OpenRaiser/Intern-Atlas huggingface dataset:
GlocalFuse-Depth: Fusing Transformers and CNNs for All-day Self-supervised Monocular Depth Estimation
Venue: arXiv cs.CV 2023
Year: 2023

In recent years, self-supervised monocular depth estimation has drawn much attention since it frees of depth annotations and achieved remarkable results on standard benchmarks. However, most of existing methods only focus on either daytime or nighttime images, thus their performance degrades on the other domain because of the large domain shift between daytime and nighttime images. To address this problem, in this paper we propose a two-branch network named GlocalFuse-Depth for self-supervised depth estimation of all-day images. The daytime and nighttime image in input image pair are fed into the two branches: CNN branch and Transformer branch, respectively, where both fine-grained details and global dependency can be efficiently captured. Besides, a novel fusion module is proposed to fuse multi-dimensional features from the two branches. Extensive experiments demonstrate that GlocalFuse-Depth achieves state-of-the-art results for all-day images on the Oxford RobotCar dataset, which proves the superiority of our method.
Fair $k$-Center: a Coreset Approach in Low Dimensions
Venue: arXiv cs.DC 2023
Year: 2023

Center-based clustering techniques are fundamental in some areas of machine learning such as data summarization. Generic $k$-center algorithms can produce biased cluster representatives so there has been a recent interest in fair $k$-center clustering. Our main theoretical contributions are two new $(3+ε)$-approximation algorithms for solving the fair $k$-center problem in (1) the dynamic incremental, i.e., one-pass streaming, model and (2) the MapReduce model. Our dynamic incremental algorithm is the first such algorithm for this problem (previous streaming algorithms required two passes) and our MapReduce one improves upon the previous approximation factor of $(17+ε).$ Both algorithms work by maintaining a small coreset to represent the full point set and their analysis requires that the underlying metric has finite-doubling dimension. We also provide related heuristics for higher dimensional data and experimental results that compare the performance of our algorithms to existing ones.
A Text Mining Analysis of Data Protection Politics: The Case of Plenary Sessions of the European Parliament
Venue: arXiv cs.CY 2023
Year: 2023

Data protection laws and policies have been studied extensively in recent years, but little is known about the parliamentary politics of data protection. This imitation applies even to the European Union (EU) that has taken the global lead in data protection and privacy regulation. For patching this notable gap in existing research, this paper explores the data protection questions raised by the Members of the European Parliament (MEPs) in the Parliament's plenary sessions and the answers given to these by the European Commission. Over a thousand of such questions and answers are covered in a period from 1995 to early 2023. Given computational analysis based on text mining, the results indicate that (a) data protection has been actively debated in the Parliament during the past twenty years. No noticeable longitudinal trends are present; the debates have been relatively constant. As could be expected, (b) the specific data protection laws in the EU have frequently been referenced in these debates, which (c) do not seem to align along conventional political dimensions such as the left-right axis. Furthermore, (d) numerous distinct data protection topics have been debated by the parliamentarians, indicating that data protection politics in the EU go well-beyond the recently enacted regulations.
Digital identity architectures: comparing goals and vulnerabilities
Venue: arXiv cs.CR 2023
Year: 2023

Digital identity systems have the promise of efficiently facilitating access to services for a nation's citizens while increasing security and convenience. There are many possible system architectures, each with strengths and weaknesses that should be carefully considered. This report first establishes a set of goals and vulnerabilities faced by any identity system, then evaluates the trade-offs of common digital identity architectures, principally comparing centralised and decentralised systems.
Deep Vision in Analysis and Recognition of Radar Data: Achievements, Advancements and Challenges
Venue: arXiv cs.CV 2023
Year: 2023

