Reviewerly/exHarmony huggingface dataset:
MagBox: Keep the risk functions running safely in a magic box

Address space layout randomization (ASLR) has been widely deployed in operating systems (OS) to hide memory layout, which mitigates code reuse attacks (CRAs). Unfortunately, the memory probing techniques can still provide attackers with enough information to bypass ASLR. Although the control flow integrity (CFI) methods are not affected by code probing, they face the precision problem of control flow graphs (CFG). To make matters worse, most methods rely on the source code of the targets to be protected, which leads to their restrictions on the protection of the objects without source code. To solve these problems, MagBox is proposed in this paper. It identifies the risk functions that can provide gadgets for CRAs by detecting and analyzing attackers’ code probing activities. If the function is probed, it will be moved to a new address space. After that, the control flow transfers of the function will be tracked and analyzed in real time to judge their legitimacy. Experiment results and analysis show that MagBox can mitigate CRAs, and only introduces 3.4% performance overhead to the CPU.
Identification of ground effect and intelligent control of unmanned aerial vehicles

In the levitation and landing phases of unmanned aerial vehicles, the complicated interactions between drones and environment are difficult to be modeled, and the fast-changing motion status also makes the precise control challenging. In this paper, the dynamics of drones are first modeled and simplified. Then, an adaptive controller and deep neural network (DNN) controller are used to control the actual system with ground effect to avoid rapid levitation and hard landing. The drone is controlled manually to fly up and down to gather the data containing the relationship between disturbance and motion status, and extended Kalman filter (EKF) is applied to decrease the measurement noise. Experimental results show that more training data lead to smaller control error. The neural network enhanced controller leads to smoother levitation and landing than the adaptive controller, and they are both better than the traditional PID controller.
Edge enhancement improves adversarial robustness in image classification

Imperceptible adversarial examples are capable of deceiving the deep neural networks with high confidence. Recent studies show that it is particularly effective to control the attack space to low-frequency components of the image on the basis of adversarial training. However, those methods face the problem of losing valuable knowledge, especially shape information, which is vital for classification and robustness. To alleviate this issue, we propose a new method based on edge detection, named Edge Enhancement (EE), which can explicitly make up for the missing shape information in frequency constraints and further enhance the adversarial robustness. Specifically, we first employ a traditional edge detection algorithm called Canny to obtain shape information due to its simplicity and intrinsic robustness. Then, we augment the low-frequency space via obtained shape features, with the weighting operation carried on. This operation can be regarded as an emphasis on shape information, which could mitigate the texture bias of deep neural networks, thereby further serving the robustness. Finally, we feed the augmented features into the deep neural network. It is worth noting that these modules are optimized along with the deep neural network, which enables an end-to-end training fashion. Experimental results show that our proposed model can significantly improve adversarial robustness over the state-of-the-art methods on three benchmark datasets, including MNIST, Tiny ImageNet, and particularly ImageNet. For example, our method achieves 51.66% accuracy on ImageNet under 10-iteration targeted PGD white-box attack where the prior art has 36.94% accuracy. Code is available at https://github.com/Aiqz/Edge-Enhancement.
The relationship between inclusion/void orientation and speed in wheel-thrown pots

The activity of wheel-throwing a ceramic vessel is governed by a number of physical forces. These are the rotational kinetic energy (expressed in terms of angular velocity and the moment of inertia) and manual pressures. Given the importance of the potter's wheel worldwide, and the frequently postulated socio-cultural meaning of wheel-throwing in particular, foundational research on the physics behind wheel-throwing has been under-represented, and there is a lack of understanding of the relationship between speed of wheel rotation, lifting speed and the orientation of clay pores or inclusions. The speed of rotation is highly relevant as different wheels can achieve different speeds. Thus, knowing the speed during manufacture may allow scholars to reconstruct the original device, manufacturing technique, and perhaps even the potter's level of skill. Two contrasting views can be found in the literature: a) there is a direct positive relationship between these variables, and that the angle of the inclusions/voids can therefore tell us about the wheel's speed at the time of making the pot and hence the device used; b) because pots are never thrown in one single motion, the angle of inclusions/voids is an averaging of all actions executed by the potter, and thus cannot provide meaningful inferences about speeds or device. Experiments with professional potters were devised to explore the relationship between these variables. They show that the interaction between the various physical forces, clay and potter is complex, leading to a large variance even within the work of a single potter, with finger pressure recognized as a major influencing variable. It is clear that angles of inclusions or voids cannot be used to project backwards to the wheel speed, lifting speed, skill level or device used. • Diagonal angle alignment of inclusions or voids identifies a vessel as wheel-thrown • Inclusion/void orientation does not allow retrospective deductions on wheel speed, lifting speed, the device used or a potter's skill • Inclusion or void orientation varies intra- and inter-potter • Finger pressure is most likely responsible for the steep inclusion or void orientation characteristic of wheel-throwing
Streaming feature selection via graph diffusion

