standard PSIs to secure unmatched elements, neither party is allowed to "single out" whether an element/user belongs to the other party or not, even though common users are required for joint advertising. This is a fascinating problem for which none of the PSI techniques have provided a solution. In light of this shortcoming, we compose differential privacy (DP) and S2PC to provide the best of both worlds and propose differentially-private PSI (DP-PSI), a new privacy model that shares PSI's strong security protection while adhering to the GDPR's recent formalization of the notion of excluding "signaling out" attacks by each party except with very low probability.
Domain Adaptation Principal Component Analysis: base linear method for learning with out-of-distribution data
Venue: arXiv cs.LG 2022
Year: 2022

Domain adaptation is a popular paradigm in modern machine learning which aims at tackling the problem of divergence (or shift) between the labeled training and validation datasets (source domain) and a potentially large unlabeled dataset (target domain). The task is to embed both datasets red into a common space in which the source dataset is informative for training while the divergence between source and target is minimized. The most popular domain adaptation solutions are based on training neural networks that combine classification and adversarial learning modules, frequently making them both data-hungry and difficult to train. We present a method called Domain Adaptation Principal Component Analysis (DAPCA) that identifies a linear reduced data representation useful for solving the domain adaptation task. DAPCA algorithm introduces positive and negative weights between pairs of data points, and generalizes the supervised extension of principal component analysis. DAPCA is an iterative algorithm that solves a simple quadratic optimization problem at each iteration. The convergence of the algorithm is guaranteed, and the number of iterations is small in practice. We validate the suggested algorithm on previously proposed benchmarks for solving the domain adaptation task. We also show the benefit of using DAPCA in analyzing the single-cell omics datasets in biomedical applications. Overall, DAPCA can serve as a practical preprocessing step in many machine learning applications leading to reduced dataset representations, taking into account possible divergence between source and target domains.
Artificial Neural Networks for Finger Vein Recognition: A Survey
Venue: arXiv cs.CV 2022
Year: 2022

Finger vein recognition is an emerging biometric recognition technology. Different from the other biometric features on the body surface, the venous vascular tissue of the fingers is buried deep inside the skin. Due to this advantage, finger vein recognition is highly stable and private. They are almost impossible to be stolen and difficult to interfere with by external conditions. Unlike the finger vein recognition methods based on traditional machine learning, the artificial neural network technique, especially deep learning, it without relying on feature engineering and have superior performance. To summarize the development of finger vein recognition based on artificial neural networks, this paper collects 149 related papers. First, we introduce the background of finger vein recognition and the motivation of this survey. Then, the development history of artificial neural networks and the representative networks on finger vein recognition tasks are introduced. The public datasets that are widely used in finger vein recognition are then described. After that, we summarize the related finger vein recognition tasks based on classical neural networks and deep neural networks, respectively. Finally, the challenges and potential development directions in finger vein recognition are discussed. To our best knowledge, this paper is the first comprehensive survey focusing on finger vein recognition based on artificial neural networks.
Stag hunt game-based approach for cooperative UAVs
Venue: arXiv cs.RO 2022
Year: 2022

Unmanned aerial vehicles (UAVs) are being employed in many areas such as photography, emergency, entertainment, defence, agriculture, forestry, mining and construction. Over the last decade, UAV technology has found applications in numerous construction project phases, ranging from site mapping, progress monitoring, building inspection, damage assessments, and material delivery. While extensive studies have been conducted on the advantages of UAVs for various construction-related processes, studies on UAV collaboration to improve the task capacity and efficiency are still scarce. This paper proposes a new cooperative path planning algorithm for multiple UAVs based on the stag hunt game and particle swarm optimization (PSO). First, a cost function for each UAV is defined, incorporating multiple objectives and constraints. The UAV game framework is then developed to formulate the multi-UAV path planning into the problem of finding payoff-dominant equilibrium. Next, a PSO-based algorithm is proposed to obtain optimal paths for the UAVs. Simulation results for a large construction site inspected by three UAVs indicate the effectiveness of the proposed algorithm in generating feasible and efficient flight paths for UAV formation during the inspection task.
Improving the Efficiency of OpenCL Kernels through Pipes
Venue: arXiv cs.DC 2022
Year: 2022

