Reviewerly/exHarmony huggingface dataset:
Preventing Kernel Hacks with HAKCs

Commodity operating system kernels remain monolithic for practical and historical reasons.All kernel code shares a single address space, executes with elevated processor privileges, and has largely unhindered access to all data, including data irrelevant to the completion of a specific task.Applying the principle of least privilege, which limits available resources only to those needed to perform a particular task, to compartmentalize the kernel would realize major security gains, similar to microkernels yet without the major redesign effort.Here, we introduce a compartmentalization design, called a Hardware-Assisted Kernel Compartmentalization (HAKC), that approximates least privilege separation, while minimizing both developer effort and performance overhead.HAKC divides code and data into separate partitions, and specifies an access policy for each partition.Data is owned by a single partition, and a partition's access-control policy is enforced at runtime, preventing unauthorized data access.When a partition needs to transfer control flow to outside itself, data ownership is transferred to the target, and transferred back upon return.The HAKC design allows for isolating code and data from the rest of the kernel, without utilizing any additional Trusted Computing Base while compartmentalized code is executing.Instead, HAKC relies on hardware for enforcement.Loadable kernel modules (LKMs), which dynamically load kernel code and data providing specialized functionality, are the single largest part of the Linux source base.Unfortunately, their collective size and complexity makes LKMs the cause of the majority of CVEs issued for the Linux kernel.The combination of a large attack surface in kernel modules, and the monolithic design of the Linux kernel, make LKMs ideal candidates for compartmentalization.To demonstrate the effectiveness of our approach, we implement HAKC in Linux v5.10 using extensions to the Arm v8.5-A ISA, and compartmentalize the ipv6.koLKM, which consists of over 55k LOC.The average overhead measured in Apachebench tests was just 1.6%-24%.Additionally, we compartmentalize the nf_tables.kopacket filtering LKM, and measure the combined impact of using both LKMs.We find a reasonable linear growth in overhead when both compartmentalized LKMs are used.Finally, we measure no significant difference in performance when using the compartmentalized ipv6.koLKM over the unmodified LKM during real-world web browsing experiments on the Alexa Top 50 websites.
Towards the optimality of QoS-aware web service composition with uncertainty

Quality of service (QoS)-aware web service composition (QWSC) has recently become one of the most challenging research issues. Although much work has been investigated, they mainly focus on certain QoS of web services, while QoS with uncertainty exposes the most important characteristic in real and highly dynamic environment. In this paper, with the consideration of uncertain service QoS and user's preferences, we model the issue of uncertain QoS-aware WSC via interval number and translate it into a multi-objective optimisation problem with global QoS constraints of user's preferences. The encoded optimisation problem is solved by an non-deterministic multi-objective evolutionary algorithm, which exploits new genetic encoding schema, the strategy of crossover and uncertain interval Pareto comparison. To validate the feasibility, large-scale experiments have been conducted on simulated datasets. The results demonstrate that our proposed approach can effectively and efficiently find optimum composite service solutions set with satisfactory convergence.
Massively Parallel Approximation Algorithms for Edit Distance and Longest Common Subsequence

String similarity measures are among the most fundamental problems in computer science.The notable examples are edit distance (ED) and longest common subsequence (LCS).These problems find their applications in various contexts such as computational biology, text processing, compiler optimization, data analysis, image analysis, etc.In this work, we revisit edit distance and longest common subsequence in the parallel settings.We present massively parallel algorithms for both problems that are optimal in the following senses:• The approximation factor of our algorithms is 1 + .• The round complexity of our algorithms is constant.
Augmenting smart home network security using blockchain technology

The idea of smart home existed from 1970's onwards but has come into the knowledge of researchers and data scientist due to the development in the domain of internet of things (IoT) (Kopetz, 2011), but it still suffers from privacy and security vulnerabilities. Conventional security policies or approaches are not applicable to IoT, mainly due to its decentralised topology and the resource constraints of the majority of its devices (Dorri et al., 2017a). This Paper presents an innovative, novel and decentralised approach that can be used to augment the existing security architecture of a smart home network. Our scheme guarantees both trustworthiness and user privacy preservations.
Improved finite-key security analysis of quantum key distribution against Trojan-horse attacks

Abstract Most security proofs of quantum key distribution (QKD) disregard the effect of information leakage from the users’ devices, and, thus, do not protect against Trojan-horse attacks (THAs). In a THA, the eavesdropper injects strong light into the QKD apparatuses, and then analyzes the back-reflected light to learn information about their internal setting choices. Only a few recent works consider this security threat, but predict a rather poor performance of QKD unless the devices are strongly isolated from the channel. Here, we derive finite-key security bounds for decoy-state-based QKD schemes in the presence of THAs, which significantly outperform previous analyses. Our results constitute an important step forward to closing the existing gap between theory and practice in QKD.
Telecom-Band Quantum Interference of Frequency-Converted Photons from Remote Detuned NV Centers