Radars are widely used to obtain echo information for effective prediction, such as precipitation nowcasting. In this paper, recent relevant scientific investigation and practical efforts using Deep Learning (DL) models for weather radar data analysis and pattern recognition have been reviewed; particularly, in the fields of beam blockage correction, radar echo extrapolation, and precipitation nowcast. Compared to traditional approaches, present DL methods depict better performance and convenience but suffer from stability and generalization. In addition to recent achievements, the latest advancements and existing challenges are also presented and discussed in this paper, trying to lead to reasonable potentials and trends in this highly-concerned field.
Selectively Providing Reliance Calibration Cues With Reliance Prediction
Venue: arXiv cs.AI 2023
Year: 2023

For effective collaboration between humans and intelligent agents that employ machine learning for decision-making, humans must understand what agents can and cannot do to avoid over/under-reliance. A solution to this problem is adjusting human reliance through communication using reliance calibration cues (RCCs) to help humans assess agents' capabilities. Previous studies typically attempted to calibrate reliance by continuously presenting RCCs, and when an agent should provide RCCs remains an open question. To answer this, we propose Pred-RC, a method for selectively providing RCCs. Pred-RC uses a cognitive reliance model to predict whether a human will assign a task to an agent. By comparing the prediction results for both cases with and without an RCC, Pred-RC evaluates the influence of the RCC on human reliance. We tested Pred-RC in a human-AI collaboration task and found that it can successfully calibrate human reliance with a reduced number of RCCs.
Stable Motion Primitives via Imitation and Contrastive Learning
Venue: arXiv cs.RO 2023
Year: 2023

Learning from humans allows non-experts to program robots with ease, lowering the resources required to build complex robotic solutions. Nevertheless, such data-driven approaches often lack the ability to provide guarantees regarding their learned behaviors, which is critical for avoiding failures and/or accidents. In this work, we focus on reaching/point-to-point motions, where robots must always reach their goal, independently of their initial state. This can be achieved by modeling motions as dynamical systems and ensuring that they are globally asymptotically stable. Hence, we introduce a novel Contrastive Learning loss for training Deep Neural Networks (DNN) that, when used together with an Imitation Learning loss, enforces the aforementioned stability in the learned motions. Differently from previous work, our method does not restrict the structure of its function approximator, enabling its use with arbitrary DNNs and allowing it to learn complex motions with high accuracy. We validate it using datasets and a real robot. In the former case, motions are 2 and 4 dimensional, modeled as first- and second-order dynamical systems. In the latter, motions are 3, 4, and 6 dimensional, of first and second order, and are used to control a 7DoF robot manipulator in its end effector space and joint space. More details regarding the real-world experiments are presented in: \url{https://youtu.be/OM-2edHBRfc}.
Extending Orthogonal Planar Graph Drawings is Fixed-Parameter Tractable
Venue: arXiv cs.CG 2023
Year: 2023

The task of finding an extension to a given partial drawing of a graph while adhering to constraints on the representation has been extensively studied in the literature, with well-known results providing efficient algorithms for fundamental representations such as planar and beyond-planar topological drawings. In this paper, we consider the extension problem for bend-minimal orthogonal drawings of planar connected graphs, which is among the most fundamental geometric graph drawing representations. While the problem was known to be \NP-hard, it is natural to consider the case where only a small part of the graph is still to be drawn. Here, we establish the fixed-parameter tractability of the problem when parameterized by the size of the missing subgraph. Our algorithm is based on multiple novel ingredients which intertwine geometric and combinatorial arguments. These include the identification of a new graph representation of bend-equivalent regions for vertex placement in the plane, establishing a bound on the treewidth of this auxiliary graph, and a global point-grid that allows us to discretize the possible placement of bends and vertices into locally bounded subgrids for each of the above regions.
Learning Deep Semantics for Test Completion
Venue: arXiv cs.SE 2023
Year: 2023