In an effort to lower the barrier to the adoption of FPGAs by a broader community, today major FPGA vendors offer compiler toolchains for OpenCL code. While using these toolchain allows porting existing code to FPGAs, ensuring performance portability across devices (i.e., CPUs, GPUs and FPGAs) is not a trivial task. This is in part due to the different hardware characteristics of these devices, including the nature of the hardware parallelism and the memory bandwidth they offer. In particular, global memory accesses are known to be one of the main performance bottlenecks for OpenCL kernels deployed on FPGA. In this paper, we investigate the use of pipes to improve memory bandwidth utilization and performance of OpenCL kernels running on FPGA. This is done by separating the global memory accesses from the computation, enabling better use of the load units required to access global memory. We perform experiments on a set of broadly used benchmark applications with various compute and memory access patterns. Our experiments, conducted on an Intel Arria GX board, show that the proposed method is effective in improving the memory bandwidth utilization of most kernels, particularly those exhibiting irregular memory access patterns. This, in turn, leads to performance improvements, in some cases significant.
Affective Manifolds: Modeling Machine's Mind to Like, Dislike, Enjoy, Suffer, Worry, Fear, and Feel Like A Human
Venue: arXiv cs.LG 2022
Year: 2022

After the development of different machine learning and manifold learning algorithms, it may be a good time to put them together to make a powerful mind for machine. In this work, we propose affective manifolds as components of a machine's mind. Every affective manifold models a characteristic group of mind and contains multiple states. We define the machine's mind as a set of affective manifolds. We use a learning model for mapping the input signals to the embedding space of affective manifold. Using this mapping, a machine or a robot takes an input signal and can react emotionally to it. We use deep metric learning, with Siamese network, and propose a loss function for affective manifold learning. We define margins between states based on the psychological and philosophical studies. Using triplets of instances, we train the network to minimize the variance of every state and have the desired distances between states. We show that affective manifolds can have various applications for machine-machine and human-machine interactions. Some simulations are also provided for verification of the proposed method. It is possible to have as many affective manifolds as required in machine's mind. More affective manifolds in the machine's mind can make it more realistic and effective. This paper opens the door; we invite the researchers from various fields of science to propose more affective manifolds to be inserted in machine's mind.
Towards Explaining Demographic Bias through the Eyes of Face Recognition Models
Venue: arXiv cs.CV 2022
Year: 2022

Biases inherent in both data and algorithms make the fairness of widespread machine learning (ML)-based decision-making systems less than optimal. To improve the trustfulness of such ML decision systems, it is crucial to be aware of the inherent biases in these solutions and to make them more transparent to the public and developers. In this work, we aim at providing a set of explainability tool that analyse the difference in the face recognition models' behaviors when processing different demographic groups. We do that by leveraging higher-order statistical information based on activation maps to build explainability tools that link the FR models' behavior differences to certain facial regions. The experimental results on two datasets and two face recognition models pointed out certain areas of the face where the FR models react differently for certain demographic groups compared to reference groups. The outcome of these analyses interestingly aligns well with the results of studies that analyzed the anthropometric differences and the human judgment differences on the faces of different demographic groups. This is thus the first study that specifically tries to explain the biased behavior of FR models on different demographic groups and link it directly to the spatial facial features. The code is publicly available here.
PV-RCNN++: Semantical Point-Voxel Feature Interaction for 3D Object Detection
Venue: arXiv cs.CV 2022
Year: 2022

Large imbalance often exists between the foreground points (i.e., objects) and the background points in outdoor LiDAR point clouds. It hinders cutting-edge detectors from focusing on informative areas to produce accurate 3D object detection results. This paper proposes a novel object detection network by semantical point-voxel feature interaction, dubbed PV-RCNN++. Unlike most of existing methods, PV-RCNN++ explores the semantic information to enhance the quality of object detection. First, a semantic segmentation module is proposed to retain more discriminative foreground keypoints. Such a module will guide our PV-RCNN++ to integrate more object-related point-wise and voxel-wise features in the pivotal areas. Then, to make points and voxels interact efficiently, we utilize voxel query based on Manhattan distance to quickly sample voxel-wise features around keypoints. Such the voxel query will reduce the time complexity from O(N) to O(K), compared to the ball query. Further, to avoid being stuck in learning only local features, an attention-based residual PointNet module is designed to expand the receptive field to adaptively aggregate the neighboring voxel-wise features into keypoints. Extensive experiments on the KITTI dataset show that PV-RCNN++ achieves 81.60$\%$, 40.18$\%$, 68.21$\%$ 3D mAP on Car, Pedestrian, and Cyclist, achieving comparable or even better performance to the state-of-the-arts.
Mining Android API Usage to Generate Unit Test Cases for Pinpointing Compatibility Issues
Venue: arXiv cs.SE 2022
Year: 2022

