Reviewerly/exHarmony huggingface dataset:
Step-Indexed Logical Relations for Countable Nondeterminism and Probabilistic Choice

Developing denotational models for higher-order languages that combine probabilistic and nondeterministic choice is known to be very challenging. In this paper, we propose an alternative approach based on operational techniques. We study a higher-order language combining parametric polymorphism, recursive types, discrete probabilistic choice and countable nondeterminism. We define probabilistic generalizations of may- and must-termination as the optimal and pessimal probabilities of termination. Then we define step-indexed logical relations and show that they are sound and complete with respect to the induced contextual preorders. For may-equivalence we use step-indexing over the natural numbers whereas for must-equivalence we index over the countable ordinals. We then show than the probabilities of may- and must-termination coincide with the maximal and minimal probabilities of termination under all schedulers. Finally we derive the equational theory induced by contextual equivalence and show that it validates the distributive combination of the algebraic theories for probabilistic and nondeterministic choice.
Learning Software Project Management From Analyzing Q&amp;A’s in the Stack Exchange

Software Project Management (SPM) is considered the key driver for the success or failure of software projects. Project failure is caused by various factors, the most important of which is poor SPM. Thus, we investigated the needs of practitioners by focusing on Project Management Q&A communities. More precisely, we targeted Stack Exchange to identify the primary needs of software project managers. More than 5000 SPM questions were analyzed from the conceptual model given by the Project Management Body of Knowledge PMBOK. For pre-training of the Machine Learning classifiers, we implemented Bidirectional Encoder Representations from Transformers (BERT) and Doc2Vec text embedding and compared their performance. Our results showed that BERT outperforms Doc2Vec for pre-training in almost all scenarios. Schedule management, followed by resource management, are the main PMBOK knowledge areas of concern for project managers. Among the process groups, the emphasis of the questions is on planning. We compared the findings with the learning and training status quo in 11 top Canadian universities. We analyzed 46 SPM-related courses and found that the rank correlation of PMBOK knowledge areas is 0.23 between the key content of the analyzed courses and the focus of Q&A’s knowledge areas analyzed from Stack Exchange.
SIT-SR 3D: Self-supervised slice interpolation via transfer learning for 3D volume super-resolution

We present SIT-SR 3D, a novel self-supervised method for 3D single image super-resolution (SISR). Scaling 2D SISR networks to 3D SISR requires code redesign, high computing resources, and 3D ground-truth. However, we circumvent this by (1) using a pre-trained 2D SISR for indirect supervision and (2) using a novel consistency loss to learn frame interpolation. Any pre-trained state of the art 2D SISR method can replace the 2D SISR used in SIT-SR 3D, thus transferring the merits of 2D to 3D and ensuring modularity. We trained two end-to-end 3D baselines in a supervised setting; a 3D RRDBNet trained only with L1 loss and a 3D ESRGAN trained with adversarial and perceptual loss. We show that the proposed pipeline's self-supervised version is qualitatively better than the baselines. When trained in a supervised setting, SIT-SR 3D achieves better PSNR than its counterparts. Furthermore, our pipeline uses fewer parameters compared to the baselines. We demonstrate our results on an open-source digital rock CT dataset. Our code and pre-trained models will be made publicly available.
Dual-stream reinforcement network for few-shot image segmentation

Deep learning has achieved successful results in semantic segmentation of images. However, semantic segmentation of images still faces challenges in real-world applications. On the one hand, due to the repeated stacking of convolution layer and pooling layer, each feature information is localized, eliminating long-term dependence and content deviation. On the other hand, it requires large number of annotated dataset for training, and can not classify unknown categories. To address the above issues, a dual-stream reinforcement network (DRNet) is proposed for few-shot image semantic segmentation in the paper. The dual-stream branches are designed, including a prototype enhancement branch and a query-guided branch. The former branch is designed to compute query vectors based on feature similarity. The latter branch is presented to extract the expression of target features and feature consistency information between images by utilizing additional memory units. Moreover, a matrix of feature correlations is generated by a graph attention mechanism between support vectors and query vectors, which allows query information to be propagated to support vectors. Finally, additional memory units are used to mine the representation of target features. In this way, images with consistent features and information about common features can be effectively fused. The experimental results show that the proposed model DRNet can achieve state-of-the-art results compared to other models. Notably, the mean-IoU scores of our model are 53.34% and 62.03% for PASCAL-5i at 1-shot and 5-shot settings, respectively, which are more competitive than other methods.
A dynamic balanced quadtree for real-time streaming data

