Reviewerly/exHarmony huggingface dataset:
Let's Take This Online: Adapting Scene Coordinate Regression Network Predictions for Online RGB-D Camera Relocalisation

Many applications require a camera to be relocalised online, without expensive offline training on the target scene. Whilst both keyframe and sparse keypoint matching methods can be used online, the former often fail away from the training trajectory, and the latter can struggle in textureless regions. By contrast, scene coordinate regression (SCoRe) methods generalise to novel poses and can leverage dense correspondences to improve robustness, and recent work has shown how to adapt SCoRe forests between scenes, allowing their state-of-the-art performance to be leveraged online. However, because they use features hand-crafted for indoor use, they do not generalise well to harder outdoor scenes. Whilst replacing the forest with a neural network and learning suitable features for outdoor use is possible, the techniques used to adapt forests between scenes are unfortunately harder to transfer to a network context. In this paper, we address this by proposing a novel way of leveraging a network trained on one scene to predict points in another scene. Our approach replaces the appearance clustering performed by the branching structure of a regression forest with a two-step process that first uses the network to predict points in the original scene, and then uses these predicted points to look up clusters of points from the new scene. We show experimentally that our online approach achieves state-of-the-art performance on both the 7-Scenes and Cambridge Landmarks datasets, whilst running in under 300ms, making it highly effective in live scenarios.
Multi-modal medical image segmentation based on vector-valued active contour models

Positron emission tomography (PET), magnetic resonance imaging (MRI) and computed tomography (CT) are widely utilized medical imaging modalities that provide essential anatomic and structural details. Many medical segmentation methods are not effective for a single-modal image of poor quality (e.g., low contrast in CT or low spatial resolution in PET). For practical radiotherapy treatment planning, multi-modal imaging information is regularly used. In this paper, a novel vector-valued active contour model is proposed to segment multi-modal medical images simultaneously for abnormal tissue regions. The method makes use of the functionality information and anatomical structure information advantages from each modality. Since each modality has its own signal characteristics, we use region-based information, combining hybrid mean intensities simultaneously. Furthermore, by utilizing a two-dimensional vector field with different image modalities, edge-based information is used to constrain the results of the image segmentation. The proposed approach is evaluated on datasets including lung PET-CT and brain MRI-CT images. Our qualitative and quantitative research results confirm the effectiveness of the proposed method.
Context-Sensitive Modeling and Analysis of Cyber-Physical Manufacturing Systems for Anomaly Detection and Diagnosis

Cyber-physical manufacturing systems (CPMS) can be defined by the integration of control, network communication, and computing with a physical manufacturing process.In this work, we present a hybrid model of CPMS combining sensor data, context information, and expert knowledge.We used the identification of global operational states and a multimodel framework to improve anomaly detection and diagnosis.The anomaly detection is based on context-sensitive adaptive threshold limits.Root cause diagnosis is based on classification models and expert knowledge.The proposed approach was implemented using the Internet of Things (IoT) to extract data from a computer numerical control machine.Results showed that using a context-sensitive modeling strategy allowed to combine physicsbased and data-driven models for residual analysis to detect an anomaly in the part, machine, or process.The identification of root cause was improved by adding context information in classification models to identify worn or broken tools and wrong material.Note to Practitioners-Anomaly detection and diagnosis of manufacturing equipment is a complex problem.Some of the challenges are complex machine dynamics and nonstationary operating conditions.This paper describes a framework for modeling manufacturing equipment using a combination of sensor data, context information, and system knowledge.The proposed modeling framework is used to improve anomaly detection for diagnostics using a context-sensitive strategy.This work aims to support more effective maintenance actions by identifying problems in the machine, part, or process.The modeling and anomaly detection strategy was used to identify anomalies in computer numerical control machines and can be extended to other equipment on the plant floor.
Learning to Jointly Generate and Separate Reflections

Existing learning-based single image reflection removal methods using paired training data have fundamental limitations about the generalization capability on real-world reflections due to the limited variations in training pairs. In this work, we propose to jointly generate and separate reflections within a weakly-supervised learning framework, aiming to model the reflection image formation more comprehensively with abundant unpaired supervision. By imposing the adversarial losses and combinable mapping mechanism in a multi-task structure, the proposed framework elegantly integrates the two separate stages of reflection generation and separation into a unified model. The gradient constraint is incorporated into the concurrent training process of the multi-task learning as well. In particular, we built up an unpaired reflection dataset with 4,027 images, which is useful for facilitating the weakly-supervised learning of reflection removal model. Extensive experiments on a public benchmark dataset show that our framework performs favorably against state-of-the-art methods and consistently produces visually appealing results.
Improving Supervised Phase Identification Through the Theory of Information Losses

