Reviewerly/exHarmony huggingface dataset:
Locally Nameless Sets

This paper provides a new mathematical foundation for the locally nameless representation of syntax with binders, one informed by nominal techniques. It gives an equational axiomatization of two key locally nameless operations, "variable opening" and "variable closing" and shows that a lot of the locally nameless infrastructure can be defined from that in a syntax-independent way, including crucially a "shift" functor for name binding. That functor operates on a category whose objects we call locally nameless sets . Functors combining shift with sums and products have initial algebras that recover the usual locally nameless representation of syntax with binders in the finitary case. We demonstrate this by uniformly constructing such an initial locally nameless set for each instance of Plotkin's notion of binding signature. We also show by example that the shift functor is useful for locally nameless sets of a semantic rather than a syntactic character. The category of locally nameless sets is proved to be isomorphic to a known topos of finitely supported M-sets, where M is the full transformation monoid on a countably infinite set. A corollary of the proof is that several categories that have been used in the literature to model variable renaming operations on syntax with binders are all equivalent to each other and to the category of locally nameless sets.
Signal-independent RFF Identification for LTE Mobile Devices via Ensemble Deep Learning

Radio frequency fingerprint (RFF)-based wireless device authentication is an emerging technique to prevent potential spoofing attacks in wireless communications. The random access preamble of the physical random access channel (PRACH) in Long Term Evolution (LTE) systems is the first message sent from a user equipment (UE). However, PRACH preambles change under different evolved Node B (eNB), which will affect the RFF extraction. In this paper, a signal-independent RFF extraction method is first proposed to extract varying LTE PRACH preambles under different LTE eNBs. Residual transient segment (RTS) features from the varying PRACH preambles are extracted for RFF identification. A convolutional neural network (CNN) based ensemble deep learning scheme is proposed to integrate benefits from different RFF features. An experimental system under real operator LTE eNB is designed to capture and identify real UE signals. Experimental results show that the classification accuracy of five UEs can reach more than 95% under the same eNB and 85% under different eNBs. Furthermore, longtime evaluations show that the UE RTS feature is robust over time.
Affine Monads and Lazy Structures for Bayesian Programming

We show that streams and lazy data structures are a natural idiom for programming with infinite-dimensional Bayesian methods such as Poisson processes, Gaussian processes, jump processes, Dirichlet processes, and Beta processes. The crucial semantic idea, inspired by developments in synthetic probability theory, is to work with two separate monads: an affine monad of probability, which supports laziness, and a commutative, non-affine monad of measures, which does not. (Affine means that T (1)≅ 1.) We show that the separation is important from a decidability perspective, and that the recent model of quasi-Borel spaces supports these two monads. To perform Bayesian inference with these examples, we introduce new inference methods that are specially adapted to laziness; they are proven correct by reference to the Metropolis-Hastings-Green method. Our theoretical development is implemented as a Haskell library, LazyPPL.
My social comfort zone: Attachment anxiety shapes peripersonal and interpersonal space

Following positive social exchanges, the neural representation of interactive space around the body (peripersonal space; PPS) expands, whereas we also feel consciously more comfortable being closer to others (interpersonal distance; ID). However, it is unclear how relational traits, such as attachment styles, interact with the social malleability of our PPS and ID. A first, exploratory study (N=48) using a visuo-tactile, augmented reality task, found that PPS depended on the combined effects of social context and attachment anxiety. A follow-up preregistered study (N = 68), showed that those with high attachment anxiety demonstrated a sharper differentiation between peripersonal and extrapersonal space, even in a non-social context. A final, preregistered large-scale survey (N = 19,417) found that people scoring high in attachment anxiety prefer closer ID and differentiate their ID less based on feelings of social closeness. We conclude that attachment anxiety reduces the social malleability of both peripersonal and interpersonal space.
HyperDNE: Enhanced hypergraph neural network for dynamic network embedding

Representation learning provides an attractive opportunity to model the evolution of dynamic networks. However, the existing methods have two limitations: (1) most graph neural network-based methods fail to utilize the high-order proximity of nodes that captures the important properties of a network topology; (2) evolutionary dynamics-based methods are much fine-grained in modeling time information but neglect the coherence of dynamic networks, which leads to the model being susceptible to subtle noise. In this paper, we propose an enhanced hypergraph neural network framework for dynamic network embedding (HyperDNE) to tackle these issues. Specifically, we innovatively design a sequential hypergraph with dual-stream output to explore the group properties of nodes and edges, and a line graph neural network is added as an auxiliary enhancement scheme to further aggregate social influence from the degree of social convergence. Then, we compute the final embedding through attentions along the node and hyperedge levels to fuse multi-level variations in the network structure. The experimental results on six real networks demonstrate significant gains for HyperDNE over several state-of-the-art network embedding baselines. The dataset and source code of HyperDNE are publicly available at https://github.com/qhgz2013/HyperDNE.
MIFNet: Multiple instances focused temporal action proposal generation

