Reviewerly/exHarmony huggingface dataset:
Cornucopia: Temporal Safety for CHERI Heaps

Use-after-free violations of temporal memory safety continue to plague software systems, underpinning many high-impact exploits. The CHERI capability system shows great promise in achieving C and C++ language spatial memory safety, preventing out-of-bounds accesses. Enforcing language-level temporal safety on CHERI requires capability revocation, traditionally achieved either via table lookups (avoided for performance in the CHERI design) or by identifying capabilities in memory to revoke them (similar to a garbage-collector sweep). CHERIvoke, a prior feasibility study, suggested that CHERI's tagged capabilities could make this latter strategy viable, but modeled only architectural limits and did not consider the full implementation or evaluation of the approach.Cornucopia is a lightweight capability revocation system for CHERI that implements non-probabilistic C/C++ temporal memory safety for standard heap allocations. It extends the CheriBSD virtual-memory subsystem to track capability flow through memory and provides a concurrent kernel-resident revocation service that is amenable to multi-processor and hardware acceleration. We demonstrate an average overhead of less than 2% and a worst-case of 8.9% for concurrent revocation on compatible SPEC CPU2006 benchmarks on a multi-core CHERI CPU on FPGA, and we validate Cornucopia against the Juliet test suite's corpus of temporally unsafe programs. We test its compatibility with a large corpus of C programs by using a revoking allocator as the system allocator while booting multi-user CheriBSD. Cornucopia is a viable strategy for always-on temporal heap memory safety, suitable for production environments.
Entity-Aware Dependency-Based Deep Graph Attention Network for Comparative Preference Classification

This paper studies the task of comparative preference classification (CPC).Given two entities in a sentence, our goal is to classify whether the first (or the second) entity is preferred over the other or no comparison is expressed at all between the two entities.Existing works either do not learn entity-aware representations well and fail to deal with sentences involving multiple entity pairs or use sequential modeling approaches that are unable to capture long-range dependencies between the entities.Some also use traditional machine learning approaches that do not generalize well.This paper proposes a novel Entityaware Dependency-based Deep Graph Attention Network (ED-GAT) that employs a multihop graph attention over a dependency graph sentence representation to leverage both the semantic information from word embeddings and the syntactic information from the dependency graph to solve the problem.Empirical evaluation shows that the proposed model achieves the state-of-the-art performance in comparative preference classification.
A Spatial RNN Codec for End-to-End Image Compression

Recently, deep learning has been explored as a promising direction for image compression. Removing the spatial redundancy of the image is crucial for image compression and most learning based methods focus on removing the redundancy between adjacent pixels. Intuitively, to explore larger pixel range beyond adjacent pixel is beneficial for removing the redundancy. In this paper, we propose a fast yet effective method for end-to-end image compression by incorporating a novel spatial recurrent neural network. Block based LSTM is utilized to remove the redundant information between adjacent pixels and blocks. Besides, the proposed method is a potential efficient system that parallel computation on individual blocks is possible. Experimental results demonstrate that the proposed model outperforms state-of-the-art traditional image compression standards and learning based image compression models in terms of both PSNR and MS-SSIM metrics. It provides a 26.73% bits-reduction than High Efficiency Video Coding (HEVC), which is the current official state-of-the-art video codec.
Threats, Challenges, And Opportunities for Open Universities and Massive Online Open Courses in The Digital Revolution

In a world of sharp transformation driven by digital technologies, education is facing a paradigm shift that exposes vulnerabilities and threats of Open Education models in the face of the trend of commodification. This article seeks to offer useful insights into the threats, challenges, and opportunities for open universities and massive online open courses in the digital revolution. The methodology fol-lowed a systematic content review of literature. With the advent of the internet, we have verified a profound transformation in the way we communicate and produce within the academics. The digital revolution favored the freedom of access to information. The digital revolution poses challenges but also opens up unprecedented opportunities for democratization and accessibility. The transformation process of open universities has to maintain the referential of the profound incorporation of pedagogical and technological innovation based on research and seek new strategies of organization and definition of quality, to guarantee its relevance and leadership in the pursuit of the massification of higher education.
How to fairly incentivise digital contact tracing

Digital apps using Bluetooth to log proximity events (henceforth, digital contact tracing) are increasingly supported by technologists and governments. By and large, the public debate on this matter focuses on privacy, with experts from both law and technology offering very concrete proposals and participating to a lively debate. Far less attention is paid to effective incentives and their fairness. This paper aims to fill this gap by offering a practical, workable solution for a promising incentive, justified by the ethical principles of non-maleficence, beneficence, autonomy and justice. This incentive is a free phone optimised for running such app.
PCGCN: Partition-Centric Processing for Accelerating Graph Convolutional Network

