Reviewerly/exHarmony huggingface dataset:
DeepSTEP - Deep Learning-Based Spatio-Temporal End-To-End Perception for Autonomous Vehicles

Autonomous vehicles demand high accuracy and robustness of perception algorithms. To develop efficient and scalable perception algorithms, the maximum information should be extracted from the available sensor data. In this work, we present our concept for an end-to-end perception architecture, named DeepSTEP. The deep learning-based architecture processes raw sensor data from the camera, LiDAR, and RaDAR, and combines the extracted data in a deep fusion network. The output of this deep fusion network is a shared feature space, which is used by perception head networks to fulfill several perception tasks, such as object detection or local mapping. DeepSTEP incorporates multiple ideas to advance state of the art: First, combining detection and localization into a single pipeline allows for efficient processing to reduce computational overhead and further improves overall performance. Second, the architecture leverages the temporal domain by using a self-attention mechanism that focuses on the most important features. We believe that our concept of DeepSTEP will advance the development of end-to-end perception systems. The network will be deployed on our research vehicle, which will be used as a platform for data collection, real-world testing, and validation. In conclusion, DeepSTEP represents a significant advancement in the field of perception for autonomous vehicles. The architecture’s end-to-end design, time-aware attention mechanism, and integration of multiple perception tasks make it a promising solution for real-world deployment. This research is a work in progress and presents the first concept of establishing a novel perception pipeline.
CASIN: Cascading Interaction Network for Robust Depth Sensing with an Auxiliary Task

Depth sensing, as a fundamental prerequisite of autonomous systems, either estimates the depth information for each pixel of a colorful image (i.e., depth prediction) or converts the noisy and sparse point clouds into a dense depth map (i.e., depth completion). Most studies treat depth prediction and depth completion as two independent tasks due to the differences between RGB images and point clouds and overlook the complementarity of two modalities, thus resulting in poor robustness to noise. With the integration of monocular cameras and LiDARs in autonomous systems, a generic framework to unify depth estimation and depth completion for robust depth sensing is essential. In this study, we develop a CAScading Interaction Network (CASIN) to capture the modality interaction to compensate for the deficiency of each modality itself. Specifically, two encoder-decoder architectures are employed to learn modality representation. Their multi-scale interactions are preserved by cascading interaction network under the supervision of one auxiliary task. Finally, the shared representation is integrated to infer dense depth map due to the complementarity of modalities. We evaluated the proposed solution on the KITTI dataset. Our experimental results show that learning the coupled interaction of both modalities under the guidance of an auxiliary task can lead to significant performance improvements with 46.64% gain for depth prediction. Furthermore, CASIN is robust to noisy input signals with less performance degradation, which makes our approach feasible in the wild for robust depth sensing.
Query-Specific Temporal Knowledge Graph Representation Learning Model

Temporal knowledge graphs (TKGs) integrate temporal information on the basis of knowledge graphs (KGs) and model the evolution of facts over time in the real world. Since most of the TKGs suffer from incompleteness, it is a very critical task to infer new temporal facts (i.e. Temporal Knowledge Graph Completion, TKGC) from TKGs. However, most of the current methods focus on the inference of static knowledge graphs, or lack reasonable and effective methods to guide the aggregation of historical subgraphs around the query. To this end, we propose a new representation learning model for KGC tasks called QsHA-Net, based on Query-specific Hierarchical Attention mechanism. To answer a query, the model incorporate the given query into hierarchical attention computation, which can more efficiently learn the neighborhood information around the central entity under the current query. Next, RNNs is utilized to learn the long-term dependency between historical subgraphs at different times. We evaluate our model using link prediction tasks on three benchmark datasets and the experimental results show that our model has a considerable improvement over the baseline models.
OR-AutoRec: An Outlier-Resilient Autoencoder-based Recommendation model

Deep neural network (DNN) is widely adopted to develop the recommender systems (RSs) in recent years due to its powerful non-linear representation learning ability. So far, various sophisticated DNN-based RSs have been achieved to provide the state-of-the-art recommendation performance. However, most of them ignore the adverse effects caused by outliers (e.g., malicious users). Inevitably, outliers commonly exist in the collected user behavior data. To address this issue, this paper proposes an outlier-resilient autoencoder-based recommendation model, termed OR-AutoRec. Its main idea is to incorporate the Cauchy Loss into an autoencoder to measure the discrepancy between the observed user behavior data and the predicted ones. As such, OR-AutoRec is resilient to outliers owing to the robustness of Cauchy Loss. By conducting extensive experiments on five benchmark datasets, we demonstrate that: 1) our OR-AutoRec is much more robust to outliers than original autoencoder-based model, and 2) our OR-AutoRec achieves significantly better prediction accuracy than both DNN-based and non-DNN-based state-of-the-art models.
Introducing mindset streams to investigate stances towards STEM in high school students and experts

