Reviewerly/exHarmony huggingface dataset:
Each Snapshot to Each Space: Space Adaptation for Temporal Knowledge Graph Completion

Temporal knowledge graphs (TKGs) organize and manage the dynamic relations between entities over time. Inferring missing knowledge in TKGs, known as temporal knowledge graph completion (TKGC), has become an important research topic. Previous models handle all facts with different timestamps in an identical latent space, even though the semantic space of the TKG changes over time. Therefore, they are not effective to reflect the temporality of knowledge. To effectively learn the time-aware information of TKGs, different latent spaces are adapted for temporal snapshots at different timestamps, which yields a novel model, i.e., Space Adaptation Network (SANe). Specifically, we extend convolutional neural networks (CNN) to map the facts with different timestamps into different latent spaces, which can effectively reflect the dynamic variation of knowledge. Meanwhile, a time-aware parameter generator is designed to explore the overlap of latent spaces, which endows CNN with specific parameters in term of the context of timestamps. Therefore, knowledge in adjacent time intervals is efficiently shared to boost the performance of TKGC, which can learn the validity of knowledge over a period of time. Extensive experiments demonstrate that SANe achieves state-of-the-art performance on four well-established benchmark datasets for temporal knowledge graph completion.
Coalition Formation Game in the Cross-Silo Federated Learning System

In cross-silo federated learning (FL), organizations cooperatively train a global model with their local data. The organizations, however, own different datasets and may be heterogeneous in terms of their expectation on the precision of the global model. Meanwhile, the cost of secure global model aggregation, including computation and communication, is proportional to the square of the number of organizations in the FL system. In this paper, we consider all organizations in the FL system as a grand coalition. We introduce a novel concept from coalition game theory which allows the dynamic formation of coalitions among organizations. A simple and distributed merge and split algorithm for coalition formation is constructed. The aim is to find an ultimate coalition structure that allows cooperating organizations to maximize their utilities in consideration of the coalition formation cost. Through this novel game theoretical framework, the FL system is able to self-organize and form a structured network composed of disjoint stable coalitions. To fairly distribute cost in each formed coalition, a cost sharing mechanism is proposed to align members' individual utility with their coalition's utility. In FL systems, training data has a significant impact on model performances, i.e., it should lead to a more precise global model if organizations with greater data complementarity are grouped. Numerical evaluations are presented to verify the proposed models.
A Two-Stage Method for Fine-Grained DNS Covert Tunnel Behavior Detection

DNS protocol is one of the most important protocols on the Internet. Network firewall and intrusion detection system generally do not block the DNS protocol, and a large number of malwares use DNS protocol as the covert tunnel for remote control. Therefore, it is very important to enhance the detection of DNS covert tunnel. At present, the researches always select closed data set to detect different tunnel tools and can not distinguish the specific attack behaviors such as download in the tunnel. This paper proposes a two-stage method for DNS covert tunnel behavior identification, which mainly identifies the DNS covert tunnel traffic and the different behaviors in the tunnel in a real-world network environment. In the first stage, by measuring and analyzing a large number of DNS traffic, the core feature threshold method is proposed to distinguish normal and tunnel traffic. The second stage mainly analyzes five kinds of DNS tunnel behaviors and classifies them with machine learning methods. Finally, more than 50 millions normal DNS sessions traffic is collected from the real network environment, and tunnel traffic is produced by DNS covert tunnel tools. Through experiments, using threshold method, the precision and recall rate of DNS covert tunnel detection reach 99 $$\%$$ , and time processing efficiency is much higher than machine learning methods. In terms of behavior recognition, the precision and recall of five behaviors reach to 93 $$\%$$ and 94 $$\%$$ .
Intelligent PID Temperature Control Using Output Recurrent Fuzzy Broad Learning System for Nonlinear Time-Delay Dynamic Systems

This paper presents a novel adaptive predictive proportional-integral-derivative (PID) control using a new output recurrent fuzzy broad learning system (ORFBLS) for setpoint control of a class of nonlinear discrete-time dynamic systems with time delay. The proposed controller, abbreviated as ORFBLS-APPID, is composed of an ORFBLS identifier for online parameter tuning and estimation, and an adaptive predictive ORFBLS-PID control for accurate setpoint tracking and disturbance rejection. The three-term gains of the PID controller are automatically tuned by an ORFBLS. The set-point tracking of the proposed ORFBLS-APPID control method is well exemplified by conducting simulations for two well-known nonlinear discrete-time dynamic systems with time delay, thus showing its effectiveness and superiority.
Investigating The Effect of Direction on The Limits of Haptic Retargeting

