Miracle-dz/UniFAR-FTUs huggingface dataset:
Adversarial Attacks on Neural Models of Code via Code Difference Reduction

—Deep learning has been widely used to solve various code-based tasks by building deep code models based on a large number of code snippets. However, deep code models are still vulnerable to adversarial attacks. As source code is discrete and has to strictly stick to the grammar and semantics constraints, the adversarial attack techniques in other domains are not applicable. Moreover, the attack techniques speciﬁc to deep code models suffer from the effectiveness issue due to the enormous attack space. In this work, we propose a novel adversarial attack technique (i.e., CODA). Its key idea is to use the code differences between the target input and reference inputs (that have small code differences but different prediction results with the target one) to guide the generation of adversarial examples. It considers both structure differences and identiﬁer differences to preserve the original semantics. Hence, the attack space can be largely reduced as the one constituted by the two kinds of code differences, and thus the attack process can be largely improved by designing corresponding equivalent structure transformations and identiﬁer renaming transformations. Our experiments on 10 deep code models (i.e., two pre-trained models with ﬁve code-based tasks) demonstrate the effectiveness and efﬁciency of CODA, the naturalness of its generated examples, and its capability of defending against attacks after adversarial ﬁne-tuning. For example, CODA improves the state-of-the-art techniques (i.e., CARROT and ALERT) by 79.25% and 72.20% on average in terms of the attack success rate, respectively.
Introduction to Computational Advertising

Web advertising is the primary driving force behind many Web activities, including Internet search as well as publishing of online content by third-party providers. Even though the notion of online advertising barely existed a decade ago, the topic is so complex that it attracts attention of a variety of established scientific disciplines, including computational linguistics, computer science, economics, psychology, and sociology, to name but a few. Consequently, a new discipline --- Computational Advertising --- has emerged, which studies the process of advertising on the Internet from a variety of angles. A successful advertising campaign should be relevant to the immediate user's information need as well as more generally to user's background and personalized interest profile, be economically worthwhile to the advertiser and the intermediaries (e.g., the search engine), as well as be aesthetically pleasant and not detrimental to user experience.
Unmasking the Invisible: Finding Location-Specific Aggregated Air Quality Index with Smartphone-Captured Images

The prevalence and mobility of smartphones make these a widely used tool for environmental health research. However, their potential for determining aggregated air quality index (AQI) based on PM2.5 concentration in specific locations remains largely unexplored in the existing literature. In this paper, we thoroughly examine the challenges associated with predicting location-specific PM2.5 concentration using images taken with smartphone cameras. The focus of our study is on Dhaka, the capital of Bangladesh, due to its significant air pollution levels and the large population exposed to it. Our research involves the development of a Deep Convolutional Neural Network (DCNN), which we train using over a thousand outdoor images taken and annotated. These photos are captured at various locations in Dhaka, and their labels are based on PM2.5 concentration data obtained from the local US consulate, calculated using the NowCast algorithm. Through supervised learning, our model establishes a correlation index during training, enhancing its ability to function as a Picture-based Predictor of PM2.5 Concentration (PPPC). This enables the algorithm to calculate an equivalent daily averaged AQI index from a smartphone image. Unlike, popular overly parameterized models, our model shows resource efficiency since it uses fewer parameters. Furthermore, test results indicate that our model outperforms popular models like ViT and INN, as well as popular CNN-based models such as VGG19, ResNet50, and MobileNetV2, in predicting location-specific PM2.5 concentration. Our dataset is the first publicly available collection that includes atmospheric images and corresponding PM2.5 measurements from Dhaka. Our code and dataset will be made public when publishing the paper.
Relation between single very‐long‐period pulses and volcanic gas emissions at Mt. Asama, Japan

Multiple volcanic observations conducted at Mt. Asama, Japan, provide evidence of a link between single very‐long‐period (VLP) seismic pulses and volcanic gas emissions. SO2 flux measurements were conducted on 2 June 2009, when Mt. Asama was producing ash‐free eruptions with VLP pulses. Gas bursts from a vent at the crater bottom following the VLP pulses provided an excellent opportunity to examine the relation directly. The SO2 emission for each eruption was calculated by integrating high temporal SO2 flux data obtained by the SO2 imaging system and subtracting the contribution from quiescent degassing from fumaroles around the crater bottom. A seismic moment of VLP pulse was estimated by the waveform inversion. We observed seven eruptions and obtained the proportional relation between VLP pulse moment and SO2 emission. The relation determined is consistent with the VLP source model; these observational results are the first report of a quantitative comparison between single VLP pulse moment and volcanic gas emission.
Community Photo Sharing: Motivational and Structural Antecedents