This paper considers the problem of Phase Identification in power distribution systems. In particular, it focuses on improving supervised learning accuracies by focusing on exploiting some of the problem's information theoretic properties. This focus, along with recent advances in Information Theoretic Machine Learning (ITML), helps us to create two new techniques. The first transforms a bound on information losses into a data selection technique. This is important because phase identification data labels are difficult to obtain in practice. The second interprets the properties of distribution systems in the terms of ITML. This allows us to obtain an improvement in the representation learned by any classifier applied to the problem. We tested these two techniques experimentally on real datasets and have found that they yield phenomenal performance in every case. In the most extreme case, they improve phase identification accuracy from 51.7% to 97.3%. Furthermore, since many problems share the physical properties of phase identification exploited in this paper, the techniques can be applied to a wide range of similar problems.
Thermogravimetric analysis and chemometric based methods for soil examination: Application to soil forensics

The potential of thermogravimetric analysis combined with chemometric methods is explored for the forensic investigation of soil samples. In the present research, three milestones are achieved by thermal techniques; the identification of the patterns in the thermogram with their chemical interpretation; the development of indices to explain the extent of organic matter stability in relation with its thermal stability; and application of multivariate statistical analysis in the prediction of geographical regions of the soil. The characterization of soil samples gives an idea about the presence of organic/inorganic components. The thermal degradation of soil samples is observed by ATR-FTIR spectroscopy. The standard normal variate normalization is performed on the obtained dataset; it minimizes the variation caused by a varying amount of soil samples. The discrimination of soil samples is achieved by using multivariate algorithms including hierarchical cluster analysis (HCA), and principal component analysis (PCA). A linear discrimination function model (LDA) is developed for the classification of unknown soil samples into their respective geographical groups. The presented methodology provides a reproducible and unbiased identification and thus, makes thermal methods as attractive tools for the examination of soil/cement/clay related forensic cases.
Attention augmentation with multi-residual in bidirectional LSTM

Recurrent neural networks (RNNs) have been proven to be efficient in processing sequential data. However, the traditional RNNs have suffered from the gradient diminishing problem until the advent of Long Short-Term Memory (LSTM). However, LSTM is weak in capturing long-time dependency in sequential data due to the inadequacy of memory capacity in LSTM cells. To address this challenge, we propose an Attention-augmentation Bidirectional Multi-residual Recurrent Neural Network (ABMRNN) to overcome the deficiency. We propose an algorithm which integrates both past and future information at every time step with omniscient attention model. The multi-residual mechanism has also been leveraged in the proposed model targeting the pattern of the relationship between current time step and further distant time steps instead of only one previous time step. The results of experiments show that our model outperforms the traditional statistical classifiers and other existing RNN architectures.
Intersecting Imaginaries

Sociotechnical imaginaries are futures that people envision might be possible and desirable. They have a real impact on how systems are designed and what values they have embedded in their design. This article examines imaginaries about autonomous systems, decentralized systems, and decentralized autonomous systems. Through a discussion of the literature on autonomous and decentralized systems and how these imaginaries play out in the blockchain community based on my qualitative research, I demonstrate how decentralized autonomous systems are related to imaginaries about the organization of and the future of work. I identify three framings of imaginaries about autonomous systems: (1) autonomous technology as physical objects, (2) as mathematical rules, and (3) as artificial mangers. I also identify two sometimes conflicting framings of imaginaries about distributed and decentralized technology: these technologies as a new form of production and as freedom from control. These imaginaries intersect in decentralized autonomous systems, and I examine what they can tell us about the design and governance of such technologies. Lastly, I suggest ways of using the concept of imaginaries in participatory design.
Deep Multiple-Attribute-Perceived Network for Real-World Texture Recognition

Texture recognition is a challenging visual task as multiple perceptual attributes may be perceived from the same texture image when combined with different spatial context. Some recent works building upon Convolutional Neural Network (CNN) incorporate feature encoding with orderless aggregating to provide invariance to spatial layouts. However, these existing methods ignore visual texture attributes, which are important cues for describing the real-world texture images, resulting in incomplete description and inaccurate recognition. To address this problem, we propose a novel deep Multiple-Attribute-Perceived Network (MAP-Net) by progressively learning visual texture attributes in a mutually reinforced manner. Specifically, a multi-branch network architecture is devised, in which cascaded global contexts are learned by introducing similarity constraint at each branch, and leveraged as guidance of spatial feature encoding at next branch through an attribute transfer scheme. To enhance the modeling capability of spatial transformation, a deformable pooling strategy is introduced to augment the spatial sampling with adaptive offsets to the global context, leading to perceive new visual attributes. An attribute fusion module is then introduced to jointly utilize the perceived visual attributes and the abstracted semantic concepts at each branch. Experimental results on the five most challenging texture recognition datasets have demonstrated the superiority of the proposed model against the state-of-the-arts.
Digital Identity and the Blockchain: Universal Identity Management and the Concept of the “Self-Sovereign” Individual