Temporal action proposal generation (TAPG) serves as a promising solution for video analysis. However, the performance of existing methods is still far from satisfactory for real-world applications. We attribute it to a crucial issue, i.e., hard multiple instances. In this paper, we investigate why this is the case. We discover that when processing multiple instances videos, mainstream approaches always recognize multiple instances as one instance due to boundary ambiguity or ignoring insignificant backgrounds between these instances. To address this problem, we propose a Multiple Instances Focused Network(MIFNet) that improves the quality of action proposals by considering boundary correlations and fusing multi-scale proposals. In particular, we first propose a pure boundary embedding module named Boundary Constraint Module (BCM) for suppressing the generation of hard negatives proposal by evaluating boundary correlation. The BCM introduces a boundary contrastive learning strategy that can pull the positive boundary pairs' representation closer and push the negative pairs' representation away. Then, a Proposal Blending Module (PBM) is proposed, which augments the proposal-level representation by modeling information among multi-scale proposals so that proposals can be complemented with local details as well as global information. The experimental results on the ActivityNet-v1.3 and THUMOS14 benchmarks demonstrate that MIFNet outperforms the state-of-the-arts.
An IoU-aware Siamese network for real-time visual tracking

Most Siamese trackers decompose the tracking task into two branches: classification and bounding box regression. They choose the bounding box with the highest classification score or the combination of the classification and predicted localization scores as the target. However, the misalignment between the classification and localization accuracy will degrade tracking performance. In this paper, we propose an IoU-aware Siamese tracker named IASNet, which predicts the IoU classification score of each regressed box to represent its localization confidence. We introduce a novel residual alignment module to capture the scale of each predicted bounding box and its border context, which further improves the reliability of the classification prediction. In addition, we build dynamic links between the classification and regression branches to enhance their interaction. The proposed dynamic loss enables the training to focus on high-quality positive samples, leading to more precise localization. Extensive experimental results on VOT2016, VOT2019, OTB100, GOT10k, UAV123, and LaSOT show that the proposed IASNet achieves state-of-the-art tracking performance and runs at approximately 65 fps on a GTX 3090 GPU, which far exceeds the real-time requirement.
Data reduction via multi-label prototype generation

A very common practice to speed up instance based classifiers is to reduce the size of their training set, that is, replace it by a condensing set, hoping that their accuracy will not worsen. This can be achieved by applying a Prototype Selection or Generation algorithm, also referred to as a Data Reduction Technique. Most of these techniques cannot be applied on multi-label problems, where an instance may belong to more than one classes. Reduction through Homogeneous Clustering (RHC) and Reduction by Space Partitioning (RSP3) are parameter-free single-label Prototype Generation algorithms. Both are based on recursive data partitioning procedures that identify homogeneous clusters of training data, which they replace by their representatives. This paper proposes variations of these algorithms for multi-label training datasets. The proposed methods generate multi-label prototypes and inherit all the desirable properties of their single-label versions. They consider clusters that contain instances that share at least one common label as homogeneous clusters. It is shown via an experimental study based on nine multi-label datasets that the proposed algorithms achieve good reduction rates without negatively affecting classification accuracy.
ShuffleNetv2-YOLOv3: a real-time recognition method of static sign language based on a lightweight network

To better meet the communication needs of hearing impaired people and the public, it is of great significance to recognize sign language more quickly and accurately on embedded platforms and mobile terminals. YOLOv3, raised by Joseph Redmon and Ali Farhadi in 2018, achieved a great improvement in detection speed with considerable accuracy by optimizing Yolo. However, YOLOv3 is still too bloated to use on mobile terminals. A static sign language recognition method based on the ShuffleNetv2-YOLOv3 lightweight model was proposed. The ShuffleNetv2-YOLOv3 lightweight model makes the network lightweight by using ShuffleNetv2 as the backbone network of YOLOv3. The lightweight network improved the recognition speed steeply. Combing with the CIoU loss function, the ShuffleNetv2-YOLOv3 keeps the recognition accuracy while improving the recognition speed. Recognition effectiveness of the self-made sign language images and public database by the ShuffleNetv2-YOLOv3 lightweight model was evaluated by F1 score and mAP value. The performance of the ShuffleNetv2-YOLOv3 model was compared with that of the YOLOv3-tiny, SSD, Faster-RCNN, and YOLOv4-tiny model, respectively. The experimental results show that the proposed ShuffleNetv2-YOLOv3 model achieved a good balance between the accuracy and speed of the gesture detection under the premise of model lightweight. The F1 score and mAP value of the ShuffleNetv2-YOLOv3 model were 99.1% and 98.4%, respectively. The gesture detection speed on the GPU reaches 54 frames per second, which is better than other models. The mobile terminal application of the proposed lightweight model was also evaluated. The minimal inference speed of single frame images on the CPU and GPU is 0.14 and 0.025 s per image, respectively. It is only 1/6.5 and 1/8.5 of the running speed of the original YOLOv3 model. The ShuffleNetv2-YOLOv3 lightweight model is conducive to quick, real time, and similar static sign language gesture recognition, laying a good foundation for real-time gesture recognition in the embedded platforms and mobile terminals.
Immersive movies: the effect of point of view on narrative engagement