Inspired by the successes of convolutional neural networks (CNN) in computer vision, the convolutional operation has been moved beyond low-dimension grids (e.g., images) to high-dimensional graph-structured data (e.g., web graphs, social networks), leading to graph convolutional network (GCN). And GCN has been gaining popularity due to its success in real-world applications such as recommendation, natural language processing, etc. Because neural network and graph propagation have high computation complexity, GPUs have been introduced to both neural network training and graph processing. However, it is notoriously difficult to perform efficient GCN computing on data parallel hardware like GPU due to the sparsity and irregularity in graphs. In this paper, we present PCGCN, a novel and general method to accelerate GCN computing by taking advantage of the locality in graphs. We experimentally demonstrate that real-world graphs usually have the clustering property that can be used to enhance the data locality in GCN computing. Then, PCGCN proposes to partition the whole graph into chunks according to locality and process subgraphs with a dual-mode computing strategy which includes a selective and a full processing methods for sparse and dense subgraphs, respectively. Compared to existing state-of-the-art implementations of GCN on real-world and synthetic datasets, our implementation on top of TensorFlow achieves up to 8.8× speedup over the fastest one of the baselines.
Semi-Supervised Natural Face De-Occlusion

Occlusions are often present in face images in the wild, e.g., under video surveillance and forensic scenarios. Existing face de-occlusion methods are limited as they require the knowledge of an occlusion mask. To overcome this limitation, we propose in this paper a new generative adversarial network (named OA-GAN) for natural face de-occlusion without an occlusion mask, enabled by learning in a semi-supervised fashion using (i) paired images with known masks of artificial occlusions and (ii) natural images without occlusion masks. The generator of our approach first predicts an occlusion mask, which is used for filtering the feature maps of the input image as a semantic cue for de-occlusion. The filtered feature maps are then used for face completion to recover a non-occluded face image. The initial occlusion mask prediction might not be accurate enough, but it gradually converges to the accurate one because of the adversarial loss we use to perceive which regions in a face image need to be recovered. The discriminator of our approach consists of an adversarial loss, distinguishing the recovered face images from natural face images, and an attribute preserving loss, ensuring that the face image after de-occlusion can retain the attributes of the input face image. Experimental evaluations on the widely used CelebA dataset and a dataset with natural occlusions we collected show that the proposed approach can outperform the state of the art methods in natural face de-occlusion.
RANCANG BANGUN GAME EDUKASI TEMPAT BERSEJARAH DI INDONESIA

In today's society and especially among children it seems as if they do not care about the historical heritage left by the ancestors, for example is a historical building.In fact, many are not aware of the existence of these buildings.Ignorance of history will lead to the assumption that historic buildings need not be preserved, and only assume that the historic building is just an old building that is not suitable for use.Therefore, we need a media that can increase children's interest and knowledge about historic sites, based on this the authors conducted a study with the title "Educational Building Design of Historic Sites in Indonesia".This game was developed with the Game Development Life Cycle Method.Then to find out the feasibility of this game was tested using ISO 9126 with four aspects of testing, namely Functionality, Usability, Efficiency and Portability.Based on the evaluation of observations made to students in grades 5 and 6 MIN 7 Bandar Lampung, there was an increase in students' ability to answer questions about historic sites in Indonesia, especially those on the islands of Sumatra and Java contained in the game by 50% of the initial conditions where students haven't played an educational game on historic sites in Indonesia.
A Survey of Secure Internet of Things in Relation to Blockchain

Distributed ledgers and blockchain technologies can improve system security and trustworthiness by providing immutable replicated histories of data. Blockchain is a linked list of blocks containing ...
An Efficient Load Balancing Method by Using Machine Learning-Based VM Distribution and Dynamic Resource Mapping

In cloud computing, balancing the load among VMs and resources is a major research area, which still needs attention. The primary aim of this research is to expand an effective cloud load balancing approach, enhance the reaction time, lessen the ready time, premiere utilization of sources as well lessen the activity rejection time. The proposed MLBL method is primarily based on the SVM and K suggest clustering method. In the proposed MLBL technique SVM classification method used to create activity businesses based totally at the size. Later K method clustering technique is used to create the institution of Virtual machines based totally on their usage of CPU and number one memory (RAM). We are providing an MLBL method based totally on system learning-based VM distribution and dynamic aid mapping. In cloud computing, VMs are scheduled to hosts as in keeping with their utilization (a host which has better availability of memory) without thinking about common utilizations. The proposed technique divides the assets into various organizations as well as VMs and then follow dynamic aid mapping. The proposed MLBL technique creates VMs clusters to execute comparable task agencies that enhance the QoS and ideal utilization of sources in addition to lessen the process rejection time.
Leakage-Adjusted Simulatability: Can Models Generate Non-Trivial Explanations of Their Behavior in Natural Language?