Despite being one of the largest and most popular projects, the official Android framework has only provided test cases for less than 30% of its APIs. Such a poor test case coverage rate has led to many compatibility issues that can cause apps to crash at runtime on specific Android devices, resulting in poor user experiences for both apps and the Android ecosystem. To mitigate this impact, various approaches have been proposed to automatically detect such compatibility issues. Unfortunately, these approaches have only focused on detecting signature-induced compatibility issues (i.e., a certain API does not exist in certain Android versions), leaving other equally important types of compatibility issues unresolved. In this work, we propose a novel prototype tool, JUnitTestGen, to fill this gap by mining existing Android API usage to generate unit test cases. After locating Android API usage in given real-world Android apps, JUnitTestGen performs inter-procedural backward data-flow analysis to generate a minimal executable code snippet (i.e., test case). Experimental results on thousands of real-world Android apps show that JUnitTestGen is effective in generating valid unit test cases for Android APIs. We show that these generated test cases are indeed helpful for pinpointing compatibility issues, including ones involving semantic code changes.
Light-YOLOv5: A Lightweight Algorithm for Improved YOLOv5 in Complex Fire Scenarios
Venue: arXiv cs.CV 2022
Year: 2022

Fire-detection technology is of great importance for successful fire-prevention measures. Image-based fire detection is one effective method. At present, object-detection algorithms are deficient in performing detection speed and accuracy tasks when they are applied in complex fire scenarios. In this study, a lightweight fire-detection algorithm, Light-YOLOv5 (You Only Look Once version five), is presented. First, a separable vision transformer (SepViT) block is used to replace several C3 modules in the final layer of a backbone network to enhance both the contact of the backbone network to global in-formation and the extraction of flame and smoke features; second, a light bidirectional feature pyramid network (Light-BiFPN) is designed to lighten the model while improving the feature extraction and balancing speed and accuracy features during a fire-detection procedure; third, a global attention mechanism (GAM) is fused into the network to cause the model to focus more on the global dimensional features and further improve the detection accuracy of the model; and finally, the Mish activation function and SIoU loss are utilized to simultaneously increase the convergence speed and enhance the accuracy. The experimental results show that compared to the original algorithm, the mean average accuracy (mAP) of Light-YOLOv5 increases by 3.3%, the number of parameters decreases by 27.1%, and the floating point operations (FLOPs) decrease by 19.1%. The detection speed reaches 91.1 FPS, which can detect targets in complex fire scenarios in real time.
Common Problems and Effects of Feedback on Fun When Programming Ozobots in Primary School
Venue: arXiv cs.SE 2022
Year: 2022

Computational thinking is increasingly introduced at primary school level, usually with some form of programming activity. In particular, educational robots provide an opportunity for engaging students with programming through hands-on experiences. However, primary school teachers might not be adequately prepared for teaching computer science related topics, and giving feedback to students can often be challenging: Besides the content of the feedback (e.g., what problems have to be handled), the way the feedback is given is also important, as it can lead to negative emotional effects. To support teachers with the way of giving feedback on common problems when teaching programming with robotics, we conducted a study consisting of seven workshops with three third and four fourth grade primary school classes. Within seven different activities, the 116 primary school children first programmed the Ozobot Evo robot in the pen-and-paper mode and then on a digital device. Throughout these activities we collected data on the problems the students encountered, the feedback given, and the fun they experienced. Our analysis reveals eight categories of problems, which we summarise in this paper together with corresponding possible feedback. We observed that problems that are urgent or can harm the students' self-efficacy have a negative impact on how enjoyable an activity is perceived. While direct instruction significantly decreased the experienced fun, hints had a positive effect. Generally, we found programming the Ozobot Evo to be encouraging for both girls and boys. To support teachers, we discuss ideas for giving encouraging feedback on common problems of Ozobot Evo programming activities and how our findings transfer to other robots.
How to Extend the Abstraction Refinement Model for Systems with Emergent Behavior ?
Venue: arXiv cs.SE 2022
Year: 2022

