OpenRaiser/Intern-Atlas huggingface dataset:
A Fingerprint Detection Method by Fingerprint Ridge Orientation Check
Venue: arXiv cs.CV 2022
Year: 2022

Fingerprints are popular among the biometric based systems due to ease of acquisition, uniqueness and availability. Nowadays it is used in smart phone security, digital payment and digital locker. Fingerprint recognition technology has been studied for a long time, and its recognition rate has recently risen to a high level. In particular, with the introduction of Deep Neural Network technologies, the recognition rate that could not be reached before was reached. In this paper, we propose a fingerprint detection algorithm used in a fingerprint recognition system.
Dual-Level Decoupled Transformer for Video Captioning
Venue: arXiv cs.CV 2022
Year: 2022

Video captioning aims to understand the spatio-temporal semantic concept of the video and generate descriptive sentences. The de-facto approach to this task dictates a text generator to learn from \textit{offline-extracted} motion or appearance features from \textit{pre-trained} vision models. However, these methods may suffer from the so-called \textbf{\textit{"couple"}} drawbacks on both \textit{video spatio-temporal representation} and \textit{sentence generation}. For the former, \textbf{\textit{"couple"}} means learning spatio-temporal representation in a single model(3DCNN), resulting the problems named \emph{disconnection in task/pre-train domain} and \emph{hard for end-to-end training}. As for the latter, \textbf{\textit{"couple"}} means treating the generation of visual semantic and syntax-related words equally. To this end, we present $\mathcal{D}^{2}$ - a dual-level decoupled transformer pipeline to solve the above drawbacks: \emph{(i)} for video spatio-temporal representation, we decouple the process of it into "first-spatial-then-temporal" paradigm, releasing the potential of using dedicated model(\textit{e.g.} image-text pre-training) to connect the pre-training and downstream tasks, and makes the entire model end-to-end trainable. \emph{(ii)} for sentence generation, we propose \emph{Syntax-Aware Decoder} to dynamically measure the contribution of visual semantic and syntax-related words. Extensive experiments on three widely-used benchmarks (MSVD, MSR-VTT and VATEX) have shown great potential of the proposed $\mathcal{D}^{2}$ and surpassed the previous methods by a large margin in the task of video captioning.
Arbitrary pattern formation by asynchronous opaque robots on infinite grid
Venue: arXiv cs.DC 2022
Year: 2022

Arbitrary pattern formation ($\mathcal{APF}$) by mobile robots is studied by many in literature under different conditions and environment. Recently it has been studied on an infinite grid network but with full visibility. In opaque robot model, circle formation on infinite grid has also been studied. In this paper, we are solving $\mathcal{APF}$ on infinite grid with asynchronous opaque robots with lights. The robots do not share any global co-ordinate system. The main challenge in this problem is to elect a leader to agree upon a global co-ordinate where the vision of the robots are obstructed by other robots. Since the robots are on a grid, their movements are also restricted to avoid collisions. In this paper, the aforementioned hardness are overcome to produce an algorithm that solves the problem.
SD-AETO: Service Deployment Enabled Adaptive Edge Task Offloading in MEC
Venue: arXiv cs.NI 2022
Year: 2022

In recent years, edge computing, as an important pillar for future networks, has been developed rapidly. Task offloading is a key part of edge computing that can provide computing resources for resource-constrained devices to run computing-intensive applications, accelerate computing speed and save energy. An efficient and feasible task offloading scheme can not only greatly improve the quality of experience (QoE) but also provide strong support and assistance for 5G/B5G networks, the industrial Internet of Things (IIoT), computing networks and so on. To achieve these goals, this paper proposes an adaptive edge task offloading scheme assisted by service deployment (SD-AETO) focusing on the optimization of the energy utilization ratio (EUR) and the processing latency. In the pre-implementation stage of the SD-AETO scheme, a service deployment scheme is invoked to assist with task offloading considering each service's popularity. The optimal service deployment scheme is obtained by using the approximate deployment graph (AD-graph). Furthermore, a task scheduling and queue offloading design procedure is proposed to complete the SD-AETO scheme based on the task priority. The task priority is generated by the corresponding service popularity and task offloading direction. Finally, we analyze our SD-AETO scheme and compare it with related approaches, and the results show that our scheme has a higher edge offloading rate and lower resource consumption for massive task scenarios in the edge network.
Crop Type Identification for Smallholding Farms: Analyzing Spatial, Temporal and Spectral Resolutions in Satellite Imagery
Venue: arXiv cs.CV 2022
Year: 2022