Data collection for natural language (NL) understanding tasks has increasingly included human explanations alongside data points, allowing past works to introduce models that both perform a task and generate NL explanations for their outputs.Yet to date, model-generated explanations have been evaluated on the basis of surface-level similarities to human explanations, both through automatic metrics like BLEU and human evaluations.We argue that these evaluations are insufficient, since they fail to indicate whether explanations support actual model behavior (faithfulness), rather than simply match what a human would say (plausibility).In this work, we address the problem of evaluating explanations from the model simulatability perspective.Our contributions are as follows: (1) We introduce a leakage-adjusted simulatability (LAS) metric for evaluating NL explanations, which measures how well explanations help an observer predict a model's output, while controlling for how explanations can directly leak the output.We use a model as a proxy for a human observer, and validate this choice with two human subject experiments.(2) Using the CoS-E and e-SNLI datasets, we evaluate two existing generative graphical models and two new approaches; one rationalizing method we introduce achieves roughly human-level LAS scores.(3) Lastly, we frame explanation generation as a multi-agent game and optimize explanations for simulatability while penalizing label leakage, which can improve LAS scores. 1
VAID: An Aerial Image Dataset for Vehicle Detection and Classification

The availability of commercial UAVs and low-cost imaging devices has made the airborne imagery popular and widely available. The aerial images are now extensively used for many applications, especially in the area of intelligent transportation systems. In this work, we present a new aerial image dataset, VAID (Vehicle Aerial Imaging from Drone), for the development and evaluation of vehicle detection algorithms. It contains about 6000 images captured under different traffic conditions, and annotated with 7 common vehicle categories for network training and testing. We compare the of vehicle detection results using the current state-of-the-art network architectures and various aerial image datasets. The experiments have demonstrated that training the networks using our VAID dataset can provide the best vehicle detection results. Our aerial image dataset is made available publicly at http://vision.ee.ccu.edu.tw/aerialimage/ and the code is available at https://github.com/KaiChun-RVL/VAID_dataset.
A Critical Evaluation of Privacy and Security Threats in Federated Learning

With the advent of smart devices, smartphones, and smart everything, the Internet of Things (IoT) has emerged with an incredible impact on the industries and human life. The IoT consists of millions of clients that exchange massive amounts of critical data, which results in high privacy risks when processed by a centralized cloud server. Motivated by this privacy concern, a new machine learning paradigm has emerged, namely Federated Learning (FL). Specifically, FL allows for each client to train a learning model locally and performs global model aggregation at the centralized cloud server in order to avoid the direct data leakage from clients. However, despite this efficient distributed training technique, an individual’s private information can still be compromised. To this end, in this paper, we investigate the privacy and security threats that can harm the whole execution process of FL. Additionally, we provide practical solutions to overcome those attacks and protect the individual’s privacy. We also present experimental results in order to highlight the discussed issues and possible solutions. We expect that this work will open exciting perspectives for future research in FL.
Adaptive ranking based ensemble learning of Gaussian process regression models for quality-related variable prediction in process industries

The proper monitoring of quality-related but hard-to-measure variables is currently one of the bottlenecks limiting the safe and efficient operations of industrial processes. This paper proposes a novel ensemble learning algorithm by coordinating global and local Gaussian process regression (GPR) models, and this algorithm is able to capture global and local process behaviours for accurate prediction and timely process monitoring. To further address the deterioration in predictions when using the off-line training and online testing strategy, this paper proposes an adaptive ranking strategy to perform ensemble learning for the sub-GPR models. In this adaptive strategy, we use the moving-window technique to rank and select several of the best sub-model predictions and then average them together to make the final predictions. Last but not least, the least absolute shrinkage and selection operator (Lasso) works together with factor analysis (FA) in a two-step variable selection method to remove under-correlated model input variables in the first stage and to compress over-correlated model input variables in the second stage. The proposed prediction model is validated in two real wastewater treatment plants (WWTPs) with stationary and nonstationary behaviours. The results show that the proposed methodology achieves better performance than other standard methods in the context of their predictions of quality-related variables.
Possible Pitfalls in the Analysis of Minerals and Loose Materials by Portable XRF, and How to Overcome Them