Writing tests is a time-consuming yet essential task during software development. We propose to leverage recent advances in deep learning for text and code generation to assist developers in writing tests. We formalize the novel task of test completion to automatically complete the next statement in a test method based on the context of prior statements and the code under test. We develop TeCo -- a deep learning model using code semantics for test completion. The key insight underlying TeCo is that predicting the next statement in a test method requires reasoning about code execution, which is hard to do with only syntax-level data that existing code completion models use. TeCo extracts and uses six kinds of code semantics data, including the execution result of prior statements and the execution context of the test method. To provide a testbed for this new task, as well as to evaluate TeCo, we collect a corpus of 130,934 test methods from 1,270 open-source Java projects. Our results show that TeCo achieves an exact-match accuracy of 18, which is 29% higher than the best baseline using syntax-level data only. When measuring functional correctness of generated next statement, TeCo can generate runnable code in 29% of the cases compared to 18% obtained by the best baseline. Moreover, TeCo is significantly better than prior work on test oracle generation.
Compressed Error HARQ: Feedback Communication on Noise-Asymmetric Channels
Venue: arXiv cs.IT 2023
Year: 2023

In modern communication systems with feedback, there are increasingly more scenarios where the transmitter has much less power than the receiver (e.g., medical implant devices), which we refer to as noise-asymmetric channels. For such channels, the feedback link is of higher quality than the forward link. However, feedback schemes for cellular communications, such as hybrid ARQ, do not fully utilize the high-quality feedback link. To this end, we introduce Compressed Error Hybrid ARQ, a generalization of hybrid ARQ tailored for noise-asymmetric channels; the receiver sends its estimated message to the transmitter, and the transmitter harmoniously switches between hybrid ARQ and compressed error retransmission. We show that our proposed method significantly improves reliability, latency, and spectral efficiency compared to the conventional hybrid ARQ in various practical scenarios where the transmitter is resource-constrained.
Exploring the Effect of Multi-step Ascent in Sharpness-Aware Minimization
Venue: arXiv cs.LG 2023
Year: 2023

Recently, Sharpness-Aware Minimization (SAM) has shown state-of-the-art performance by seeking flat minima. To minimize the maximum loss within a neighborhood in the parameter space, SAM uses an ascent step, which perturbs the weights along the direction of gradient ascent with a given radius. While single-step or multi-step can be taken during ascent steps, previous studies have shown that multi-step ascent SAM rarely improves generalization performance. However, this phenomenon is particularly interesting because the multi-step ascent is expected to provide a better approximation of the maximum neighborhood loss. Therefore, in this paper, we analyze the effect of the number of ascent steps and investigate the difference between both single-step ascent SAM and multi-step ascent SAM. We identify the effect of the number of ascent on SAM optimization and reveal that single-step ascent SAM and multi-step ascent SAM exhibit distinct loss landscapes. Based on these observations, we finally suggest a simple modification that can mitigate the inefficiency of multi-step ascent SAM.
Multivariate Systemic Risk Measures and Computation by Deep Learning Algorithms
Venue: arXiv cs.LG 2023
Year: 2023

In this work we propose deep learning-based algorithms for the computation of systemic shortfall risk measures defined via multivariate utility functions. We discuss the key related theoretical aspects, with a particular focus on the fairness properties of primal optima and associated risk allocations. The algorithms we provide allow for learning primal optimizers, optima for the dual representation and corresponding fair risk allocations. We test our algorithms by comparison to a benchmark model, based on a paired exponential utility function, for which we can provide explicit formulas. We also show evidence of convergence in a case for which explicit formulas are not available.
Theoretical limits of Virtual Reality
Venue: arXiv cs.HC 2023
Year: 2023