With the advent of big data, industries and individuals generate a large amount of stream data through various intelligent devices. Stream data arrive gradually over time, unlike historical data. This causes an increasingly unbalanced tree because of the unpredictable data density when an index method such as the classical quadtree is used. To solve this problem, this paper formally defines the quadtree imbalance problem caused by constant stream data density, defines a more reasonable measure to evaluate the balance of a quadtree, and proposes a dynamic balanced quadtree (DB-quadtree) for real-time stream data. Experiments on one synthetic dataset and three real datasets show that the method can improve the balance of the tree.
Knowledge-aware patient representation learning for multiple disease subtypes

Learning latent representations of patients with a target disease is a core problem in a broad range of downstream applications, such as clinical endpoint prediction. The suffering of patients may have multiple subtypes with certain similarities and differences, which need to be addressed for learning effective patient representation to facilitate the downstream tasks. However, existing studies either ignore the distinction of disease subtypes to learn disease-level representations, or neglect the correlations between subtypes and only learn disease subtype-level representations, which affects the performance of patient representation learning. To alleviate this problem, we studied how to effectively integrate data from all disease subtypes to improve the representation of each subtype. Specifically, we proposed a knowledge-aware shared-private neural network model to explicitly use disease-oriented knowledge and learn shared and specific representations from the disease and its subtype perspectives. To evaluate the feasibility of the proposed model, we conducted a particular downstream task, i.e., clinical endpoint prediction, on the basis of the learned patient presentations. The results on the real-world clinical datasets demonstrated that our model could yield a significant improvement over state-of-the-art models.
Understanding Biosafety and Biosecurity in Ukraine

The Russian invasion of Ukraine in February 2022 was accompanied by unfounded Russian allegations of bioweapon activities in Ukraine conducted by the United States and its allies. While false, such allegations can cause substantial damage to disarmament efforts and international cooperation for strengthening disease surveillance and global health security. The purpose of this article is to describe Ukraine's biosafety, biosecurity, and dual-use policies and to provide important context for understanding the unwarranted Russian allegations. Moreover, the analysis of Ukraine's biorisk management system highlights some of the international efforts underway to ensure that life sciences research across the world is conducted safely, securely, and responsibly. With the help of international partners, Ukraine has strengthened its biorisk management governance, as well as identified areas for improvement that it is working to address.
(De)constructing machines as critical technical practice

This paper discusses the role of technology under the framework of Critical Technical Practice specifically in the form of constructing artefacts and deconstructing tools in order to produce what Philip Agre would describe as ‘reflexive work of critique’ (Agre, 1997:155). By presenting the activities and methods used in the teaching and shaping of undergraduate courses, this paper aims to show how technical objects, such as data, datasets, application programming interfaces and machine learning models, can be considered as discursive subjects, demonstrating pedagogical understanding across fields. The courses operate in the humanities tradition and take critical technical practice as a didactic approach, insofar as software and data are understood and manipulated on an instrumental level, while encouraging critical engagement and embodied reflection that bridge the technical and social/cultural domains. Within this pedagogical approach, critical is not only understood as a paradigm of rationality or quantitative, data-driven argumentation, but as adopting a critical position – that is, to research and reflect on the social structures and cultural phenomena entangled with digital objects, bodies, tools, methods and software production. By embracing work-in-progress and reflexive exploration, we aim to extend the notion of critical technical practice by unfolding how (de)constructing machines can be achieved beyond thinking of technology as neutral instrumentalisation. The challenge is how to find a balance, not only as researchers but as educators, unfolding aspects of both formality and functionality as well as questioning and understanding technology at a discursive and critical level. We argue that learning technical practice in an educational setting is not an end, but rather a means to question existing technological structures and create further changes in socio-technical systems.
FedRL: Improving the Performance of Federated Learning with Non-IID Data

Federated learning preserves data privacy by training machine learning models in a distributed fashion, where local models are trained on the client devices and aggregated on the server. Prevalent aggregation algorithms in federated learning perform well in homogeneous settings, but suffer from inadequate convergence in heterogeneous settings due to non-IID data distribution. In this paper, we explore the shortcomings of existing work and recognize that the memory loss of optimizers in aggregation steps limits convergence performance. In response, we propose FedRL, a new adaptive aggregation algorithm with the supervision of a policy-based deep reinforcement learning agent. Using real-world datasets, we evaluate the effectiveness of FedRL by comparing to state-of-the-art adaptive aggregation algorithms in the literature, and show its superiority in accelerating convergence to a target accuracy.
LidNet: Boosting Perception and Motion Prediction from a Sequence of LIDAR Point Clouds for Autonomous Driving

