Reviewerly/exHarmony huggingface dataset:
Towards Self-Managing Cloud Storage with Reinforcement Learning

Cloud storage services are often associated with various performance issues due to load imbalance, interference from background tasks such as data scrubbing, backfilling, recovery, and the difference in processing capabilities of heterogeneous servers in a datacenter. This has a significant impact on a broad range of applications that are characterized by massive working sets and real-time constraints. However, it is challenging and burdensome for human operators to hand-tune various control-knobs in a cloud-scale storage cluster for maintaining optimal performance under diverse workload conditions. Our study on an open-source object-based storage system, Ceph, shows that common load balancing strategies are ineffective unless they are adapted according to workload characteristics. Furthermore, positive effects of an applied strategy may not be immediately visible. To address these challenges, we developed a machine learning based system adaptation technique that enables a cloud storage system to manage itself through load balancing and data migration with the aim of delivering optimal performance in the face of diverse workload patterns and resource bottlenecks. In particular, we applied a stochastic policy gradient based reinforcement learning technique to track performance hotspots in the storage cluster, and take appropriate corrective actions to maximize future performance under a variety of complex scenarios. For this purpose, we leveraged system-level performance monitoring and commonly available control-knobs in object-based cloud storage systems. We implemented the developed techniques to build an Adaptive Resource Management (ARM) system for object based storage cluster, and evaluated its performance on NSF Cloud's Chameleon testbed. Experiments using Cloud Object Storage Benchmark (COSBench) show that, ARM improves the average read and write response time of Ceph storage cluster by upto 50% and 33% respectively, compared to the default case. It also outperforms a state-of-the-art dynamic load rebalancing technique in terms of read and write performance of Ceph storage by 43% and 36% respectively.
A Constraint Programming Approach to Simultaneous Task Allocation and Motion Scheduling for Industrial Dual-Arm Manipulation Tasks

Modern lightweight dual-arm robots bring the physical capabilities to quickly take over tasks at typical industrial workplaces designed for workers. Low setup times - including the instructing/specifying of new tasks - are crucial to stay competitive. We propose a constraint programming approach to simultaneous task allocation and motion scheduling for such industrial manipulation and assembly tasks. Our approach covers the robot as well as connected machines. The key concept are Ordered Visiting Constraints, a descriptive and extensible model to specify such tasks with their spatiotemporal requirements and combinatorial or ordering constraints. Our solver integrates such task models and robot motion models into constraint optimization problems and solves them efficiently using various heuristics to produce makespan-optimized robot programs. For large manipulation tasks with 200 objects, our solver implemented using Google's Operations Research tools requires less than a minute to compute usable plans. The proposed task model is robot-independent and can easily be deployed to other robotic platforms. This portability is validated through several simulation-based experiments.
A Power Management Approach to Reduce Energy Consumption for Edge Computing Servers

With the rapid development of edge computing and its applications, requests to edge servers is expected to grow, resulting in higher edge network energy consumption. This in essence would also result in higher operational costs for running edge applications. Furthermore, service providers try to manage their resources efficiently to provide appropriate quality of services to their customers while reducing service costs. To appropriately manage resources, it is necessary to apply useful models to measure energy consumption in the edge network. The linear relationship between energy consumption and CPU utilization is one powerful modeling method used to compute the energy consumption of edge network servers. The method calculates the power consumption of a server based on its CPU utilization during run-time. In this paper, we propose a linear power model for the EdgeCloudSim simulator to measure the energy consumption of edge network servers. Moreover, we introduce a simple dynamic power management model used to minimize power consumption in the edge network by switching the edge servers on and off based on provisioned application needs. The experimental and simulation results show a notable reduction in the total energy consumption when applying the proposed simple model on two different orchestration policies to manage the edge network servers.
3D articulated skeleton extraction using a single consumer-grade depth camera