In recent years there has been a strong development of the concept of virtual reality (VR) which from the first video games developed in the 60s has reached the current immersive systems. VR and the consequent deception of perception pose an interesting question: is it possible to deceive the mind to the point of not being able to recognize whether the perceived reality is real or simulated? In addition to this question, another question arises spontaneously: is it possible to simulate a non-reality in which the physical laws do not apply? The answer to the first question is that it would theoretically be possible to deceive the mind to the point of not being able to recognize whether the perceived reality is real or simulated, furthermore it is also possible to simulate a non-real, i.e. magical, world. However, this possibility is based on hiding degrees of freedom from the observer, therefore it requires the complicity of the observer to be realised. It can be said that a VR system can simulate both a real and non-real experience. For an observer, a virtual reality experience is still an experience of reality. This experience can be exchanged for a different experience, that is, for a different reality that can be real or not real.
Nonlinear response of Silicon Photonics microresonators for reservoir computing neural network
Venue: arXiv cs.ET 2023
Year: 2023

Nowadays, Information Photonics is extensively studied and sees applications in many fields. The interest in this breakthrough technology is mainly stimulated by the possibility of achieving real-time data processing for high-bandwidth applications, still implemented through small-footprint devices that would allow for breaking the limit imposed by Moore's law. One potential breakthrough implementation of information photonics is via integrated photonic circuits. Within this approach, the most suitable computational scheme is achieved by integrated photonic neural networks. In this chapter, we provide a review of one possible way to implement a neural network by using silicon photonics. Specifically, we review the work we performed at the Nanoscience Laboratory of the University of Trento. We present methodologies, results, and future challenges about a delayed complex perceptron for fast data processing, a microring resonator exploiting nonlinear dynamics for a reservoir computing approach, and a microring resonator with the addition of a feedback delay loop for time series processing.
Secrecy Outage Analysis of Energy Harvesting Relay-based Mixed UOWC-RF Network with Multiple Eavesdroppers
Venue: arXiv cs.IT 2023
Year: 2023

This work deals with the physical layer security performance of a dual-hop underwater optical communication (UOWC)-radio frequency (RF) network under the intruding attempts of multiple eavesdroppers via RF links. The intermediate decode and forward relay node between the underwater source and the destination transforms the optical signal into electrical form and re-transmits it to the destination node with the help of harvested energy by the relay from an integrated power beacon within the system. The source-to-relay link (UOWC) follows a mixture exponential generalized Gamma turbulence with pointing error impairments whereas all the remaining links (RF) undergo $κ-μ$ shadowed fading. With regards to the types of intruders, herein two scenarios are considered, i.e., colluding (\textit{Scenario-I}) and non-colluding (\textit{Scenario-II}) eavesdroppers and the analytical expressions of secure outage probability, probability of strictly positive secrecy capacity, and effective secrecy throughput are derived in closed form for each scenario. Furthermore, the impacts of UOWC and RF channel parameters as well as detection techniques on secrecy capacity are demonstrated, and following this a comparison between the two considered scenarios is demonstrated that reveals the collusion between the eavesdroppers imposes the most harmful threat on secrecy throughput but a better secrecy level can be attained adopting diversity at the destination and power beacon nodes along with heterodyne detection rather than intensity modulation and direct detection technique. Finally, all the derived expressions are corroborated via Monte Carlo simulations.
Unsupervised Learning on a DIET: Datum IndEx as Target Free of Self-Supervision, Reconstruction, Projector Head
Venue: arXiv cs.AI 2023
Year: 2023

Costly, noisy, and over-specialized, labels are to be set aside in favor of unsupervised learning if we hope to learn cheap, reliable, and transferable models. To that end, spectral embedding, self-supervised learning, or generative modeling have offered competitive solutions. Those methods however come with numerous challenges \textit{e.g.} estimating geodesic distances, specifying projector architectures and anti-collapse losses, or specifying decoder architectures and reconstruction losses. In contrast, we introduce a simple explainable alternative -- coined \textbf{DIET} -- to learn representations from unlabeled data, free of those challenges. \textbf{DIET} is blatantly simple: take one's favorite classification setup and use the \textbf{D}atum \textbf{I}nd\textbf{E}x as its \textbf{T}arget class, \textit{i.e. each sample is its own class}, no further changes needed. \textbf{DIET} works without a decoder/projector network, is not based on positive pairs nor reconstruction, introduces no hyper-parameters, and works out-of-the-box across datasets and architectures. Despite \textbf{DIET}'s simplicity, the learned representations are of high-quality and often on-par with the state-of-the-art \textit{e.g.} using a linear classifier on top of DIET's learned representation reaches $71.4\%$ on CIFAR100 with a Resnet101, $52.5\%$ on TinyImagenet with a Resnext50.
Thermal Analysis of Malignant Brain Tumors by Employing a Morphological Differentiation-Based Method in Conjunction with Artificial Neural Network
Venue: arXiv cs.CV 2023
Year: 2023