While ‘classical’ human identity has kept philosophers busy since millennia, ‘Digital Identity’ seems primarily machine related. Telephone numbers, E-Mail inboxes, or Internet Protocol (IP)-addresses are irrelevant to define us as human beings at first glance. However, with the omnipresence of digital space the digital aspects of identity gain importance. In this submission, we aim to put recent developments in context and provide a categorization to frame the landscape as developments proceed rapidly. First, we present selected philosophical perspectives on identity. Secondly, we explore how the legal landscape is approaching identity from a traditional dogmatic perspective both in national and international law. After blending the insights from those sections together in a third step, we will go on to describe and discuss current developments that are driven by the emergence of new tools such as ‘Distributed Ledger Technology’ and ‘Zero Knowledge Proof’. One of our main findings is that the management of digital identity is transforming from a purpose driven necessity towards a self-standing activity that becomes a resource for many digital applications. In other words, whereas traditionally identity is addressed in a predominantly sectoral fashion whenever necessary, new technologies transform digital identity management into a basic infrastructural service, sometimes even a commodity. This coincides with a trend to take the ‘control’ over identity away from governmental institutions and corporate actors to ‘self-sovereign individuals’, who have now the opportunity to manage their digital self autonomously. To make our conceptual statements more relevant, we present several already existing use cases in the public and private sector. Subsequently, we discuss potential risks that should be mitigated in order to create a desirable relationship between the individual, public institutions, and the private sector in a world where self-sovereign identity management has become the norm. We will illustrate these issues along the discussion around privacy, as well as the development of backup mechanisms for digital identities. Despite the undeniable potential for the management of identity, we suggest that particularly at this point in time there is a clear need to make detailed (non-technological) governance decisions impacting the general design and implementation of self-sovereign identity systems.
Demonstrating Quantum Coherence and Metrology that is Resilient to Transversal Noise

Quantum systems can be exploited for disruptive technologies but in practice quantum features are fragile due to noisy environments. Quantum coherence, a fundamental such feature, is a basis-dependent property that is known to exhibit a resilience to certain types of Markovian noise. Yet, it is still unclear whether this resilience can be relevant in practical tasks. Here, we experimentally investigate the resilient effect of quantum coherence in a photonic Greenberger-Horne-Zeilinger state under Markovian bit-flip noise, and explore its applications in a noisy metrology scenario. In particular, using up to six-qubit probes, we demonstrate that the standard quantum limit can be outperformed under a transversal noise strength of approximately equal magnitude to the signal, providing experimental evidence of metrological advantage even in the presence of uncorrelated Markovian noise. This work highlights the important role of passive control in noisy quantum hardware, which can act as a low-overhead complement to more traditional approaches such as quantum error correction, thus impacting on the deployment of quantum technologies in real-world settings.
Cartel

As Multi-access Edge Computing (MEC) and 5G technologies evolve, new applications are emerging with unprecedented capacity and real-time requirements. At the core of such applications there is a need for machine learning (ML) to create value from the data at the edge. Current ML systems transfer data from geo-distributed streams to a central datacenter for modeling. The model is then moved to the edge and used for inference or classification. These systems can be ineffective because they introduce significant demand for data movement and model transfer in the critical path of learning. Furthermore, a full model may not be needed at each edge location. An alternative is to train and update the models online at each edge with local data, in isolation from other edges. Still, this approach can worsen the accuracy of models due to reduced data availability, especially in the presence of local data shifts.
Depth Based Semantic Scene Completion With Position Importance Aware Loss

Semantic scene completion (SSC) refers to the task of inferring the 3D semantic segmentation of a scene while simultaneously completing the 3D shapes. We propose PALNet, a novel hybrid network for SSC based on single depth. PALNet utilizes a two-stream network to extract both 2D and 3D features from multi-stages using fine-grained depth information to efficiently capture the context, as well as the geometric cues of the scene. Current methods for SSC treat all parts of the scene equally causing unnecessary attention to the interior of objects. To address this problem, we propose Position Aware Loss (PA-Loss) which is position importance aware while training the network. Specifically, PA-Loss considers Local Geometric Anisotropy to determine the importance of different positions within the scene. It is beneficial for recovering key details like the boundaries of objects and the corners of the scene. Comprehensive experiments on two benchmark datasets demonstrate the effectiveness of the proposed method and its superior performance. Code and demo 1 1 Video demo can be found here: https://youtu.be/j-LAMcMh0yg . are avaliable at https://github.com/UniLauX/PALNet .
Designing with Gaze