Photons from two far detuned solid-state emitters are converted to the telecom L-band and made indistinguishable in fully independently operated quantum nodes, progressing towards a metropolitan scale quantum network.
Semi-supervised and personalized federated activity recognition based on active learning and label propagation

Abstract One of the major open problems in sensor-based Human Activity Recognition (HAR) is the scarcity of labeled data. Among the many solutions to address this challenge, semi-supervised learning approaches represent a promising direction. However, their centralized architecture incurs in the scalability and privacy problems that arise when the process involves a large number of users. Federated learning (FL) is a promising paradigm to address these problems. However, the FL methods that have been proposed for HAR assume that the participating users can always obtain labels to train their local models (i.e., they assume a fully supervised setting). In this work, we propose FedAR: a novel hybrid method for HAR that combines semi-supervised and federated learning to take advantage of the strengths of both approaches. FedAR combines active learning and label propagation to semi-automatically annotate the local streams of unlabeled sensor data, and it relies on FL to build a global activity model in a scalable and privacy-aware fashion. FedAR also includes a transfer learning strategy to fine-tune the global model on each user. We evaluated our method on two public datasets, showing that FedAR reaches recognition rates and personalization capabilities similar to state-of-the-art FL supervised approaches. As a major advantage, FedAR only requires a very limited number of annotated data to populate a pre-trained model and a small number of active learning questions that quickly decrease while using the system, leading to an effective and scalable solution for the data scarcity problem of HAR.
An Autoscaling Platform Supporting Graph Data Modelling Big Data Analytics

Big Data has proved to be vast and complex, without being efficiently manageable through traditional architectures, whereas data analysis is considered crucial for both technical and non-technical stakeholders. Current analytics platforms are siloed for specific domains, whereas the requirements to enhance their use and lower their technicalities are continuously increasing. This paper describes a domain-agnostic single access autoscaling Big Data analytics platform, namely Diastema, as a collection of efficient and scalable components, offering user-friendly analytics through graph data modelling, supporting technical and non-technical stakeholders. Diastema’s applicability is evaluated in healthcare through a predicting classifier for a COVID19 dataset, considering real-world constraints.
FedDUAP: Federated Learning with Dynamic Update and Adaptive Pruning Using Shared Data on the Server

Despite achieving remarkable performance, Federated Learning (FL) suffers from two critical challenges, i.e., limited computational resources and low training efficiency. In this paper, we propose a novel FL framework, i.e., FedDUAP, with two original contributions, to exploit the insensitive data on the server and the decentralized data in edge devices to further improve the training efficiency. First, a dynamic server update algorithm is designed to exploit the insensitive data on the server, in order to dynamically determine the optimal steps of the server update for improving the convergence and accuracy of the global model. Second, a layer-adaptive model pruning method is developed to perform unique pruning operations adapted to the different dimensions and importance of multiple layers, to achieve a good balance between efficiency and effectiveness. By integrating the two original techniques together, our proposed FL model, FedDUAP, significantly outperforms baseline approaches in terms of accuracy (up to 4.8% higher), efficiency (up to 2.8 times faster), and computational cost (up to 61.9% smaller).
Machine learning techniques for forecasting agricultural prices: A case of brinjal in Odisha, India

Background Price forecasting of perishable crop like vegetables has importance implications to the farmers, traders as well as consumers. Timely and accurate forecast of the price helps the farmers switch between the alternative nearby markets to sale their produce and getting good prices. The farmers can use the information to make choices around the timing of marketing. For forecasting price of agricultural commodities, several statistical models have been applied in past but those models have their own limitations in terms of assumptions. Methods In recent times, Machine Learning (ML) techniques have been much successful in modeling time series data. Though, numerous empirical studies have shown that ML approaches outperform time series models in forecasting time series, but their application in forecasting vegetables prices in India is scared. In the present investigation, an attempt has been made to explore efficient ML algorithms e.g. Generalized Neural Network (GRNN), Support Vector Regression (SVR), Random Forest (RF) and Gradient Boosting Machine (GBM) for forecasting wholesale price of Brinjal in seventeen major markets of Odisha, India. Results An empirical comparison of the predictive accuracies of different models with that of the usual stochastic model i.e. Autoregressive integrated moving average (ARIMA) model is carried out and it is observed that ML techniques particularly GRNN performs better in most of the cases. The superiority of the models is established by means of Model Confidence Set (MCS), and other accuracy measures such as Mean Error (ME), Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Prediction Error (MAPE). To this end, Diebold-Mariano test is performed to test for the significant differences in predictive accuracy of different models. Conclusions Among the machine learning techniques, GRNN performs better in all the seventeen markets as compared to other techniques. RF performs at par with GRNN in four markets. The accuracies of other techniques such as SVR, GBM and ARIMA are not up to the mark.
Self-Distilled StyleGAN: Towards Generation from Internet Photos