Abstract Cinematic virtual reality (CVR) offers filmmakers a wide range of possibilities to explore new techniques regarding movie scripting, shooting and editing. Despite the many experiments performed so far both with both live action and computer-generated movies, just a few studies focused on analyzing how the various techniques actually affect the viewers’ experience. Like in traditional cinema, a key step for CVR screenwriters and directors is to choose from which perspective the viewers will see the scene, the so-called point of view (POV). The aim of this paper is to understand to what extent watching an immersive movie from a specific POV could impact the narrative engagement (NE), i.e., the viewers’ sensation of being immersed in the movie environment and being connected with its characters and story. Two POVs that are typically used in CVR, i.e., first-person perspective (1-PP) and external perspective (EP), are investigated through a user study in which both objective and subjective metrics were collected. The user study was carried out by leveraging two live action 360° short films with distinct scripts. The results suggest that the 1-PP experience could be more pleasant than the EP one in terms of overall NE and narrative presence, or even for all the NE dimensions if the potential of that POV is specifically exploited.
Mulvernet: Nucleus Segmentation and Classification of Pathology Images Using the HoVer-Net and Multiple Filter Units

Nucleus segmentation and classification are crucial in pathology image analysis. Automated nuclear classification and segmentation methods support analysis and understanding of cell characteristics and functions, and allow the analysis of large-scale nuclear forms in the diagnosis and treatment of diseases. Common problems in these tasks arise from the inconsistent sizes and shapes of the cells in each pathology image. This study aims to develop a new method to address these problems based primarily on the horizontal and vertical distance network (HoVer-Net), multiple filter units, and attention gate mechanisms. The results of the study will significantly impact cell segmentation and classification by showing that a multiple filter unit improves the performance of the original HoVer-Net model. In addition, our experimental results show that the Mulvernet achieves outperforming results in both nuclei segmentation and classification compared to several methods. The ability to segment and classify different types of nuclei automatically has a direct influence on further pathological analysis, offering great potential not only to accelerate the diagnostic process in clinics but also for enhancing our understanding of tissue and cell properties to improve patient care and management.
Classification regularized dimensionality reduction improves ultrasound thyroid nodule diagnostic accuracy and inter-observer consistency

Convolutional Neural Networks (CNNs) for medical image analysis usually only output a probability value, providing no further information about the original image or inter-relationships between different images. Dimensionality Reduction Techniques (DRTs) are used for visualization of high dimensional medical image data, but they are not intended for discriminative classification analysis.We develop an interactive phenotype distribution field visualization system for medical images to accurately reflect the pathological characteristics of lesions and their similarity to assist radiologists in diagnosis and medical research.We propose a novel method, Classification Regularized Uniform Manifold Approximation and Projection (UMAP) referred as CReUMAP, combining the advantages of CNN and DRT, to project the extracted feature vector fused with the malignant probability predicted by a CNN to a two-dimensional space, and then apply a spatial segmentation classifier trained on 2614 ultrasound images for prediction of thyroid nodule malignancy and guidance to radiologists.The CReUMAP embedding correlates well with the TI-RADS categories of thyroid nodules. The parametric version that embeds external test dataset of 303 images in presence of the training data with known pathological diagnosis improves the benign and malignant nodule diagnostic accuracy (p-value = 0.016) and confidence (p-value = 1.902 × 10-6) of eight radiologists of different experience levels significantly as well as their inter-observer agreements (kappa≥0.75). CReUMAP achieve 90.8% accuracy, 92.1% sensitivity and 88.6% specificity in test set.CReUMAP embedding is well correlated with the pathological diagnosis of thyroid nodules, and helps radiologists achieve more accurate, confident and consistent diagnosis. It allows a medical center to generate its locally adapted embedding using an already-trained classification model in an updateable manner on an ever-growing local database as long as the extracted feature vectors and predicted diagnostic probabilities of the correspondent classification model can be outputted.
Resource and Heterogeneity-aware Clients Eligibility Protocol in Federated Learning