Haptic Retargeting enables spatially decoupled physical objects to provide haptic feedback for multiple virtual objects in Virtual Reality (VR). By decoupling the virtual hand from its real position, through Hand Redirection, multiple virtual objects can be mapped to a single physical proxy. However, redirection beyond a detectable level is disruptive to the user experience. The limits of haptic retargeting have mainly been explored in one primary direction—the user reaching forwards. We designed an experiment with participants performing reaching movements across 8 reaching directions in the horizontal plane, with a hand redirection of up to 30°. We identify an overall haptic retargeting limit and find that a physical proxy can be remapped to virtual objects of up to 16.14° away. We find a significant effect of reaching direction on the limit. In practice, however, these differences are small, measuring only a couple of degrees, translating to approximately 1cm across a 30cm reach. We argue that, while the psychology literature might suggest the need for specific directional limits and while we do find an effect of direction on retargeting limits, interaction designers can mitigate these requirements by applying slightly conservative global retargeting limits. Our contributions further the community’s knowledge of both how to deploy haptic retargeting in interaction without compromising the user’s experience and how visual and proprioceptive cues interact in peripersonal space in VR.
The role of diversity in cybersecurity risk analysis

Cybersecurity threat and risk analysis (RA) approaches are used to identify and mitigate security risks early-on in the software development life-cycle. Existing approaches automate only parts of the analysis procedure, leaving key decisions in identification, feasibility and risk analysis, and quality assessment to be determined by expert judgement. Therefore, in practice teams of experts manually analyze the system design by holding brainstorming workshops. Such decisions are made in face of uncertainties, leaving room for biased judgement (e.g., preferential treatment of category of experts). Biased decision making during the analysis may result in unequal contribution of expertise, particularly since some diversity dimensions (i.e., gender) are underrepresented in security teams. Beyond the work of risk perception of non-technical threats, no existing work has empirically studied the role of diversity in the risk analysis of technical artefacts. This paper proposes an experimental plan for identifying the key diversity factors in RA.
Jaco: An Offline Running Privacy-aware Voice Assistant

With the recent advance in speech technology, smart voice assistants have been improved and are now used by many people.But often these assistants are running online as a cloud service and are not always known for a good protection of users' privacy.This paper presents the architecture of a novel voice assistant, called Jaco, with the following features: (a) It can run completely offline, even on low resource devices like a RaspberryPi.(b) Through a skill concept it can be easily extended.(c) The architectural focus is on protecting users' privacy, but without restricting capabilities for developers.(d) It supports multiple languages.(e) It is competitive with other voice assistant solutions.In this respect the assistant combines and extends the advantages of other approaches.
Formal Methods Meet Machine Learning (F3ML)

The field of machine learning focuses on computationally efficient, yet approximate algorithms. On the contrary, the field of formal methods focuses on mathematical rigor and provable correctness. Despite their superficial differences, both fields offer mutual benefit. Formal methods offer methods to verify and explain machine learning systems, aiding their adoption in safety critical domains. Machine learning offers approximate, computationally efficient approaches that let formal methods scale to larger problems. This paper gives an introduction to the track “Formal Methods Meets Machine Learning” (F3ML) and shortly presents its scientific contributions, structured into two thematic subthemes: One, concerning formal methods based approaches for the explanation and verification of machine learning systems, and one concerning the employment of machine learning approaches to scale formal methods.
High Quality Superpixel Generation Through Regional Decomposition

Superpixel generation is increasingly an important area for computer vision tasks. While superpixels with highly regular shapes are preferred to make the subsequent processing easier, the accuracy of the superpixel boundaries is also necessary. Previous methods usually depend on a distance function considering both spatial and color coherency regularization on the whole image, which however is hard to balance between shape regularity and boundary adherence, especially when the desired number of superpixels is small. In addition, non-adaptive parameters and insufficient contour information also affect the performance of segmentation. To mitigate these problems, we propose a robust divide-and-conquer superpixel segmentation method, of which the core idea is that we apply a new contour information extraction and a pixel clustering to separate the input image into flat and non-flat regions, where the former targets shape regularity and the latter emphasizes boundary adherence, followed by an efficient hierarchical merging to clean up tiny and dangling superpixels. Our algorithm requires no additional parameter tuning except the desired number of superpixels since our internal parameters are self-adaptive to the image contents. Experimental results demonstrate that for public benchmark datasets, our algorithm consistently generates more regular superpixels with stronger boundary adherence than state-of-the-art methods while maintaining a competitive efficiency. The source code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/YunyangXu/HQSGRD</uri> .
Thwarting Longitudinal Location Exposure Attacks in Advertising Ecosystem via Edge Computing