StyleGAN is known to produce high-fidelity images, while also offering unprecedented semantic editing. However, these fascinating abilities have been demonstrated only on a limited set of datasets, which are usually structurally aligned and well curated. In this paper, we show how StyleGAN can be adapted to work on raw uncurated images collected from the Internet. Such image collections impose two main challenges to StyleGAN: they contain many outlier images, and are characterized by a multi-modal distribution. Training StyleGAN on such raw image collections results in degraded image synthesis quality. To meet these challenges, we proposed a StyleGAN-based self-distillation approach, which consists of two main components: (i) A generative-based self-filtering of the dataset to eliminate outlier images, in order to generate an adequate training set, and (ii) Perceptual clustering of the generated images to detect the inherent data modalities, which are then employed to improve StyleGAN’s “truncation trick” in the image synthesis process. The presented technique enables the generation of high-quality images, while minimizing the loss in diversity of the data. Through qualitative and quantitative evaluation, we demonstrate the power of our approach to new challenging and diverse domains collected from the Internet. New datasets and pre-trained models are provided in our project website https://self-distilled-stylegan.github.io/.
Knowledge Graphs: A Practical Review of the Research Landscape

Knowledge graphs (KGs) have rapidly emerged as an important area in AI over the last ten years. Building on a storied tradition of graphs in the AI community, a KG may be simply defined as a directed, labeled, multi-relational graph with some form of semantics. In part, this has been fueled by increased publication of structured datasets on the Web, and well-publicized successes of large-scale projects such as the Google Knowledge Graph and the Amazon Product Graph. However, another factor that is less discussed, but which has been equally instrumental in the success of KGs, is the cross-disciplinary nature of academic KG research. Arguably, because of the diversity of this research, a synthesis of how different KG research strands all tie together could serve a useful role in enabling more ‘moonshot’ research and large-scale collaborations. This review of the KG research landscape attempts to provide such a synthesis by first showing what the major strands of research are, and how those strands map to different communities, such as Natural Language Processing, Databases and Semantic Web. A unified framework is suggested in which to view the distinct, but overlapping, foci of KG research within these communities.
Hold On and Swipe: A Touch-Movement Based Continuous Authentication Schema based on Machine Learning

In recent years, the amount of secure information being stored on mobile devices has grown exponentially. However, current security schemas for mobile devices such as physiological biometrics and passwords are not secure enough to protect this information. Behavioral biometrics have been heavily researched as a possible solution to this security deficiency for mobile devices. This study aims to contribute to this innovative research by evaluating the performance of a multi-modal behavioral biometric based user authentication scheme using touch dynamics and phone movement. This study uses a fusion of two popular publicly available datasets - the Hand Movement Orientation and Grasp (HMOG) dataset and the BioIdent dataset. This study evaluates our model&#x0027;s performance using three common machine learning algorithms; Random Forest, Support Vector Machine, and K-Nearest Neighbor reaching accuracy rates as high as 82&#x0025;, with each algorithm performing respectively for all success metrics reported.
Iterative rule-guided reasoning over sparse knowledge graphs with deep reinforcement learning

In recent years, reasoning over knowledge graphs (KGs) has been widely adapted to empower retrieval systems, recommender systems, and question answering systems, generating a surge in research interest. Recently developed reasoning methods usually suffer from poor performance when applied to incomplete or sparse KGs, due to the lack of evidential paths that can reach target entities. To solve this problem, we propose a hybrid multi-hop reasoning model with reinforcement learning (RL) called SparKGR, which implements dynamic path completion and iterative rule guidance strategies to increase reasoning performance over sparse KGs. Firstly, the model dynamically completes the missing paths using rule guidance to augment the action space for the RL agent; this strategy effectively reduces the sparsity of KGs, thus increasing path search efficiency. Secondly, an iterative optimization of rule induction and fact inference is designed to incorporate global information from KGs to guide the RL agent exploration; this optimization iteratively improves overall training performance. We further evaluated the SparKGR model through different tasks on five real world datasets extracted from Freebase, Wikidata and NELL. The experimental results indicate that SparKGR outperforms state-of-the-art baseline models without losing interpretability.
Hierarchical Clustering with Contiguity Constraint in <i>R</i>