Portable X-ray fluorescence spectroscopy is now widely used in almost any field of geoscience. Handheld XRF analysers are easy to use, and results are available in almost real time anywhere. However, the results do not always match laboratory analyses, and this may deter users. Rather than analytical issues, the bias often results from sample preparation differences. Instrument setup and analysis conditions need to be fully understood to avoid reporting erroneous results. The technique’s limitations must be kept in mind. We describe a number of issues and potential pitfalls observed from our experience and described in the literature. This includes the analytical mode and parameters; protective films; sample geometry and density, especially for light elements; analytical interferences between elements; physical effects of the matrix and sample condition, and more. Nevertheless, portable X-ray fluorescence spectroscopy (pXRF) results gathered with sufficient care by experienced users are both precise and reliable, if not fully accurate, and they can constitute robust data sets. Rather than being a substitute for laboratory analyses, pXRF measurements are a valuable complement to those. pXRF improves the quality and relevance of laboratory data sets.
Class-agnostic Few-shot Object Counting

Object counting which aims to calculate the number of total instances of the given class is a classic but crucial task that can be applied to many applications. Most of the prior works only focus on counting certain classes of objects such as people, cars, animals, etc. However, in recent years, there are lots of applications that need to get the count of the unseen class of objects such as a mechanical arm commanded to grab the novel object. In this paper, we present an effective object counting network, Class-agnostic Few-shot Object Counting Network (CFOCNet), that supports counting arbitrary classes of object unseen during training stage. Instead of counting a pre-defined class, our model is able to count instances based on input reference images and reduces the huge cost of data collection, training and parameter tuning for each new object class. Our model utilizes not only the similarity between query image and reference images but self attending the query image to learn the self-repeatedness. Using a two-stream Resnet that matches features in different scales, our network can automatically learn to aggregate different scales of the matching scores. We evaluate our method on the subset of the COCO dataset that contains 80 classes of objects and many diverse scenes. In the experiments, our network outperforms other methods including detection and some previous works by a large margin. To the best of our knowledge, we are the first that mainly focuses on few-shot object counting in the class-agnostic manner.
Augmented reality navigation for minimally invasive knee surgery using enhanced arthroscopy

During the minimally invasive knee surgery, surgeons insert surgical instruments and arthroscopy through small incisions, and implement treatment assisted by 2D arthroscopic images. However, this 2D arthroscopic navigation faces several problems. Firstly, the guidance information is displayed on a screen away from the surgical area, which makes hand/eye coordination difficult. Secondly, the small incision limits the surgeons to view the internal knee structures only from an arthroscopic camera. In addition, arthroscopic images commonly appear obscure visions. To solve these problems, we proposed a novel in-situ augmented reality navigation system with the enhanced arthroscopic information. Firstly, intraoperative anatomical locations were obtained by using arthroscopic images and arthroscopy calibration. Secondly, tissue properties-based model deformation method was proposed to update the 3D preoperative knee model with anatomical location information. Then, the updated model was further rendered with glasses-free real 3D display for achieving the global in-situ augmented reality view. In addition, virtual arthroscopic images were generated from the updated preoperative model to provide the anatomical information of the operation area. Experimental results demonstrated that virtual arthroscopic images could reflect the correct structure information with a mean error of 0.32 mm. Compared with 2D arthroscopic navigation, the proposed augmented reality navigation reduced the targeting errors by 2.10 mm and 2.70 mm for the experiments of knee phantom and in-vitro swine knee, respectively. Our navigation method is helpful for minimally invasive knee surgery since it can provide the global in-situ information and detail anatomical information.
The Impact of Artificial Intelligence on Consumers Identity and Human Skills

The development of artificial intelligence is one of the main paradigms of the contemporary society, which will radically change the existence of individuals and our society and it will have important effects on the economy.The use of artificial intelligence in the daily work of individuals and in the relationship between companies and consumers has a great number of advantages such as the increased efficiency, a high degree of fascination in interaction, but in the same time there are several fears related to its development in the future.Due to its great data storage capacity about the behavior of individuals and the processing speed of this data, there is a risk that the forms of artificial intelligence will become smarter than humans and thus intervene in the decisions made by them.Through the constant use of artificial intelligence, there is a high risk of manipulation of consumers as well as a high degree of dependence on intelligent technologies.This close relationship between the user and artificial intelligence can reduce an individual's cognitive abilities and can affect their thinking, personality and relationships with its social circle.This paper presents a mediation model between the efficiency and fascination with artificial intelligence and the consumers' perception of preserving their self-identity and human skills, having as mediator the influence and model of the social circle.The research results show that a higher degree of efficiency and fascination, as well as a positive influence from the social circle decrease the consumers' perception of reduction of human skills in relation to artificial intelligence.Moreover, the social circle mediates the relationship between efficiency and fascination produced by artificial intelligence and the perception of preserving human abilities.