As geo-location data has been increasingly adopted as a high-profile feature in targeted advertising, exposing user real locations to untrusted cloud services or advertisers has raised severe privacy concerns. To protect location privacy with formal guarantee, a wide-stretched line of recent studies focuses on injecting controlled geo-indistinguishability (geo-IND) noise as per each location exposure. However, in advertising, over the course of 2 years, a single user can report and contribute near 1k location data points on average, which allows a longitudinal attacker to infer some statistics from the perturbed locations.In this study, we demonstrate the above-mentioned privacy risk via revealing an inference attack mechanism, coined as a longitudinal location exposure attack. This novel attack illustrates the possibility of recovering 75%∼90% of user top-1 locations (within only 200-meter range) among 37k users. In light of this deficiency, we propose a novel edge-assisted location privacy protection system, entitled Edge-PrivLocAd, that is adapted to location-based advertising. The novelty of Edge-PrivLocAd stems from our n-fold Gaussian mechanism, which adds permanent noise to the statistical user location profile and thus can defend against longitudinal attackers while balancing the privacy-utility trade-off. In addition, our system incorporates a posterior-based sampling technique into the location re-mapping process, that boosts location utility without privacy loss. We develop a fully-functioning prototype and empirically evaluate the proposed system. Our experimental results show that Edge-PrivLocAd is practical and scalable in real-world scenarios.
Dynamic Kernel Selection for Improved Generalization and Memory Efficiency in Meta-learning

Gradient based meta-learning methods are prone to overfit on the meta-training set, and this behaviour is more prominent with large and complex networks. Moreover, large networks restrict the application of meta-learning models on low-power edge devices. While choosing smaller networks avoid these issues to a certain extent, it affects the overall generalization leading to reduced performance. Clearly, there is an approximately optimal choice of network architecture that is best suited for every meta-learning problem, however, identifying it beforehand is not straight-forward. In this paper, we present Metadock, a task-specific dynamic kernel selection strategy for designing compressed CNN models that generalize well on unseen tasks in meta-learning. Our method is based on the hypothesis that for a given set of similar tasks, not all kernels of the network are needed by each individual task. Rather, each task uses only a fraction of the kernels, and the selection of the kernels per task can be learnt dynamically as a part of the inner update steps. Metadockcompresses the meta-model as well as the task-specific inner models, thus providing significant reduction in model size for each task, and through constraining the number of active kernels for every task, it implicitly mitigates the issue of meta-overfitting. We show that for the same inference budget, pruned versions of large CNN models obtained using our approach consistently outperform the conventional choices of CNN models. Metadock couples well with popular meta-learning approaches such as iMAML [22]. The efficacy of our method is validated on CIFAR-fs [1] and mini-ImageNet [28] datasets, and we have observed that our approach can provide improvements in model accuracy of up to 2% on standard meta-learning benchmark, while reducing the model size by more than 75%. Our code is available at https://github.com/transmuteAI/MetaDOCK.
SOMSI: Spherical Novel View Synthesis with Soft Occlusion Multi-Sphere Images

Spherical novel view synthesis (SNVS) is the task of estimating 360 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">○</sup> views at dynamic novel views given a set of 360 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">○</sup> input views. Prior arts learn multi-sphere image (MSI) representations that enable fast rendering times but are only limited to modelling low-dimensional color values. Modelling high-dimensional appearance features in MSI can result in better view synthesis, but it is not feasible to represent high-dimensional features in a large number (> 64) of MSI spheres. We propose a novel MSI representation called Soft Occlusion MSI (SOMSI) that enables modelling high-dimensional appearance features in MSI while retaining the fast rendering times of a standard MSI. Our key insight is to model appearance features in a smaller set (e.g. 3) of occlusion levels instead of larger number of MSI levels. Experiments on both synthetic and real-world scenes demonstrate that using SOMSI can provide a good balance between accuracy and run-time. SOMSI can produce considerably better results compared to MSI based MODS [1], while having similar fast rendering time. SOMSI view synthesis quality is on-par with state-of-the-art NeRF [24] like model while being 2 orders of magnitude faster. For code, additional results and data, please visit https://tedyhabtegebrial.github.io/somsi.
Deep Sparse and Low-Rank Prior for Hyperspectral Image Denoising