In recent years there has seen a substantial growth of social computing. Content contribution from individual members is critical to the viability of social computing communities, and therefore it is important to understand what drives users to share content with others in such settings. We extend previous literature on user contribution by studying the factors that are associated with users’ photo sharing in a social computing community. We draw on motivation theories as well as on network structural properties analysis, two critical concepts in predicting user behavior in communities. Our results indicate that users with higher commitment to the community and greater structural embeddedness tend to share more, whereas enjoyment shows no effect on the level of sharing. Counter to our expectations, the motivation of self-development was negatively related to photo sharing. Directions for further development of this research, as well as implications for theory and practice are discussed.
VisDrone-CC2021: The Vision Meets Drone Crowd Counting Challenge Results

Crowding counting research evolves quickly by the lever-age of development in deep learning. Many researchers put their efforts into crowd counting tasks and have achieved many significant improvements. However, current datasets still barely satisfy this evolution and high quality evaluation data is urgent. Motivated by high quality and quantity study in crowding counting, we collect a drone-captured dataset formed by 5,468 images(images in RGB and thermal appear in pairs and 2,734 respectively). There are 1,807 pairs of images for training, and 927 pairs for testing. We manually annotate persons with points in each frame. Based on this dataset, we organized the Vision Meets Drone Crowd Counting Challenge(Visdrone-CC2021) in conjunction with the International Conference on Computer Vision (ICCV 2021). Our challenge attracts many researchers to join, which pave the road of speed up the milestone in crowding counting. To summarize the competition, we select the most remarkable algorithms from participants’ sub-missions and provide a detailed analysis of the evaluation results. More information can be found at the website: http://www.aiskyeye. com/.
Achievable Rate-Power Tradeoff in THz SWIPT Systems with Resonant Tunnelling Diodes

—In this paper, we study terahertz (THz) simultaneous wireless information and power transfer (SWIPT) systems. Since coherent information detection is challenging at THz frequencies and Schottky diodes are not usable for THz energy harvesting (EH), we employ unipolar amplitude shift keying (ASK) modulation at the transmitter (TX) and a resonant tunnelling diode (RTD)-based EH circuit at the receiver (RX) to extract both information and power from the received signal. However, the electrical properties of Schottky diodes and RTDs are different, and unlike EH receivers based on a single Schottky diode, an accurate closed-form EH model for RTD-based RXs is not available, yet. In this paper, we model the dependency of the instantaneous RX output power on the instantaneous received power by a non-linear piecewise function, whose parameters are adjusted to ﬁt circuit simulation results. We formulate an optimization problem to maximize the mutual information between the TX and RX signals subject to constraints on the peak amplitude of the transmitted signal and the required average harvested power at the RX. Furthermore, we determine a feasibility condition for the formulated problem, and for high and low required average harvested powers, we derive the achievable information rate numerically and in closed form, respectively. Our simulation results highlight a tradeoff between the information rate and the average harvested power. Finally, we show that this tradeoff is determined by the peak amplitude of the transmitted signal and the maximum instantaneous harvested power for low and high received signal powers, respectively.
The nature and provenance of Golestan loess deposits in northeast Iran

Mineralogical, textural, geochemical, and weathering characteristics of loess deposits in Golestan province of Iran suggest that they are mostly derived from felsic igneous rocks and are related to Quaternary palaeoclimate. Whole‐rock analyses indicate heavy minerals such as zircon, tourmaline and phyllosillicate minerals (e.g. muscovite, chlorite, illite) exert a significant control on the chemical composition. The loess samples display uniform chemical composition, indicative of similar alteration history. Chemical index of alteration suggests a weak to moderate degree of weathering in a felsic source area. Scanning electron micrographs of quartz grains reveal abundant silt‐sized quartz particles, a result of glacial grinding during the Late Pleistocene in Central Asia. Subsequently, these silt particles were transported from Central Asia to their depositional site by wind and paraglacial processes. Local topography of northeast Iran (Alborz Mountains) acted as a major barrier, entrapping the airborne particles on the plains of Golestan province. Copyright © 2012 John Wiley & Sons, Ltd.
Fast and Robust Matching for Multimodal Remote Sensing Image Registration

While image matching has been studied in remote sensing community for decades, matching multimodal data [e.g., optical, light detection and ranging (LiDAR), synthetic aperture radar (SAR), and map] remains a challenging problem because of significant nonlinear intensity differences between such data. To address this problem, we present a novel fast and robust template matching framework integrating local descriptors for multimodal images. First, a local descriptor [such as histogram of oriented gradient (HOG) and local self-similarity (LSS) or speeded-up robust feature (SURF)] is extracted at each pixel to form a pixelwise feature representation of an image. Then, we define a fast similarity measure based on the feature representation using the fast Fourier transform (FFT) in the frequency domain. A template matching strategy is employed to detect correspondences between images. In this procedure, we also propose a novel pixelwise feature representation using orientated gradients of images, which is named channel features of orientated gradients (CFOG). This novel feature is an extension of the pixelwise HOG descriptor with superior performance in image matching and computational efficiency. The major advantages of the proposed matching framework include: 1) structural similarity representation using the pixelwise feature description and 2) high computational efficiency due to the use of FFT. The proposed matching framework has been evaluated using many different types of multimodal images, and the results demonstrate its superior matching performance with respect to the state-of-the-art methods.
Unsupervised Domain Adaption of Object Detectors: A Survey