The integration of the modern Machine Learning (ML) models into remote sensing and agriculture has expanded the scope of the application of satellite images in the agriculture domain. In this paper, we present how the accuracy of crop type identification improves as we move from medium-spatiotemporal-resolution (MSTR) to high-spatiotemporal-resolution (HSTR) satellite images. We further demonstrate that high spectral resolution in satellite imagery can improve prediction performance for low spatial and temporal resolutions (LSTR) images. The F1-score is increased by 7% when using multispectral data of MSTR images as compared to the best results obtained from HSTR images. Similarly, when crop season based time series of multispectral data is used we observe an increase of 1.2% in the F1-score. The outcome motivates further advancements in the field of synthetic band generation.
LPGNet: Link Private Graph Networks for Node Classification
Venue: arXiv cs.LG 2022
Year: 2022

Classification tasks on labeled graph-structured data have many important applications ranging from social recommendation to financial modeling. Deep neural networks are increasingly being used for node classification on graphs, wherein nodes with similar features have to be given the same label. Graph convolutional networks (GCNs) are one such widely studied neural network architecture that perform well on this task. However, powerful link-stealing attacks on GCNs have recently shown that even with black-box access to the trained model, inferring which links (or edges) are present in the training graph is practical. In this paper, we present a new neural network architecture called LPGNet for training on graphs with privacy-sensitive edges. LPGNet provides differential privacy (DP) guarantees for edges using a novel design for how graph edge structure is used during training. We empirically show that LPGNet models often lie in the sweet spot between providing privacy and utility: They can offer better utility than "trivially" private architectures which use no edge information (e.g., vanilla MLPs) and better resilience against existing link-stealing attacks than vanilla GCNs which use the full edge structure. LPGNet also offers consistently better privacy-utility tradeoffs than DPGCN, which is the state-of-the-art mechanism for retrofitting differential privacy into conventional GCNs, in most of our evaluated datasets.
Controlled Dropout for Uncertainty Estimation
Venue: arXiv cs.LG 2022
Year: 2022

Uncertainty quantification in a neural network is one of the most discussed topics for safety-critical applications. Though Neural Networks (NNs) have achieved state-of-the-art performance for many applications, they still provide unreliable point predictions, which lack information about uncertainty estimates. Among various methods to enable neural networks to estimate uncertainty, Monte Carlo (MC) dropout has gained much popularity in a short period due to its simplicity. In this study, we present a new version of the traditional dropout layer where we are able to fix the number of dropout configurations. As such, each layer can take and apply the new dropout layer in the MC method to quantify the uncertainty associated with NN predictions. We conduct experiments on both toy and realistic datasets and compare the results with the MC method using the traditional dropout layer. Performance analysis utilizing uncertainty evaluation metrics corroborates that our dropout layer offers better performance in most cases.
Arabic Fake News Detection Based on Deep Contextualized Embedding Models
Venue: arXiv cs.CL 2022
Year: 2022

Social media is becoming a source of news for many people due to its ease and freedom of use. As a result, fake news has been spreading quickly and easily regardless of its credibility, especially in the last decade. Fake news publishers take advantage of critical situations such as the Covid-19 pandemic and the American presidential elections to affect societies negatively. Fake news can seriously impact society in many fields including politics, finance, sports, etc. Many studies have been conducted to help detect fake news in English, but research conducted on fake news detection in the Arabic language is scarce. Our contribution is twofold: first, we have constructed a large and diverse Arabic fake news dataset. Second, we have developed and evaluated transformer-based classifiers to identify fake news while utilizing eight state-of-the-art Arabic contextualized embedding models. The majority of these models had not been previously used for Arabic fake news detection. We conduct a thorough analysis of the state-of-the-art Arabic contextualized embedding models as well as comparison with similar fake news detection systems. Experimental results confirm that these state-of-the-art models are robust, with accuracy exceeding 98%.
Latency Guarantee for Ubiquitous Intelligence in 6G: A Network Calculus Approach
Venue: arXiv cs.NI 2022
Year: 2022

With the gradual deployment of 5G and the continuous popularization of edge intelligence (EI), the explosive growth of data on the edge of the network has promoted the rapid development of 6G and ubiquitous intelligence (UbiI). This article aims to explore a new method for modeling latency guarantees for UbiI in 6G given 6G's extremely stochastic nature in terahertz (THz) environments, THz channel tail behavior, and delay distribution tail characteristics generated by the UBiI random component, and to find the optimal solution that minimizes the end-to-end (E2E) delay of UbiI. In this article, the arrival curve and service curve of network calculus can well characterize the stochastic nature of wireless channels, the tail behavior of wireless systems and the E2E service curve of network calculus can model the tail characteristic of the delay distribution in UbiI. Specifically, we first propose demands and challenges facing 6G, edge computing (EC), edge deep learning (DL), and UbiI. Then, we propose the hierarchical architecture, the network model, and the service delay model of the UbiI system based on network calculus. In addition, two case studies demonstrate the usefulness and effectiveness of the network calculus approach in analyzing and modeling the latency guarantee for UbiI in 6G. Finally, future open research issues regarding the latency guarantee for UbiI in 6G are outlined.
Longitudinal cardio-respiratory fitness prediction through wearables in free-living environments
Venue: arXiv cs.LG 2022
Year: 2022