Spectral and spatial correlation in hyperspectral images (HSIs) can be exploited in HSI processing because it directly induces a sparse and low-rank prior via linear transformations. Researchers have used the sparse and low-rank prior as an image prior for HSI restoration, such as denoising, deblurring, and super-resolution. This paper proposes a HSI denoising method that incorporates a sparse and low-rank prior with a deep image prior (DIP). The sparse and low-rank prior is obtained using the 2-dimensional discrete wavelet transform (2-D DWT), and singular value decomposition (SVD), while the DIP is provided by the structure of a convolutional neural network (CNN). The combination of a sparse and low-rank prior with a DIP views the CNN-based denoising method similar to a model-based method, inheriting the advantages of both model-based and CNN-based methods. Experimental results with simulated and real HSI datasets show that the proposed method outperforms the conventional sparse and low-rank based methods in both quantitative and qualitative performance. Codes are available at https://github.com/hvn2/DIP-SLR
DSP-Based Traffic Target Detection for Intelligent Transportation

Internet of Things (IoT)-based intelligent transportation is attracting more and more attention. As a key component of intelligent transportation, traffic video monitoring is very important, in which vehicle and pedestrian detection on the road is a crucial task. Although vehicle and pedestrian detection through deep learning (DL) may achieve high accuracy, it tends to require high computing resources, which hinders its use on IoT devices. As an important class of IoT devices, digital signal processor (DSP) has the characteristics of low energy consumption, small size, and strong performance, which has been widely used in intelligent transportation. In order to use DL on DSP for accurate vehicle and pedestrian detection, we first propose a series of general tactics to optimize the object detection convolutional neural network (CNN) model, including convolution layer optimization, cache optimization, compiler optimization, intrinsics optimization and direct memory access (DMA) acceleration, and then a parallel scheme to extend the model to run on multicore, and further quantize the implementation of the model. We evaluate it on UA-DETRAC and KITTI datasets. Experimental results show that our method achieves a faster speed than running the same CNN model on a mainstream desktop CPU, with only 0.06% accuracy loss.
Unsupervised Representation Learning for Binary Networks by Joint Classifier Learning

Self-supervised learning is a promising unsupervised learning framework that has achieved success with large floating point networks. But such networks are not readily deployable to edge devices. To accelerate deployment of models with the benefit of unsupervised representation learning to such resource limited devices for various downstream tasks, we propose a self-supervised learning method for binary networks that uses a moving target network. In particular, we propose to Jointly train a randomly initialized classifier, attached to a pretrained floating point feature extractor, with a binary network. Additionally, we propose a feature similarity loss, a dynamic loss balancing and modified multi-stage training to further improve the accuracy, and call our method BURN. Our empirical validations over five downstream tasks using seven datasets show that BURN outperforms self-supervised baselines for binary networks and sometimes outperforms supervised pretraining. Code is availabe at https://github.com/naver-ai/burn.
Particle Filters in Latent Space for Robust Deformable Linear Object Tracking

Tracking of deformable linear objects (DLOs) is important for many robotic applications. However, achieving robust and accurate tracking is challenging due to the lack of distinctive features or appearance on the DLO, the object's high-dimensional state space, and the presence of occlusion. In this letter, we propose a method for tracking the state of a DLO by applying a particle filter approach within a lower-dimensional state embedding learned by an autoencoder. The dimensionality reduction preserves state variation, while simultaneously enabling a particle filter to accurately track DLO state evolution with a practically feasible number of particles. Compared to previous works, our method requires neither running a high-fidelity physics simulation, nor manual designs of constraints and regularization. Without the assumption of knowing the initial DLO state, our method can achieve accurate tracking even under complex DLO motions and in the presence of severe occlusions.
Cross-Project Software Defect Prediction Based on Class Code Similarity

Software defect prediction techniques can help software developers find software defects as soon as possible.It can also reduce the cost of software development.This technique usually predicts the target project through the entire source project.However, the data distribution difference between the entire source project and the target project is generally large, so the software defect prediction accuracy is not high.we propose a crossproject software defect prediction technique based on class code similarity CCS-CPDP.Firstly, this technique converts the code set extracted by AST(Abstract Syntax Tree) into a vector set through the DTI (Doc2Bow and TF-IDF) strategy; Secondly, the similarity will be calculated between the vector set of target items and training items; Finally, according to the principle of the majority decision subordinate category in KNN, the number of most similar class instances of the training item is determined, and the source item is refined by selecting the class instance, thereby we can realize software defect prediction.This method compares with four traditional classification models (KNN, Random Forest, Naive Bayes, and Logistic Regression) for defect prediction.Experimental results show that CCS-CPDP compared with the baseline, recall and f1 increased by 18.03% and 14.1% respectively.In addition, the refined source projects selected by CCS-CPDP compared with the current source project selection technology, recall and f1 had improved by 37.6% and 12.7%.