—Recent advances in deep learning have led to the development of accurate and efﬁcient models for various computer vision applications such as object classiﬁcation, semantic segmentation, and object detection. However, learning highly accurate models relies on the availability of datasets with large number of annotated images. Due to this, model performance drops drastically when evaluated on label-scarce datasets having visually distinct images. This issue is commonly referred to as covariate shift or dataset bias . Domain adaptation attempts to address this problem by leveraging domain shift characteristics from labeled data in a related domain when learning a classiﬁer for label-scarce target dataset. There are a plethora of works to adapt object classiﬁcation and semantic segmentation models to label-scarce target dataset through unsupervised domain adaptation. Considering that object detection is a fundamental task in computer vision, many recent works have recently focused on addressing the domain adaptation issue for object detection as well. In this paper, we provide a brief introduction to the domain adaptation problem for object detection and present an overview of various methods proposed till date for addressing this problem. Furthermore, we highlight strategies proposed for this problem and the associated shortcomings. Subsequently, we identify multiple aspects of the unsupervised domain adaptive detection problem that are most promising for the future research in the area. We believe that this survey shall be valuable to the pattern recognition experts working in the ﬁelds of computer vision, biometrics, medical imaging, and autonomous navigation by introducing them to the problem, getting them familiar with the current status of the progress and providing with promising direction for future research.
Loosely Coupled Odometry, UWB Ranging, and Cooperative Spatial Detection for Relative Monte-Carlo Multi-Robot Localization

—As mobile robots become more ubiquitous, their deployments grow across use cases where GNSS positioning is either unavailable or unreliable. This has led to increased interest in multi-modal relative localization methods. Complementing onboard odometry, ranging allows for relative state estimation, with ultra-wideband (UWB) ranging having gained widespread recognition due to its low cost and centimeter-level out-of-box accuracy. Infrastructure-free localization methods allow for more dynamic, ad-hoc, and ﬂexible deployments, yet they have received less attention from the research community. In this work, we propose a cooperative relative multi-robot localization where we leverage inter-robot ranging and simultaneous spatial detections of objects in the environment. To achieve this, we equip robots with a single UWB transceiver and a stereo camera. We propose a novel Monte-Carlo approach to estimate relative states by either employing only UWB ranges or dynamically integrating simultaneous spatial detections from the stereo cameras. We also address the challenges for UWB ranging error mitigation, especially in non-line-of-sight , with a study on different LSTM networks to estimate the ranging error. The proposed approach has multiple beneﬁts. First, we show that a single range is enough to estimate the accurate relative states of two robots when fusing odometry measurements. Second, our experiments also demonstrate that our approach surpasses traditional methods such as multilateration in terms of accuracy. Third, to increase accuracy even further, we allow for the integration of cooperative spatial detections. Finally, we show how ROS2 and Zenoh can be integrated to build a scalable wireless communication solution for multi-robot systems. The experimental validation includes real-time deployment and autonomous navigation based on the relative positioning method.
Aiding Grasp Synthesis for Novel Objects Using Heuristic-Based and Data-Driven Active Vision Methods

In this work, we present several heuristic-based and data-driven active vision strategies for viewpoint optimization of an arm-mounted depth camera to aid robotic grasping. These strategies aim to efficiently collect data to boost the performance of an underlying grasp synthesis algorithm. We created an open-source benchmarking platform in simulation (https://github.com/galenbr/2021ActiveVision), and provide an extensive study for assessing the performance of the proposed methods as well as comparing them against various baseline strategies. We also provide an experimental study with a real-world two finger parallel jaw gripper setup by utilizing an existing grasp planning benchmark in the literature. With these analyses, we were able to quantitatively demonstrate the versatility of heuristic methods that prioritize certain types of exploration, and qualitatively show their robustness to both novel objects and the transition from simulation to the real world. We identified scenarios in which our methods did not perform well and objectively difficult scenarios, and present a discussion on which avenues for future research show promise.
A Linear Degenerate Elliptic Equation Arising from Two-Phase Mixtures