Cardiorespiratory fitness is an established predictor of metabolic disease and mortality. Fitness is directly measured as maximal oxygen consumption (VO$_{2}max$), or indirectly assessed using heart rate responses to standard exercise tests. However, such testing is costly and burdensome because it requires specialized equipment such as treadmills and oxygen masks, limiting its utility. Modern wearables capture dynamic real-world data which could improve fitness prediction. In this work, we design algorithms and models that convert raw wearable sensor data into cardiorespiratory fitness estimates. We validate these estimates' ability to capture fitness profiles in free-living conditions using the Fenland Study (N=11,059), along with its longitudinal cohort (N=2,675), and a third external cohort using the UK Biobank Validation Study (N=181) who underwent maximal VO$_{2}max$ testing, the gold standard measurement of fitness. Our results show that the combination of wearables and other biomarkers as inputs to neural networks yields a strong correlation to ground truth in a holdout sample (r = 0.82, 95CI 0.80-0.83), outperforming other approaches and models and detects fitness change over time (e.g., after 7 years). We also show how the model's latent space can be used for fitness-aware patient subtyping paving the way to scalable interventions and personalized trial recruitment. These results demonstrate the value of wearables for fitness estimation that today can be measured only with laboratory tests.
Green Accelerated Hoeffding Tree
Venue: arXiv cs.LG 2022
Year: 2022

State-of-the-art machine learning solutions mainly focus on creating highly accurate models without constraints on hardware resources. Stream mining algorithms are designed to run on resource-constrained devices, thus a focus on low power and energy and memory-efficient is essential. The Hoeffding tree algorithm is able to create energy-efficient models, but at the cost of less accurate trees in comparison to their ensembles counterpart. Ensembles of Hoeffding trees, on the other hand, create a highly accurate forest of trees but consume five times more energy on average. An extension that tried to obtain similar results to ensembles of Hoeffding trees was the Extremely Fast Decision Tree (EFDT). This paper presents the Green Accelerated Hoeffding Tree (GAHT) algorithm, an extension of the EFDT algorithm with a lower energy and memory footprint and the same (or higher for some datasets) accuracy levels. GAHT grows the tree setting individual splitting criteria for each node, based on the distribution of the number of instances over each particular leaf. The results show that GAHT is able to achieve the same competitive accuracy results compared to EFDT and ensembles of Hoeffding trees while reducing the energy consumption up to 70%.
Bounded-degree plane geometric spanners in practice
Venue: arXiv cs.CG 2022
Year: 2022

The construction of bounded-degree plane geometric spanners has been a focus of interest since 2002 when Bose, Gudmundsson, and Smid proposed the first algorithm to construct such spanners. To date, eleven algorithms have been designed with various trade-offs in degree and stretch factor. We have implemented these sophisticated algorithms in C++ using the CGAL library and experimented with them using large synthetic and real-world pointsets. Our experiments have revealed their practical behavior and real-world efficacy. We share the implementations via GitHub for broader uses and future research. We present a simple practical algorithm, named AppxStretchFactor, that can estimate stretch factors (obtains lower bounds on the exact stretch factors) of geometric spanners - a challenging problem for which no practical algorithm is known yet. In our experiments with bounded-degree plane geometric spanners, we find that AppxStretchFactor estimates stretch factors almost precisely. Further, it gives linear runtime performance in practice for the pointset distributions considered in this work, making it much faster than the naive Dijkstra-based algorithm for calculating stretch factors
Predicting Future Occupancy Grids in Dynamic Environment with Spatio-Temporal Learning
Venue: arXiv cs.CV 2022
Year: 2022

Reliably predicting future occupancy of highly dynamic urban environments is an important precursor for safe autonomous navigation. Common challenges in the prediction include forecasting the relative position of other vehicles, modelling the dynamics of vehicles subjected to different traffic conditions, and vanishing surrounding objects. To tackle these challenges, we propose a spatio-temporal prediction network pipeline that takes the past information from the environment and semantic labels separately for generating future occupancy predictions. Compared to the current SOTA, our approach predicts occupancy for a longer horizon of 3 seconds and in a relatively complex environment from the nuScenes dataset. Our experimental results demonstrate the ability of spatio-temporal networks to understand scene dynamics without the need for HD-Maps and explicit modeling dynamic objects. We publicly release our occupancy grid dataset based on nuScenes to support further research.
Goal-Oriented Next Best Activity Recommendation using Reinforcement Learning
Venue: arXiv cs.AI 2022
Year: 2022