Articulated skeleton extraction or learning has been extensively studied for 2D (e.g., images and video) and 3D (e.g., volume sequences, motion capture, and mesh sequences) data. Nevertheless, robustly and accurately learning 3D articulated skeletons from point set sequences captured by a single consumer-grade depth camera still remains challenging, since such data are often corrupted with substantial noise and outliers. Relatively few approaches have been proposed to tackle this problem. In this paper, we present a novel unsupervised framework to address this issue. Specifically, we first build one-to-one point correspondences among the point cloud frames in a sequence with our non-rigid point cloud registration algorithm. We then generate a skeleton involving a reasonable number of joints and bones with our skeletal structure extraction algorithm. We lastly present an iterative Linear Blend Skinning based algorithm for accurate joints learning. At the end, our method can learn a quality articulated skeleton from a single 3D point sequence possibly corrupted with noise and outliers. Through qualitative and quantitative evaluations on both publicly available data and in-house Kinect-captured data, we show that our unsupervised approach soundly outperforms state of the art techniques in terms of both quality (i.e., visual) and accuracy (i.e., Euclidean distance error metric). Moreover, the poses of our extracted skeletons are even comparable to those by KinectSDK, a well-known supervised pose estimation technique; for example, our method and KinectSDK achieves similar distance errors of 0.0497 and 0.0521.
Asymmetric Message Franking: Content Moderation for Metadata-Private End-to-End Encryption

Content moderation is crucial for stopping abusive and harassing messages in online platforms. Existing moderation mechanisms, such as message franking, require platform providers to be able to associate user identifiers to encrypted messages. These mechanisms fail in metadata-private messaging systems, such as Signal, where users can hide their identities from platform providers. The key technical challenge preventing moderation is achieving cryptographic accountability while preserving deniability. In this work, we resolve this tension with a new cryptographic primitive: asymmetric message franking (AMF) schemes. We define strong security notions for AMF schemes, including the first formal treatment of deniability in moderation settings. We then construct, analyze, and implement an AMF scheme that is fast enough to use for content moderation of metadata-private messaging.
Empty Cities: Image Inpainting for a Dynamic-Object-Invariant Space

In this paper we present an end-to-end deep learning framework to turn images that show dynamic content, such as vehicles or pedestrians, into realistic static frames. This objective encounters two main challenges: detecting all the dynamic objects, and inpainting the static occluded background with plausible imagery. The former challenge is addressed by the use of a convolutional network that learns a multiclass semantic segmentation of the image. The second problem is approached with a conditional generative adversarial model that, taking as input the original dynamic image and its dynamic/static binary mask, is capable of generating the final static image. These generated images can be used for applications such as augmented reality or vision-based robot localization purposes. To validate our approach, we show both qualitative and quantitative comparisons against other state-of-the-art inpainting methods by removing the dynamic objects and hallucinating the static structure behind them. Furthermore, to demonstrate the potential of our results, we carry out pilot experiments that show the benefits of our proposal for visual place recognition.
Flexibly Connectable Light Field System For Free View Exploration

Conventional captured-image-based virtual reality (VR) systems have three degrees of freedom (DoFs), where only rotational user motion is tracked for view rendering. This is a major cause of the reduced sense of reality. To increase user immersion levels akin to the real world, 3-DoF+ VR systems that support not only rotational but also translational view changes have been proposed. The light-field (LF) approach is suitable for this type of 3-DoF+ VR because it renders a view from a free view position by simply combining lights. Many previous systems have limited scalability because they assume a single LF for the acquisition and representation of light. One recent work connects multiple LFs at a physical intersection to increase the scalability. However, these fixed connection points limit the renderable view range and the layout of multiple LFs. Furthermore, in conventional single- or multiple-LF systems, the representable ranges of the view positions are highly dependent on the input field-of-view (FOV) of the camera used. In order to realize a wide view exploration range, the above-mentioned limitations must be overcome. This paper proposes a flexible connection scheme for multiple-LF systems taking advantage of the constant radiance of rays in LF theory. The proposed flexibly connectable LF system is able to widen the range of the renderable view position under reasonable conditions of the camera FOV. A light-field unit (LFU) which uses the proposed flexible connection is implemented. The LFU has a square-shaped structure and is thus easily stackable. This offers the advantage of dramatically expanding the scope of view explorations. The proposed LFU achieves 3-DoF+ VR with good quality as well as high scalability. Its cost-performance outcome is also better compared to those in previous works.
Towards the Design of Robotic Drivers for Full-Scale Self-Driving Racing Cars

Autonomous vehicles are undergoing a rapid development thanks to advances in perception, planning and control methods and technologies achieved in the last two decades. Moreover, the lowering costs of sensors and computing platforms are attracting industrial entities, empowering the integration and development of innovative solutions for civilian use. Still, the development of autonomous racing cars has been confined mainly to laboratory studies and small to middle scale vehicles. This paper tackles the development of a planning and control framework for an electric full scale autonomous racing car, which is an absolute novelty in the literature, upon which we report our preliminary experiments and perspectives on future work. Our system leverages real time Nonlinear Model Predictive Control to track a pre-planned racing line. We describe the whole control system architecture including the mapping and localization methods employed.
A formal model for event reconstruction in digital forensic investigation