Autonomous driving is strongly contingent on perception and motion prediction for scene understanding. In this paper, we propose LIDAR Network (LidNet) to boost perception and motion prediction accuracy by redesigning MotionNet architecture. MotionNet is a new real-time encoder-decoder model that achieves joint perception and motion prediction at a pixel level. LidNet improves MotionNet performance by replacing every two spatial convolution layers in its encoder-decoder architecture with residual blocks and relies on average pooling rather than strided convolution for spatial reduction. In addition, we adjust the lateral skip connections linking encoders and decoders to result in a symmetric network. The global temporal maximum pooling layers on the lateral connections are replaced with temporal average pooling. Further, we introduce a center layer between the encoder-decoder architecture, with no spatial reduction applied at the lowest levels. Our extensive evaluation performed on the nuScenes dataset confirms that LidNet outperforms the state-of-the-art and operates in real-time.
SDN traffic anomaly detection method based on convolutional autoencoder and federated learning

With the rapid development of the Internet, people pay more and more attention to network security and data privacy. Using the characteristics of SDN data and control separation, it is easy to embed a traffic detection model in edge devices to achieve abnormal traffic detection. However, although the traditional intrusion detection model can provide good recognition accuracy, it requires many labeled samples for model training. Not only is it challenging to obtain labeled samples, but it also brings privacy issues. This paper combines federated learning and anomaly-based CAE model in the SDN network and realizes intrusion detection on encrypted traffic under the premise of effectively protecting data privacy and reducing the workload of data labeling. Furthermore, we design an aggregation model selection algorithm based on loss and data volume evaluation, which reduces the overall training time of the federation and improves the model's accuracy.
Addressing the Tangle Vulnerability: A Preventive Strategy for IOTA by Using Large Deviation Theory

IOTA is an emerging blockchain platform specially designed for the Internet of Things (IoT). It adopts a distributed ledger based on a directed acyclic graph (DAG) structure called the Tangle, achieving high concurrency, incremental scalability, and zero transaction fees. While the DAG-based ledger structure can promise many benefits for IOTA, it also exposes a wide attacking surface for the double-spending attack. By creating fake branches to replace the legitimate ones in the Tangle, the attackers can achieve the purpose of double-spending. Therefore, there is a great need for securing IOTA with preventive strategies. In this work, we present the design of an effective preventive strategy for IOTA that can reduce the Tangle vulnerability against the double-spending attack. Our main idea is to in-vestigate the Tangle vulnerability problem by using the large deviation theory. We provide the Tangle vulnerability probability and the decay speed of the probability. By evaluating the decay speed, we find the key factor related to the robustness level against double-spending attacks. Besides, we define and deduce the security threshold based on the evaluation results that can affect the probability decay speed of the Tangle vulnerability. With the security threshold, IOTA can efficiently inspect illegal Tangle branches to resist double-spending attacks. Finally, the comprehensive theoretical analysis and simulation experiments demonstrate that the proposed strategy is efficient and practical.
Securing Federated Learning against Overwhelming Collusive Attackers

In the era of a data-driven society with the ubiquity of Internet of Things (IoT) devices storing large amounts of data localized at different places, distributed learning has gained a lot of traction, however, assuming independent and identically distributed data (iid) across the devices. While relaxing this assumption that anyway does not hold in reality due to the heterogeneous nature of devices, federated learning (FL) has emerged as a privacy-preserving solution to train a collaborative model over non-iid data distributed across a massive number of devices. However, the appearance of malicious devices (attackers), who intend to corrupt the FL model, is inevitable due to unrestricted participation. In this work, we aim to identify such attackers and mitigate their impact on the model, essentially under a setting of bidirectional label flipping attacks with collusion. We propose two graph theoretic algorithms, based on Minimum Spanning Tree and k-Densest graph, by leveraging correlations between local models. Our FL model can nullify the influence of attackers even when they are up to 70% of all the clients whereas prior works could not afford more than 50% of clients as attackers. The effectiveness of our algorithms is ascertained through experiments on two benchmark datasets, namely MNIST and Fashion-MNIST, with overwhelming attackers. We establish the superiority of our algorithms over the existing ones using accuracy, attack success rate, and early detection round.
Service Chaining Graph: Latency- and Energy-aware Mobile VR Deployment over MEC Infrastructures