We introduce mindset streams for assessing ways of bridging two target concepts in concept maps. We focus on behavioural forma mentis networks (BFMN), which map the associative and affective dimensions of memory recalls. Inspired by trains of thoughts taking several paths to link ideas, mindset streams are defined as BFMN subgraphs induced by all shortest paths between two target concepts, e.g. all recalls in shortest paths bridging “math” and “learning”. These streams quantify the following features of the mindset encoded in a BFMN: (i) semantic content (i.e. which ideas mediate connections between targets?), (ii) valence coherence/conflict (i.e. are connections mediated by entwining ideas perceived negatively, positively or neutrally?), and (iii) semantic relevance (i.e. are the bridges between targets peripheral or central for the connectivity/betweenness of the BFMN?). We investigate mindset streams between ‘maths”/“physics” and key motivational aspects of learning (“fun”, “work”, “failure”) in two BFMNs, encoding how 159 students and 59 experts perceived and associated concepts about Science Technology Engineering and Maths (STEM), respectively. Statistical comparisons against configuration models show that high schoolers bridge “maths” and “fun” only through overabundant levels of valence-conflicting associations, contrasting negatively perceived domain knowledge with peer-related positive experiences. This conflict is absent in the researchers’ mindset stream, which rather bridges “math” and “fun” through positive, science-related associations. The mindset streams of both groups bridge “maths” and “physics” to “work” through mostly positive career-related jargon. Students’ mindset streams of “failure” and “math”/“physics” are dominated by negative associations with test anxiety, whereas researchers integrate “failure” and “math”/“physics” in semantically richer and more positive contexts, denoting failure itself as a cornerstone of STEM learning. We discuss our findings and future research directions in view of relevant psychology/education literature.
Deep features fusion for user authentication based on human activity

Abstract The exponential growth in the use of smartphones means that users must constantly be concerned about the security and privacy of mobile data because the loss of a mobile device could compromise personal information. To address this issue, continuous authentication systems have been proposed, in which users are monitored transparently after initial access to the smartphone. In this study, the authors address the problem of user authentication by considering human activities as behavioural biometric information. The authors convert the behavioural biometric data (considered as time series) into a 2D colour image. This transformation process keeps all the characteristics of the behavioural signal. Time series does not receive any filtering operation with this transformation, and the method is reversible. This signal‐to‐image transformation allows us to use the 2D convolutional networks to build efficient deep feature vectors. This allows them to compare these feature vectors to the reference template vectors to compute the performance metric. The authors evaluate the performance of the authentication system in terms of Equal Error Rate on a benchmark University of Californy, Irvine Human Activity Recognition dataset, and they show the efficiency of the approach.
Research Data Management Practices and Challenges in Academic Libraries: A Comprehensive Review

This comprehensive review article delves into the current landscape of research data management (RDM) practices and challenges faced by academic libraries across various regions. Utilizing a wide range of studies and data collected from different countries, this article aims to provide a comprehensive overview of the state of RDM services, the role of librarians, and the advancements in technology within academic libraries. The review explores the importance of RDM in supporting open science, data sharing, and reproducibility, while also shedding light on areas that require further development and improvement.
SCDNet: Real-time Semantic Segmentation Network with Split Connection and Flexible Dilated Convolution

Recently, semantic segmentation has been applied widely in various realistic scenarios. Many semantic segmentation backbone networks use various forms of dense connection to improve the efficiency of gradient propagation in the network. They achieve excellent segmentation accuracy but lack inference speed. Therefore, we propose a backbone network SCDNet with a dual path structure and higher speed and accuracy. Firstly, we propose a split connection structure, which is a streamlined lightweight backbone with a parallel structure to increase inference speed. Secondly, we introduce a flexible dilated convolution using different dilation rates so the network can have richer receptive fields to perceive objects. Finally, we propose a three-level hierarchical module to effectively balance the feature maps with multiple resolutions. Our work achieves a trade-off of accuracy and speed on the Cityscapes and Camvid datasets. Specifically, we get a 20% improvement in FPS and a 0.5% improvement in mIoU on the Cityscapes test set.
A Constraint Virtual Adversarial Training