Event reconstruction is an important phase in digital forensic investigation, which determines what happened during the incident. The digital investigator uses the findings of this phase to prepare reports for the court. Since the results must be reproducible and verifiable, it is necessary that the event reconstruction methods be rigorous and strict. In order to fulfill the legal requirements, this study proposes an event reconstruction framework which is based on the formal mathematical methods. In particular, it uses the temporal logic model checking that is an automatic verification technique. The idea is that the system under investigation is modeled as a transition system. Then the digital forensic property is specified using the modal μ-calculus. Finally, a model checking algorithm verifies whether the transition system meets the property. In order to demonstrate the proposed formal event reconstruction framework, an abstract model of the FAT file system is presented and some digital forensic properties are formulated. A big problem in model checking is the so-called state space explosion. This study addresses this problem and suggests some solutions to it. Finally, the proposed framework is applied to a case study to demonstrate how some hypotheses can be proved or refuted.
A Systematic Literature Review of Blockchain Architectures Applied to Public Services

The growth in use of Blockchain technology has enabled the development of applications in various industries. In particular, there has been an interest about using it in government to manage electronic public services, such as configuring and improving electronic government (e-government). However, the in-depth study of practical Blockchain solutions in the government sector is still under-researched despite the growing academic interest in this area. Therefore, this article presents a systematic literature review based on the current technology relating to Blockchain architectures applied by governments to public services.
Semi-Supervised Deep Coupled Ensemble Learning With Classification Landmark Exploration

Using an ensemble of neural networks with consistency regularization is effective for improving performance and stability of deep learning, compared to the case of a single network. In this paper, we present a semi-supervised Deep Coupled Ensemble (DCE) model, which contributes to ensemble learning and classification landmark exploration for better locating the final decision boundaries in the learnt latent space. First, multiple complementary consistency regularizations are integrated into our DCE model to enable the ensemble members to learn from each other and themselves, such that training experience from different sources can be shared and utilized during training. Second, in view of the possibility of producing incorrect predictions on a number of difficult instances, we adopt class-wise mean feature matching to explore important unlabeled instances as classification landmarks, on which the model predictions are more reliable. Minimizing the weighted conditional entropy on unlabeled data is able to force the final decision boundaries to move away from important training data points, which facilitates semi-supervised learning. Ensemble members could eventually have similar performance due to consistency regularization, and thus only one of these members is needed during the test stage, such that the efficiency of our model is the same as the non-ensemble case. Extensive experimental results demonstrate the superiority of our proposed DCE model over existing state-of-the-art semi-supervised learning methods.
NeXUS: Practical and Secure Access Control on Untrusted Storage Platforms using Client-Side SGX

With the rising popularity of file-sharing services such as Google Drive and Dropbox in the workflows of individuals and corporations alike, the protection of client-outsourced data from unauthorized access or tampering remains a major security concern. Existing cryptographic solutions to this problem typically require server-side support, involve non-trivial key management on the part of users, and suffer from severe re-encryption penalties upon access revocations. This combination of performance overheads and management burdens makes this class of solutions undesirable in situations where performant, platform-agnostic, dynamic sharing of user content is required. We present NEXUS, a stackable filesystem that leverages trusted hardware to provide confidentiality and integrity for user files stored on untrusted platforms. NEXUS is explicitly designed to balance security, portability, and performance: it supports dynamic sharing of protected volumes on any platform exposing a file access API without requiring server-side support, enables the use of fine-grained access control policies to allow for selective sharing, and avoids the key revocation and file re-encryption overheads associated with other cryptographic approaches to access control. This combination of features is made possible by the use of a client-side Intel SGX enclave that is used to protect and share NEXUS volumes, ensuring that cryptographic keys never leave enclave memory and obviating the need to reencrypt files upon revocation of access rights. We implemented a NEXUS prototype that runs on top of the AFS filesystem and show that it incurs ×2 overhead for a variety of common file and database operations.
Accelerated multiple alarm flood sequence alignment for abnormality pattern mining