The Abstraction Refinement Model has been widely adopted since it was firstly proposed many decades ago. This powerful model of software evolution process brings important properties into the system under development, properties such as the guarantee that no extra behavior (specifically harmful behaviors) will be observed once the system is deployed. However, perfect systems with such a guarantee are not a common thing to find in real world cases, anomalies and unspecified behaviors will always find a way to manifest in our systems, behaviors that are addressed in this paper with the name "emergent behavior". In this paper, we extend the Abstract Refinement Model to include the concept of the emergent behavior. Eventually, this should enable system developers to: (i) Concretely define what an emergent behavior is, (ii) help reason about the potential sources of the emergent behavior along the development process, which in return will help in controlling the emergent behavior at early steps of the development process.
LogicRank: Logic Induced Reranking for Generative Text-to-Image Systems
Venue: arXiv cs.AI 2022
Year: 2022

Text-to-image models have recently achieved remarkable success with seemingly accurate samples in photo-realistic quality. However as state-of-the-art language models still struggle evaluating precise statements consistently, so do language model based image generation processes. In this work we showcase problems of state-of-the-art text-to-image models like DALL-E with generating accurate samples from statements related to the draw bench benchmark. Furthermore we show that CLIP is not able to rerank those generated samples consistently. To this end we propose LogicRank, a neuro-symbolic reasoning framework that can result in a more accurate ranking-system for such precision-demanding settings. LogicRank integrates smoothly into the generation process of text-to-image models and moreover can be used to further fine-tune towards a more logical precise model.
On the lack of women researchers in the Middle East & North Africa
Venue: arXiv cs.DL 2022
Year: 2022

Recent gender policies in the Middle East and North Africa (MENA) region have improved legal equality for women with noticeable effects in some countries. The implications of these policies on science, however, is not well-understood. This study applies a bibliometric lens to describe the landscape of gender disparities in scientific research in MENA. Specifically, we examine 1.7 million papers indexed in the Web of Science published by 1.1 million authors from MENA between 2008 and 2020. We used bibliometric indicators to analyse potential disparities between men and women in the share of authors, research productivity, and seniority in authorship. The results show that gender parity is far from being achieved in MENA. Overall, men authors obtain higher representation, research productivity, and seniority. But some countries standout: Tunisia, Lebanon, Turkey, Algeria and Egypt have higher shares or women researchers compared to the rest of MENA countries. The UAE, Qatar, and Jordan have shown progress in terms of women participation in science, but Saudi Arabia lags behind. We find that women are more likely to stop publishing than men and that men publish on average between 11% and 51% more than women, with this gap increasing over time. Finally, men, on average, achieved senior positions in authorship faster than women. Our longitudinal study contributes to a better understanding of gender disparities in science in MENA which is catching up in terms of policy engagement and women representation. However, the results suggest that the effects of the policy changes have yet to materialize into distinct improvement in women's participation and performance in science.
Demystifying the COVID-19 vaccine discourse on Twitter
Venue: arXiv cs.SI 2022
Year: 2022

Developing an understanding of the public discourse on COVID-19 vaccination on social media is important not only for addressing the current COVID-19 pandemic, but also for future pathogen outbreaks. We examine a Twitter dataset containing 75 million English tweets discussing COVID-19 vaccination from March 2020 to March 2021. We train a stance detection algorithm using natural language processing (NLP) techniques to classify tweets as `anti-vax' or `pro-vax', and examine the main topics of discourse using topic modelling techniques. While pro-vax tweets (37 million) far outnumbered anti-vax tweets (10 million), a majority of tweets from both stances (63% anti-vax and 53% pro-vax tweets) came from dual-stance users who posted both pro- and anti-vax tweets during the observation period. Pro-vax tweets focused mostly on vaccine development, while anti-vax tweets covered a wide range of topics, some of which included genuine concerns, though there was a large dose of falsehoods. A number of topics were common to both stances, though pro- and anti-vax tweets discussed them from opposite viewpoints. Memes and jokes were amongst the most retweeted messages. Whereas concerns about polarisation and online prevalence of anti-vax discourse are unfounded, targeted countering of falsehoods is important.