In this study, a morphological differentiation-based method has been introduced which employs temperature distribution on the tissue surface to detect brain tumor's malignancy. According to the common tumor CT scans, two different scenarios have been implemented to describe irregular shape of the malignant tumor. In the first scenario, tumor has been considered as a polygon base prism and in the second one, it has been considered as a star-shaped base prism. By increasing the number of sides of the polygon or wings of the star, degree of the malignancy has been increased. Constant heat generation has been considered for the tumor and finite element analysis has been conducted by the ABAQUS software linked with a PYTHON script on both tumor models to study temperature variations on the top tissue surface. This temperature distribution has been characterized by 10 parameters. In each scenario, 98 sets of these parameters has been used as inputs of a radial basis function neural network (RBFNN) and number of sides or wings has been selected to be the output. The RBFNN has been trained to identify malignancy of tumor based on its morphology. According to the RBFNN results, the proposed method has been capable of differentiating between benign and malignant tumors and estimating the degree of malignancy with high accuracy
Data level and decision level fusion of satellite multi-sensor AOD retrievals for improving PM2.5 estimations, a study on Tehran
Venue: arXiv cs.CV 2023
Year: 2023

One of the techniques for estimating the surface particle concentration with a diameter of fewer than 2.5 micrometers (PM2.5) is using aerosol optical depth (AOD) products. Different AOD products are retrieved from various satellite sensors, like MODIS and VIIRS, by various algorithms, such as Deep Blue and Dark Target. Therefore, they do not have the same accuracy and spatial resolution. Additionally, the weakness of algorithms in AOD retrieval reduces the spatial coverage of products, particularly in cloudy or snowy areas. Consequently, for the first time, the present study investigated the possibility of fusing AOD products from observations of MODIS and VIIRS sensors retrieved by Deep Blue and Dark Target algorithms to estimate PM2.5 more accurately. For this purpose, AOD products were fused by machine learning algorithms using different fusion strategies at two levels: the data level and the decision level. First, the performance of various machine learning algorithms for estimating PM2.5 using AOD data was evaluated. After that, the XGBoost algorithm was selected as the base model for the proposed fusion strategies. Then, AOD products were fused. The fusion results showed that the estimated PM2.5 accuracy at the data level in all three metrics, RMSE, MAE, and R2, was improved (R2=0.64). Despite the simplicity and lower computational cost of the data level fusion method, the spatial coverage did not improve considerably due to eliminating poor quality data through the fusion process. Afterward, the fusion of products at the decision level was followed in eleven scenarios. In this way, the best result was obtained by fusing Deep Blue products of MODIS and VIIRS sensors (R2=0.81)
Deepfake Detection Analyzing Hybrid Dataset Utilizing CNN and SVM
Venue: arXiv cs.CV 2023
Year: 2023

Social media is currently being used by many individuals online as a major source of information. However, not all information shared online is true, even photos and videos can be doctored. Deepfakes have recently risen with the rise of technological advancement and have allowed nefarious online users to replace one face with a computer generated face of anyone they would like, including important political and cultural figures. Deepfakes are now a tool to be able to spread mass misinformation. There is now an immense need to create models that are able to detect deepfakes and keep them from being spread as seemingly real images or videos. In this paper, we propose a new deepfake detection schema using two popular machine learning algorithms.