Node classification is widely used in computer vision, natural language processing, recommendation systems, and other fields, and is essentially a semi-supervised learning task. However, due to the additional consideration of connections between nodes, classification based on a graph neural network may be more sensitive to perturbations, as the perturbation from connected nodes exacerbates the impact on classification results. Existing adversarial rarely consider the direction of the perturbations, to lose the topology and influence of the original samples in the surrounding pieces. To counter this, we propose a new method called the constraint virtual adversarial training method based on the graph (G-CAT), which considers the connections and contributions between nodes in the graph structure. We design the influence of different nodes through node attention mechanisms to further constrain the adversarial perturbation further. In particular, our G-CAT is conceptually beneficial to exist graph-based classification methods that can dynamically adapt the adversarial training through virtual labels. Empirical results on three real-world datasets show that G-CAT significantly outperforms state-of-the-art algorithms in classification accuracy.
Coflow scheduling with unknown prior knowledge based on traffic characteristics

Improving data transfer efficiency is critical to reducing application completion time. Existing coflow scheduling schemes fail to take full advantage of the traffic characteristics in the network, especially in coflow scheduling where prior knowledge is unknown. This paper finds that making full use of flow characteristics can further reduce the coflow completion time. We propose a scheduling scheme A-SNCF based on traffic characteristics. Although A-SNCF lacks complete coflow information before transmission, A-SNCF can make reasonable scheduling based on the knowable information during transmission. The scheme proposed in this paper can schedule coflow according to traffic characteristics, which can further reduce the data transmission time. Experimental results on open source datasets show that the average completion time of A-SNCF is 20% less than that of MCS.
Reidentifying soccer players in broadcast videos using Body Feature Alignment Based on Pose

Re-identification (re-id) of people in images is a well-studied problem in computer vision for many applications. The re-identification of players in broadcast videos of team sports is the main subject of this work. We specifically concentrate on recognizing the same player in images taken at any given time during a match from various camera angles. Some significant differences exist between this task and other traditional person re-id applications, such as same team wear highly similar clothes, for each identification, there are only a small number of samples, and low resolutions of the images. One of the most difficult problems in object re-identification is extracting robust feature representation (ReID). Even though methods based on convolution neural networks (CNNs) have had significant success. But to improve extracting features, we present the novel approach Body Feature Alignment Based on Pose, utilizing pose landmarks to extract the image's useful information. During the feature constructing stage, our method makes use of human landmarks to obtain the angles and distances between the joints. According the results, the proposed method provide comparable improvements for convolutional networks.
PerDoor: Persistent Backdoors in Federated Learning using Adversarial Perturbations

Federated Learning (FL) enables numerous par-ticipants to train deep learning models collaboratively without exposing sensitive personal data. However, distributed nature of FL and unvetted data makes it vulnerable to backdoor attacks by injecting malicious functionality into the centralized model during training, causing desired misclassifications for specific adversary-chosen inputs. Prior works established successful back-door injection in FL systems; however, these are not demon-strated to be long-lasting. Backdoor functionality does not survive if the adversary is prevented from training since the centralized model continuously mutates during successive FL rounds. This work proposes PerDoor, a persistent-by-construction backdoor injection technique for FL, driven by adversarial perturbation and targeting parameters of the centralized model deviating less in successive FL rounds and contributing the least to main task accuracy. Exhaustive evaluation considering image classification scenarios portrays up to 8.2x persistence by PerDoor compared to state-of-the-art backdoor attacks in FL and exhibits its potency against state-of-the-art backdoor prevention methods.
Automatic Detection for Machine-generated Texts is Easy

The recognition of machine-generated text can infer the problems existing in the text generation model, so we evaluate the ability of machine and human to detect and judge the generated text. Specifically, we generate and build five datasets for evaluation adopting models like GPT2-Chinese, Jiuge system, and GPT-Neo in combination with the raw corpus. Compared with the manual classification of volunteers, the text is analyzed by two classical classification models BiLSTM and FastText, which found that the average accuracy of machine classification can reach 94.54%, while the average accuracy of human is only 65.87%. Therefore, we make a thorough research in why machines can effectively distinguish differences between human-written text and machine-generated text. According to the feedback of human evaluators and evaluation of the generated text, this paper puts forward six disadvantages of machine-generated text, including topic drift, prolix sentences, abnormal paragraphs, poor text length control, overused phrases, and sparsity of uncommon characters. Based on different datasets, the defects are tested by analysis models and statistical methods, thus providing relevant guidance for greater improvement in the quality of model generation.
A Few-shot-learning-based Method to Object Recognition in Multiple Scenarios