Federated Learning (FL) is a new paradigm of Machine Learning (ML) that enables on-device computation via decentralized data training. However, traditional FL algorithms impose strict requirements on the clients' selection and its ratio. Moreover, the data training becomes inefficient when the client's computational resources are limited. Towards this goal, we aim to extend FL, a decentralized learning framework that efficiently works with heterogeneous clients in practical industrial scenarios. To this end, we propose a Clients' Eligibility Protocol (CEP), a resource-aware FL solution, for a heterogeneous environment. To this end, we use a Trusted Authority (TA) between the clients and the cloud server, which calculates the client's eligibility score based on local computing resources such as bandwidth, memory, and battery life and selects the most resourceful clients for training. If a client gives a slow response or infuses an incorrect model, the TA declares that the client is ineligible for future training. Besides, the proposed CEP leverages the asynchronous FL model, which avoids a long delay in a client's response. The empirical results proves that the proposed CEP gains the benefits of resource-aware clients selection and achieves 88 % and 93 % of accuracy on AlexNet and LeNet, respectively.
TrafficGCN: Mobile Application Encrypted Traffic Classification Based on GCN

With the gradual adoption of 4G and 5G communication technologies, the number of mobile devices has increased dramatically. Identifying apps can provide technical support for fine-grained network management or optimizing the quality of network connections. The current development of new technologies, such as HTTPS and content delivery networks (CDN) technology, presents new challenges to mobile application classification. Existing techniques either ignore the implicit graph relationships in the traffic or lack comprehensive traffic features analysis, resulting in poor classification accuracy or inapplicability to large-scale data. In this paper, we propose TrafficGCN, a novel mobile application classification technique to solve the above problem. TrafficGCN constructs communication behavior graphs by combining packet-level and flow-level traffic data. The graph convolutional neural network (GCN) is then used to learn a large number of graph connectivity relations and node properties generated by different applications. In addition, we present a traffic graph dataset for mobile application classification. Comparing TrafficGCN with traditional deep learning algorithms (DNN, CNN, LSTM) and the recently developed techniques (MAppGraph, FlowPrint, AppScanner), the experimental results show that it significantly improves classification performance 5.44%-18.72% in various metrics. These results demonstrate that TrafficGCN has great potential for mobile network management,
Joint Routing, Channel, and Key-Rate Assignment for Resource-Efficient QKD Networking

Quantum Key Distribution (QKD) is a recent technology for secure distribution of symmetric keys, which is currently being deployed to increase communications security against quantum attacks. However, the key rate achievable over a weak quantum signal is limited by the link performance (e.g., loss and noise) and propagation distance, especially in multi-node QKD networks, making it necessary to design a scheme to efficiently and timely distribute keys to the various nodes. In this work, we formulate, using a Mixed Integer Linear Programming (MILP) model, a novel Routing, Channel, and Key-rate Assignment (RCKA) problem for QKD with Quantum Key Pool (QKP), which exploits the opportunity of using trusted relays and optical bypass. Our formulation accounts for the possibility to build a quantum key distribution path that combines both quantum channels and trusted relays to increase the acceptance ratio of key rate requests. Leveraging different versions of the proposed MILP model, we evaluate several strategies exploiting different combinations of trusted relays and optical bypass for the RCKA problem. Results show how different trade-offs between security and resource-efficiency (expressed in terms of acceptance ratio of key rate requests vs. key storing rate in QKP) can be achieved when adopting trusted-relay and/or optical-bypass technologies. Trusted relays can provide a higher acceptance ratio when the number of QKD modules (transmitters or receivers) is sufficiently large, while optical bypass, which does not require the implementation of expensive trusted relays, is preferable when the number of QKD modules is a limiting factor.
Intent-based multi-agent reinforcement learning for service assurance in cellular networks

Recently, intent-based management is receiving good attention in telecom networks owing to stringent performance requirements for many of the use cases. Several approaches on the literature employ traditional methods in the telecom domain to fulfill intents on the KPIs, which can be defined as a closed loop. However, these methods consider every closed-loop independent of each other which degrades the combined closed-loop performance. Also, when many closed loops are needed, these methods are not easily scalable. Multi-agent reinforcement learning (MARL) techniques have shown significant promise in many areas in which traditional closed-loop control falls short, typically for complex coordination and conflict management among loops. In this work, we propose a method based on MARL to achieve intent-based management without the requirement of the model of the underlying system. Moreover, when there are conflicting intents, the MARL agents can implicitly incentivize the loops to cooperate, without human interaction, by prioritizing the important KPIs. Experiments have been performed on a network emulator on optimizing KPIs for three services and we observe the proposed system performs well and is able to fulfill all existing intents when there are enough resources or prioritize the KPIs when there are scarce resources.