We consider the linear degenerate elliptic system of two first order equations ${\mathbf{u}}=-a(\phi)(\nabla{p} - \mathbf{g})$ and $\nabla\cdot(b(\phi)\mathbf{u})+\phi{p}=\phi^{1/2}{f}$, where $a$ and $b$ satisfy $a(0)=b(0)=0$ and are otherwise positive, and the porosity $\phi\ge0$ may be zero on a set of positive measure. This model equation has a similar degeneracy to that arising in the equations describing the mechanical system modeling the dynamics of partially melted materials, e.g., in the earth's mantle and in polar ice sheets and glaciers. In the context of mixture theory, $\phi$ represents the phase variable separating the solid one-phase ($\phi=0$) and fluid-solid two-phase ($\phi>0$) regions. The equations should remain well-posed as $\phi$ vanishes so that the free boundary between the one- and two-phase regions need not be found explicitly. Two main problems arise. First, as $\phi$ vanishes, one equation is lost. Second, it is shown by stability or energy bounds for the solution that the pre.
DEPARTMENT OF INTELLIGENT SYSTEMS

This thesis covers the topic of utilizing neural nets for recommending movies. The principle of using neural nets with machine learning and both the general and the advanced techniques of creating a recommender system are also covered in the thesis. The core of the thesis is the design, implementation and finally the evaluation of a system for movie recommendations based upon the data mined from the user profiles from the ČSFD (Czech-Slovak film database). In order to accomplish this goal the system utilizies an explicit factorization model based on collaborative filtering between items to predict an accurate rating that the user would presumably give to a movie after watching it. This thesis also describes the relation between dataset size and prediction accuracy and demonstrates this accuracy by analyzing user feedback. Klíčová slova doporučovací systémy, neuronové sítě, latentní faktorové modely, filmy, kolaborativní filtrování, doporučování filmů, faktorizace matic, spotlight, dolování dat, ČSFD
A Scale-Invariant Task Balancing Approach for Multi-Task Learning

Multi-task learning (MTL), a learning paradigm to learn multiple related tasks simultaneously, has achieved great success in various fields. However, task-balancing remains a significant challenge in MTL, with the disparity in loss/gradient scales often leading to performance compromises. In this paper, we propose a Scale-Invariant Multi-Task Learning (SI-MTL) method to alleviate the task-balancing problem from both loss and gradient perspectives. Specifically, SI-MTL contains a logarithm transformation which is performed on all task losses to ensure scale-invariant at the loss level, and a gradient balancing method, SI-G, which normalizes all task gradients to the same magnitude as the maximum gradient norm. Extensive experiments conducted on several benchmark datasets consistently demonstrate the effectiveness of SI-G and the state-of-the-art performance of SI-MTL.
Genetic Algorithms for the Single Source Capacitated Location Problem : a Computational Study

In the Single Source Capacitated Location Problem (SSCLP) a set I = {1, . , n} of possible plant locations, each with a maximum capacity ai, i ∈ I, and a set J = {1, . ,m} of customers, each with a demand bj , j ∈ J , are given. It is required to choose a subset of plants and to fully assign each customer to one of the chosen plants, in such a way that the total cost is minimized, and the plant capacities are not exceeded. Total cost is composed by fixed costs, fi, i ∈ I, whenever plant i is open, and by the cost of assigning each customer to one plant, cij , i ∈ I, j ∈ J . Considering xij = { 1, if customer j is assigned to plant i 0, otherwise and yi = { 1, if plant i is open 0, otherwise the SSCLP can be formulated as follows:
Influence of West Antarctic Ice Sheet collapse on Antarctic surface climate

Climate model simulations are used to examine the impact of a collapse of the West Antarctic Ice Sheet (WAIS) on the surface climate of Antarctica. The lowered topography following WAIS collapse produces anomalous cyclonic circulation with increased flow of warm, maritime air toward the South Pole and cold‐air advection from the East Antarctic plateau toward the Ross Sea and Marie Byrd Land, West Antarctica. Relative to the background climate, areas in East Antarctica that are adjacent to the WAIS warm, while substantial cooling (several ∘C) occurs over parts of West Antarctica. Anomalously low isotope‐paleotemperature values at Mount Moulton, West Antarctica, compared with ice core records in East Antarctica, are consistent with collapse of the WAIS during the last interglacial period, Marine Isotope Stage 5e. More definitive evidence might be recoverable from an ice core record at Hercules Dome, East Antarctica, which would experience significant warming and positive oxygen isotope anomalies if the WAIS collapsed.
DNAPlotter: circular and linear interactive genome visualization

Summary: DNAPlotter is an interactive Java application for generating circular and linear representations of genomes. Making use of the Artemis libraries to provide a user-friendly method of loading in sequence files (EMBL, GenBank, GFF) as well as data from relational databases, it filters features of interest to display on separate user-definable tracks. It can be used to produce publication quality images for papers or web pages. Availability: DNAPlotter is freely available (under a GPL licence) for download (for MacOSX, UNIX and Windows) at the Wellcome Trust Sanger Institute web sites: http://www.sanger.ac.uk/Software/Artemis/circular/ Contact: artemis@sanger.ac.uk