Alarm floods can interfere with operators and may therefore cause or aggravate industrial accidents. A novel algorithm is proposed for pattern mining in multiple alarm floods. Unlike traditional methods which either cannot deal with multiple sequences with time stamps or suffer from high computational cost, the computational complexity of this proposed algorithm is reduced significantly by introducing a generalized pairwise sequence alignment method and a progressive multiple sequence alignment approach. Two types of alignment refinement methods are developed to improve the alignment accuracy. The effectiveness of the proposed algorithm is tested using a dataset from a real chemical plant.
Pattern-Driven Navigation in 2D Multiscale Visualizations with Scalable Insets

We present Scalable Insets, a technique for interactively exploring and navigating large numbers of annotated patterns in multiscale visualizations such as gigapixel images, matrices, or maps. Exploration of many but sparsely-distributed patterns in multiscale visualizations is challenging as visual representations change across zoom levels, context and navigational cues get lost upon zooming, and navigation is time consuming. Our technique visualizes annotated patterns too small to be identifiable at certain zoom levels using insets, i.e., magnified thumbnail views of the annotated patterns. Insets support users in searching, comparing, and contextualizing patterns while reducing the amount of navigation needed. They are dynamically placed either within the viewport or along the boundary of the viewport to offer a compromise between locality and context preservation. Annotated patterns are interactively clustered by location and type. They are visually represented as an aggregated inset to provide scalable exploration within a single viewport. In a controlled user study with 18 participants, we found that Scalable Insets can speed up visual search and improve the accuracy of pattern comparison at the cost of slower frequency estimation compared to a baseline technique. A second study with 6 experts in the field of genomics showed that Scalable Insets is easy to learn and provides first insights into how Scalable Insets can be applied in an open-ended data exploration scenario.
Deterrent effects of punishment and training on insider security threats: a field experiment on phishing attacks

There is no doubt that organisational security threats are currently surging, internally and externally. In an effort to prevent internal threats – employee violations of information security policy (ISP) – security training programmes and sanction policies are implemented to a large extent in organisations. However, few studies have verified their practical effectiveness and the impact of individual characteristics within organisations. This study conducted a field experiment to examine the actual deterrent effect of those measures against phishing attacks. By fabricating fake phishing schemes through a simulation programme, the specific deterrent effect of punishment, the general deterrent effect of education, and employees' organisational position were tested on their ISP compliance behaviour. Findings confirmed that punishment effectively prevented the punished from falling for phishing again and that the trained group fell for phishing much less often than the untrained group. In addition, the higher one's organisational position, the more likely the person fell for phishing. Regardless of the treatment (i.e. training or punishment), this position effect stood out. The results of this study offer researchers and practitioners insightful information about effective deterrence measures and policies for organisational information security.
Adaptive Consistency Propagation Method for Graph Clustering

Graph clustering plays an important role in data mining. Based on an input data graph, data points are partitioned into clusters. However, most existing methods keep the data graph fixed during the clustering procedure, so they are limited to exploit the implied data manifold and highly dependent on the initial graph construction. Inspired by the recent development on manifold learning, this paper proposes an Adaptive Consistency Propagation (ACP) method for graph clustering. In order to utilize the features captured from different perspectives, we further put forward the Multi-view version of the ACP model (MACP). The main contributions are threefold: (1) the manifold structure of input data is sufficiently exploited by propagating the topological connectivities between data points from near to far; (2) the optimal graph for clustering is learned by taking graph learning as a part of the optimization procedure; and (3) the negotiation among the heterogeneous features is captured by the multi-view clustering model. Extensive experiments on real-world datasets validate the effectiveness of the proposed methods on both single-and multi-view clustering, and show their superior performance over the state-of-the-arts.
A Hybrid Transforms-Based Robust Video Zero-Watermarking Algorithm for Resisting High Efficiency Video Coding Compression

With the rampancy of pirated videos, video watermarking for copyright protection has become a widely researched topic.In this paper, zero-watermarking is applied to videos for the first time to resist high efficiency video coding compression, which can improve the robustness of the watermarking algorithm and ensure the videos' quality.A robust video zero-watermarking algorithm based on the discrete wavelet transform, the all phase biorthogonal transform, and singular value decomposition is proposed.Utilizing the properties of hybrid transforms, robust features can be extracted from videos, and robust zero-watermarks can be constructed.Experimental results demonstrate that the proposed algorithm has strong robustness to high efficiency video coding compression attacks with different quantization parameters.In addition, the algorithm can also resist common image processing attacks, geometric attacks, frame-based attacks, and hybrid attacks.Compared with existing video watermarking algorithms, the proposed algorithm can more accurately and completely reconstruct watermark images.