To recognize the target classes with only a few samples, few-shot learning (FSL) uses prior knowledge learned from the source classes and is usually expressed as a special domain adaptation problem. However, Existing few-shot learning methods make the implicit assumption that the few target class samples are from the same domain or different domain as the source class samples, which greatly limits their application in the wild. This paper introduces a few-shot learning method in multiple scenarios which requires no prior knowledge on the label set. For a given target domain labels set, it may overlap with the set of source domain labels to varying degrees, thereby bringing up an additional class gap based on the domain gap. In order to solve this problem in a unified framework, we propose a novel domain adaptation network which is designed to address a specific challenge: How to achieve domain adaptation whilst maintaining source/target per-class discriminativeness when the target domain label set is unseen. Our solution is to design corresponding soft label transfer network and minimax entropy network for different target domain label set, then we quantify the transferability of the source domain label set during the training process to discover the relationship between the source domain label set and the target domain label set. Further, we broaden the model's understanding of the data by learning from self-supervised signals how to solve a jigsaw puzzle on the same sample. Extensive experiments show that our model outperforms the state-of-the-art models.
ACTS: Autonomous Cost-Efficient Task Orchestration for Serverless Analytics

Serverless computing has become increasingly popular for cloud applications, due to its compelling properties of high-level abstractions, lightweight runtime, high elasticity and pay-per-use billing. In this revolutionary computing paradigm shift, challenges arise when adapting data analytics applications to the serverless environment, due to the lack of support for efficient state sharing, which attract ever-growing research attention. In this paper, we aim to exploit the advantages of task-level orchestration and fine-grained resource provisioning for data analytics on serverless platforms, with the hope of fulfilling the promise of serverless deployment to the maximum extent. To this end, we present ACTS, an autonomous cost-efficient task orchestration framework for serverless analytics. ACTS judiciously schedules and coordinates function tasks to mitigate cold-start latency and state sharing overhead. In addition, ACTS explores the optimization space of fine-grained workload distribution and function resource configuration for cost efficiency. We have deployed and implemented ACTS on AWS Lambda, evaluated with various data analytics workloads. Results from extensive experiments demonstrate that ACTS achieves up to 98% monetary cost reduction while maintaining superior job completion time performance, in comparison with the state-of-the-art baselines.
High-throughput Sampling, Communicating and Training for Reinforcement Learning Systems

Reinforcement Learning (RL) algorithms require large amounts of computational resources and time to train due to the simultaneous model training and real-time interactions with simulation environments. This results in an RL algorithm's training time potentially exceeding days to months. We present HRL, a comprehensive optimization system designed to improve the system throughput of RL algorithms. HRL addresses the challenges of discovering and identifying bottlenecks in the sampling, training, or communication stages and promptly improving their performance. We propose a group-parallel pipeline method to improve the sampling efficiency and apply data quantization and multi-learner training to resolve the network and learning bottlenecks. The HRL system has been fully implemented and integrated with XingTian. The results of the experiments show that the HRL system can improve the throughput by 18.6%-90.6%.
Collaborative Edge Service Placement for Maximizing QoS with Distributed Data Cleaning

The proliferation of dirty data on Internet of Things (IoT) devices can undermine the accuracy of data-driven decision-making by affecting the distribution of original data. The Quality of Service (QoS) of data cleaning on these devices is heavily impacted by processing delay and accuracy. In this paper, we find that edge service placement is a key step aligned with data cleaning and consider the collaborative edge service placement with distributed data cleaning (SPDC) problem. To address this issue, we propose a novel distributed collaborative edge-based architecture that effectively balances the demands of storage, communication, computation, and load constraints. Experimental results show that the proposed approach significantly improves the accuracy of data cleaning by 0.31%-86.07% and reduces delay by 2.73%-58.71% compared to state-of-the-art baselines.
spotDNN: Provisioning Spot Instances for Predictable Distributed DNN Training in the Cloud

Distributed Deep Neural Network (DDNN) training on cloud spot instances is increasingly compelling as it can significantly save the user budget. To handle unexpected instance revocations, provisioning a heterogeneous cluster using the asynchronous parallel mechanism becomes the dominant method for DDNN training with spot instances. However, blindly provisioning a cluster of spot instances can easily result in unpre-dictable DDNN training performance, mainly because bottlenecks occur on the parameter server network bandwidth and PCIe bandwidth resources, as well as the inadequate cluster heterogeneity. To address the challenges above, we propose spotDNN, a heterogeneity-aware spot instance provisioning framework that provides predictable performance for DDNN training in the cloud. By explicitly considering the contention for bottle-neck resources, we first build an analytical performance model of DDNN training in heterogeneous clusters. It leverages the weighted average batch size and convergence coefficient to quantify the DDNN training loss in heterogeneous clusters. Through a lightweight workload profiling, we further design a cost-efficient instance provisioning strategy which incorporates the bounds calculation and sliding window techniques to effectively guarantee the training performance service level objectives (SLOs). We have implemented a prototype of spotDNN and conducted extensive experiments on Amazon EC2. Experiment results show that spotDNN can deliver predictable DDNN training performance while reducing the monetary cost by up to 68.1% compared to the existing solutions, yet with acceptable runtime overhead.