Recommending a sequence of activities for an ongoing case requires that the recommendations conform to the underlying business process and meet the performance goal of either completion time or process outcome. Existing work on next activity prediction can predict the future activity but cannot provide guarantees of the prediction being conformant or meeting the goal. Hence, we propose a goal-oriented next best activity recommendation. Our proposed framework uses a deep learning model to predict the next best activity and an estimated value of a goal given the activity. A reinforcement learning method explores the sequence of activities based on the estimates likely to meet one or more goals. We further address a real-world problem of multiple goals by introducing an additional reward function to balance the outcome of a recommended activity and satisfy the goal. We demonstrate the effectiveness of the proposed method on four real-world datasets with different characteristics. The results show that the recommendations from our proposed approach outperform in goal satisfaction and conformance compared to the existing state-of-the-art next best activity recommendation techniques.
Musical Score Following and Audio Alignment
Venue: arXiv cs.SD 2022
Year: 2022

Real-time tracking of the position of a musical performance on a musical score, i.e. score following, can be useful in music practice, performance and production. Example applications of such technology include computer-aided accompaniment and automatic page turning. Score following is a challenging task, especially when considering deviations in performance data from the score stemming from mistakes or expressive choices. In this project, the extensive research present in the field is first explored before two open-source evaluation testbenches for score following--one quantitative and the other qualitative--are introduced. A new way of obtaining quantitative testbench data is proposed, and the QualScofo dataset for qualitative benchmarking is introduced. Subsequently, three different score followers, each of a different class, are implemented. First, a beat-based follower for an interactive conductor application--the TuneApp Conductor--is created to demonstrate an entertaining application of score following. Then, an Approximate String Matching (ASM) non-real-time follower is implemented to complement the quantitative testbench and provide more technical background details of score following. Finally, a Constant Q-Transform (CQT) Dynamic Time Warping (DTW) score follower robust against major challenges in score following (such as polyphonic music and performance deviations) is outlined and implemented; it is shown that this CQT-based approach consistently and significantly outperforms a commonly used FFT-based approach in extracting audio features for score following.
Synthetic Data -- what, why and how?
Venue: arXiv cs.LG 2022
Year: 2022

This explainer document aims to provide an overview of the current state of the rapidly expanding work on synthetic data technologies, with a particular focus on privacy. The article is intended for a non-technical audience, though some formal definitions have been given to provide clarity to specialists. This article is intended to enable the reader to quickly become familiar with the notion of synthetic data, as well as understand some of the subtle intricacies that come with it. We do believe that synthetic data is a very useful tool, and our hope is that this report highlights that, while drawing attention to nuances that can easily be overlooked in its deployment.
The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanations
Venue: arXiv cs.LG 2022
Year: 2022

Machine learning models in safety-critical settings like healthcare are often blackboxes: they contain a large number of parameters which are not transparent to users. Post-hoc explainability methods where a simple, human-interpretable model imitates the behavior of these blackbox models are often proposed to help users trust model predictions. In this work, we audit the quality of such explanations for different protected subgroups using real data from four settings in finance, healthcare, college admissions, and the US justice system. Across two different blackbox model architectures and four popular explainability methods, we find that the approximation quality of explanation models, also known as the fidelity, differs significantly between subgroups. We also demonstrate that pairing explainability methods with recent advances in robust machine learning can improve explanation fairness in some settings. However, we highlight the importance of communicating details of non-zero fidelity gaps to users, since a single solution might not exist across all settings. Finally, we discuss the implications of unfair explanation models as a challenging and understudied problem facing the machine learning community.
Implicit semantic-based personalized micro-videos recommendation
Venue: arXiv cs.IR 2022
Year: 2022

With the rapid development of mobile Internet and big data, a huge amount of data is generated in the network, but the data that users are really interested in a very small portion. To extract the information that users are interested in from the huge amount of data, the information overload problem needs to be solved. In the era of mobile internet, the user's characteristics and other information should be combined in the massive amount of data to quickly and accurately recommend content to the user, as far as possible to meet the user's personalized needs. Therefore, there is an urgent need to realize high-speed and effective retrieval in tens of thousands of micro-videos. Video data content contains complex meanings, and there are intrinsic connections between video data. For multimodal information, subspace coding learning is introduced to build a coding network from public potential representations to multimodal feature information, taking into account the consistency and complementarity of information under each modality to obtain a public representation of the complete eigenvalue. An end-to-end reordering model based on deep learning and attention mechanism, called interest-related product similarity model based on multimodal data, is proposed for providing top-N recommendations. The multimodal feature learning module, interest-related network module and product similarity recommendation module together form the new model.By conducting extensive experiments on publicly accessible datasets, the results demonstrate the state-of-the-art performance of our proposed algorithm and its effectiveness.