This article presents a new implementation of hierarchical clustering for the R language that allows one to apply spatial or temporal contiguity constraints during the clustering process. The need for contiguity constraint arises, for instance, when one wants to partition a map into different domains of similar physical conditions, identify discontinuities in time series, group regional administrative units with respect to their performance, and so on. To increase computation efficiency, we programmed the core functions in plain C. The result is a new R function, constr.hclust, which is distributed in package adespatial. The program implements the general agglomerative hierarchical clustering algorithm described by Lance and Williams (1966; 1967), with the particularity of allowing only clusters that are contiguous in geographic space or along time to fuse at any given step. Contiguity can be defined with respect to space or time. Information about spatial contiguity is provided by a connection network among sites, with edges describing the links between connected sites. Clustering with a temporal contiguity constraint is also known as chronological clustering. Information on temporal contiguity can be implicitly provided as the rank positions of observations in the time series. The implementation was mirrored on that found in the hierarchical clustering function hclust of the standard R package stats (R Core Team 2022). We transcribed that function from Fortran to C and added the functionality to apply constraints when running the function. The implementation is efficient. It is limited mainly by input/output access as massive amounts of memory are potentially needed to store copies of the dissimilarity matrix and update its elements when analyzing large problems. We provided R computer code for plotting results for numbers of clusters.
AlScN Piezoelectric MEMS Mirrors with Large Field of View for LiDAR Application

This paper presents AlScN piezoelectric two-axis MEMS mirrors with gimbal-less and gimbaled designs fabricated in a CMOS-compatible manner. Integrated piezoelectric sensors provided feedback signals of the actual mirror positions. The mirror with a diameter of 1.5 mm possessed adjustable optical tilt angles of up to 22.6° @ 30 V, with a high resonance frequency of about 8.2 kHz, while the 3 mm mirror reached 48.5° @ 41 V. The mirror with the gimbaled structure exhibited an excellent field of view and good mechanical decoupling. Additionally, a significant improvement in mirror scanning performance was observed in a vacuum (4 Pa), proving that the optical field of view was magnified by more than a factor of 10.
Image-Text Matching with Fine-Grained Relational Dependency and Bidirectional Attention-Based Generative Networks

Generally, most existing cross-modal retrieval methods only consider global or local semantic embeddings, lacking fine-grained dependencies between objects. At the same time, it is usually ignored that the mutual transformation between modalities also facilitates the embedding of modalities. Given these problems, we propose a method called BiKA (Bidirectional Knowledge-assisted embedding and Attention-based generation). The model uses a bidirectional graph convolutional neural network to establish dependencies between objects. In addition, it employs a bidirectional attention-based generative network to achieve the mutual transformation between modalities. Specifically, the knowledge graph is used for local matching to constrain the local expression of the modalities, in which the generative network is used for mutual transformation to constrain the global expression of the modalities. In addition, we also propose a new position relation embedding network to embed position relation information between objects. The experiments on two public datasets show that the performance of our method has been dramatically improved compared to many state-of-the-art models.
Causality-based CTR prediction using graph neural networks

As a prevalent problem in online advertising, CTR prediction has attracted plentiful attention from both academia and industry. Recent studies have been reported to establish CTR prediction models in the graph neural networks (GNNs) framework. However, most of GNNs-based models handle feature interactions in a complete graph, while ignoring causal relationships among features, which results in a huge drop in the performance on out-of-distribution data. This paper is dedicated to developing a causality-based CTR prediction model in the GNNs framework (Causal-GNN) integrating representations of feature graph, user graph and ad graph in the context of online advertising. In our model, a structured representation learning method (GraphFwFM) is designed to capture high-order representations on feature graph based on causal discovery among field features in gated graph neural networks (GGNNs), and GraphSAGE is employed to obtain graph representations of users and ads. Experiments conducted on three public datasets demonstrate the superiority of Causal-GNN in AUC and Logloss and the effectiveness of GraphFwFM in capturing high-order representations on causal feature graph.
MyStyle

We introduce MyStyle, a personalized deep generative prior trained with a few shots of an individual. MyStyle allows to reconstruct, enhance and edit images of a specific person, such that the output is faithful to the person's key facial characteristics. Given a small reference set of portrait images of a person (~ 100), we tune the weights of a pretrained StyleGAN face generator to form a local, low-dimensional, personalized manifold in the latent space. We show that this manifold constitutes a personalized region that spans latent codes associated with diverse portrait images of the individual. Moreover, we demonstrate that we obtain a personalized generative prior, and propose a unified approach to apply it to various ill-posed image enhancement problems, such as inpainting and super-resolution, as well as semantic editing. Using the personalized generative prior we obtain outputs that exhibit high-fidelity to the input images and are also faithful to the key facial characteristics of the individual in the reference set. We demonstrate our method with fair-use images of numerous widely recognizable individuals for whom we have the prior knowledge for a qualitative evaluation of the expected outcome. We evaluate our approach against few-shots baselines and show that our personalized prior, quantitatively and qualitatively, outperforms state-of-the-art alternatives.