Perceptual studies show that the Quality of Service (QoS) of large-scale Mobile Virtual Reality (MVR) applications is positively correlated to video frame rate and the duration of the immersive experience. These metrics depend on the sum of computation and network latency needed to generate and deliver a video frame to the Head-Mounted Display (HMD) and the power consumption on the HMD. Recent research shows that Multi-access Edge Computing (MEC) can support mobile HMDs to reduce their computing latency, but its potential to maintain the acceptable end-to-end (E2E) latency and reduce power consumption under high mobility conditions remains unexplored. This paper proposes Service Chaining Graph (SCG), an orchestrator to split VR applications into atomic services and deploy them across HMDs and MEC servers according to an optimization problem that aims to jointly minimize latency and energy consumption. Through simulations, we show that SCG reduces E2E latency by up to 74% in three high-mobility user-dense scenarios compared to four widely used service-migration strategies against a moderate increase in power consumption. Unlike other approaches, SCG can find a balance between average latency and energy consumption by migrating services between MEC servers and HMDs according to a policy depending on application requirements.
Learning-based Multi-Objective Resource Allocation for Over-the-Air Federated Learning

Over-the-air federated learning (AirFL) has developed as a communication-efficient solution to enable distributed machine learning over edge devices by integrating computation and communication into a joint design. However, the aggregation error caused by noise and wireless channel fading may lead to a compromised learning performance in an AirFL system. Potential malicious participants can also cause this compromise such that decoding the individual uploaded model updates at the server side to perform security examination is still imperative for AirFL. In this paper, we investigate a novel multi-objective resource allocation problem to combat against such aggregation error and improve the information transmission efficiency of uplink by jointly optimizing the device selection, power allocation and receive scalar control. The objective of the multi-objective optimization problem (MOP) is to minimize the average mean squared error (MSE) of the over-the-air aggregation for different communication rounds and maximize the long-term energy efficiency (EE) of the system. Considering the complexity and dynamic environment, we present a deep reinforcement learning (DRL) based framework to solve the MOP. The MOP is firstly decomposed into multiple subproblems. Each subproblem is modelled as a neural network, which can be addressed by the proposed learning-based resource allocation (LRA) algorithm. The numerical results illustrate that our proposed approach can effectively tackle the MOP and outperform the benchmark approaches with appropriate training mechanism and reward design.
Communication-efficient Distributed Learning in V2X Networks: Parameter Selection and Quantization

In recent years, automotive systems have been integrating Federated Learning (FL) tools to provide enhanced driving functionalities, exploiting sensor data at connected vehicles to cooperatively learn assistance information for safety and maneuvering systems. Conventional FL policies require a central coordinator, namely a Parameter Server (PS), to orchestrate the learning process which limits the scalability and robustness of the training platform. Consensus-driven FL methods, on the other hand, enable fully decentralized learning implementations where vehicles mutually share the Machine Learning (ML) model parameters, possibly via Vehicle-to-Everything (V2X) networking, at the expense of larger communication resource consumption compared to vanilla FL approaches. This paper proposes a communication-efficient consensus-driven FL design tailored for the training of Deep Neural Networks (DNN) in vehicular networks. The vehicles taking part in the FL process independently select a pre-determined percentage of model parameters to be quantized and exchanged on each training round. The proposed technique is validated on a cooperative sensing use case where vehicles rely on Lidar point clouds to detect possible road objects/users in their surroundings via DNN. The validation considers latency, accuracy and communication efficiency trade-offs. Experimental results highlight the impact of parameter selection and quantization on the communication overhead in varying settings.
Representation Learning of Knowledge Graph for Wireless Communication Networks

With the application of the fifth-generation wireless communication technologies, more smart terminals are being used and generating huge amounts of data, which has prompted extensive research on how to handle and utilize these wireless data. Researchers currently focus on the research on the upper-layer application data or studying the intelligent transmission methods concerning a specific problem based on a large amount of data generated by the Monte Carlo simulations. This article aims to understand the endogenous relationship of wireless data by constructing a knowledge graph according to the wireless communication protocols, and domain expert knowledge and further investigating the wireless endogenous intelligence. We firstly construct a knowledge graph of the endogenous factors of wireless core network data collected via a 5G/B5G testing network. Then, a novel model based on graph convolutional neural networks is designed to learn the representation of the graph, which is used to classify graph nodes and simulate the relation prediction. The proposed model realizes the automatic nodes classification and network anomaly cause tracing. It is also applied to the public datasets in an unsupervised manner. Finally, the results show that the classification accuracy of the proposed model is better than the existing unsupervised graph neural network models, such as VGAE and ARVGE.