Recent developments in gaze tracking present new opportunities for social computing. This paper presents a study of Tama, a gaze actuated smart speaker. Tama was designed taking advantage of research on gaze in conversation. Rather than being activated with a wake word (such as "Ok Google") Tama detects the gaze of a user, moving an articulated 'head' to achieve mutual gaze. We tested Tama's use in a multi-party conversation task, with users successfully activating and receiving a response to over 371 queries (over 10 trials). When Tama worked well, there was no significant difference in length of interaction. However, interactions with Tama had a higher rate of repeated queries, causing longer interactions overall. Video analysis lets us explain the problems users had interacting with gaze. In the discussion, we describe implications for designing new gaze systems, using gaze both as input and output. We also discuss how the relationship to anthropomorphic design and taking advantage of learned skills of interaction. Finally, two paths for future work are proposed, one in the field of speech agents, and the second in using human gaze as an interaction modality more widely.
Practical Fully Secure Three-Party Computation via Sublinear Distributed Zero-Knowledge Proofs

Secure multiparty computation enables a set of parties to securely carry out a joint computation on their private inputs without revealing anything but the output. A particularly motivated setting is that of three parties with a single corruption (hereafter denoted 3PC). This 3PC setting is particularly appealing for two main reasons: (1) it admits more efficient MPC protocols than in other standard settings; (2) it allows in principle to achieve full security (and fairness). Highly efficient protocols exist within this setting with security against a semi-honest</> adversary; however, a significant gap remains between these and protocols with stronger security against a malicious</> adversary. In this paper, we narrow this gap within concretely efficient protocols. More explicitly, we have the following contributions: Concretely Efficient Malicious 3PC. We present an optimized 3PC protocol for arithmetic circuits over rings with (amortized) communication of 1 ring element per multiplication gate per party, matching the best semi-honest protocols. The protocol applies also to Boolean circuits, significantly improving over previous protocols even for small circuits. Our protocol builds on recent techniques of Boneh et al. (Crypto 2019) for sublinear zero-knowledge proofs on distributed data, together with an efficient semi-honest protocol based on replicated secret sharing (Araki et al., CCS 2016). We present a concrete analysis of communication and computation costs, including several optimizations. For example, for 40-bit statistical security, and Boolean circuit with a million (nonlinear) gates, the overhead on top of the semi-honest protocol can involve less than 0.5KB of communication for the entire circuit,</> while the computational overhead is dominated by roughly 30 multiplications per gate in the field F247. In addition, we implemented and benchmarked the protocol for varied circuit sizes. Full Security. We augment the 3PC protocol to further provide full security</> (with guaranteed output delivery) while maintaining amortized 1 ring element communication per party per multiplication gate, and with hardly any impact on concrete efficiency. This is contrasted with the best previous 3PC protocols from the literature, which allow a corrupt party to mount a denial-of-service attack without being detected.
Houdini's Escape

Linux Control Groups, i.e., cgroups, are the key building blocks to enable operating-system-level containerization. The cgroups mechanism partitions processes into hierarchical groups and applies different controllers to manage system resources, including CPU, memory, block I/O, etc. Newly spawned child processes automatically copy cgroups attributes from their parents to enforce resource control. Unfortunately, inherited cgroups confinement via process creation does not always guarantee consistent and fair resource accounting. In this paper, we devise a set of exploiting strategies to generate out-of-band</>workloads via de-associating processes from their original process groups. The system resources consumed by such workloads will not be charged to the appropriate cgroups. To further demonstrate the feasibility, we present five case studies within Docker containers to demonstrate how to break the resource rein of cgroups in realistic scenarios. Even worse, by exploiting those cgroups' insufficiencies in a multi-tenant container environment, an adversarial container is able to greatly amplify the amount of consumed resources, significantly slow-down other containers on the same host, and gain extra unfair advantages on the system resources. We conduct extensive experiments on both a local testbed and an Amazon EC2 cloud dedicated server. The experimental results demonstrate that a container can consume system resources (e.g., CPU) as much as $200\times$ of its limit, and reduce both computing and I/O performance of particular workloads in other co-resident containers by 95%.