Streaming feature selection for unlabeled data aims to remove redundant and irrelevant features from the continuously arriving features without label information. Most existing methods usually focus on selecting a small set of features that approximately reconstruct each sample in the raw data. However, the real-world streaming data may contain irrelevant features which the current reconstruction strategy cannot effectively exclude. These irrelevant features significantly impair the reliability of the selected feature subset. To address this problem, we introduce a dynamic similarity graph to learn the pairwise sample correlations for adaptively evaluating irrelevant features. By virtue of similarity graph diffusion, the unreliable similarities caused by irrelevant features can be gradually eliminated. The past and current diffused graphs are then used to guide feature selection, thus successfully removing redundant and irrelevant features, respectively. The proposed method consists of two stages: 1) minimum redundancy: accepting only features containing new information based on the past diffused graph; 2) maximum relevance: selecting the most relevant features based on the current diffused graph. Additionally, a compound threshold operator is derived to solve the graph-based learning objective. Extensive experiments on real-world data demonstrate that the proposed method outperforms state-of-the-art unsupervised feature selection methods.
Evaluation of end-to-end aspect-based sentiment analysis methods employing novel benchmark dataset for aspect, and opinion review analysis

Aspect-based sentiment analysis (ABSA) deals with the determination of sentiments for opinion targets. While historically this research task has been addressed with pipeline approaches, more recent works use neural networks to jointly deal with the aspect term and opinion term extraction, as well as the polarity classification. Although learned together, most NN-based approaches and all pipeline approaches do not model correlations between the tasks. This is also based on the absence of adequate datasets which are annotated for all sub-tasks in a unified tagging scheme. We address this bottleneck and introduce the first purposely designed and annotated dataset for ABSA. The DAORA dataset covers 2,100 Tripadvisor reviews, and it is annotated on aspect terms, opinion terms, as well as aspect term polarity, using a unified tagging scheme. It was designed especially for end-to-end aspect-based sentiment analysis of real-world reviews and does not use any sentence repetition or removal. We evaluate the DAORA dataset in several experiments employing state-of-the-art models for ABSA. We set benchmarks and analyze the strengths as well as weaknesses of the data and approaches.
Unsupervised abstractive summarization via sentence rewriting

Unsupervised extractive summarization aims to extract salient sentences from the document without labeled corpus. Existing methods have achieved promising progress, thanks to the power of large-scale pre-trained language models and high-quality contextualized representations. However, extractive summaries often fail to maintain smooth transitions between sentences and struggle to form a coherent and fluent text due to splicing of sentences. Nevertheless, to the best of our knowledge, very few studies currently focus on unsupervised abstractive summarization. Inspired by the intuitive human process of writing summaries, which involves extracting salient sentences first and then reconstructing them, in this paper, we propose an Extract-then-Abstract framework to generate more coherent and human-like summary. Specifically, we first adopt extractive summarization model as summarizer to generate extractive summary in the extraction stage. Then in the abstraction stage, we propose a BART-based sentence write model to generate more coherent and fluent abstractive summary. To this end, we design a novel parallel data creation method for our rewrite model by proposing an effective sentence sampling strategy without any manual annotation cost. Extensive experiments including automatic evaluation and human evaluation demonstrate that our framework consistently outperforms strong baselines for unsupervised abstractive summarization and can generate more coherent and human-like summary while maintaining in competitive ROUGE scores for unsupervised extractive summarization.
Feature Inference Attack on Shapley Values

As a solution concept in cooperative game theory, Shapley value is highly recognized in model interpretability studies and widely adopted by the leading Machine Learning as a Service (MLaaS) providers, such as Google, Microsoft, and IBM. However, as the Shapley value-based model interpretability methods have been thoroughly studied, few researchers consider the privacy risks incurred by Shapley values, despite that interpretability and privacy are two foundations of machine learning (ML) models.
QFaaS

Serverless computing has greatly simplified cloud programming. It liberates cloud tenants from various system administration and resource management tasks, such as configuration and provisioning. Under this new cloud computing paradigm, a single monolithic application is divided into separate stateless functions, i.e., function-as-a-service (FaaS), which are then orchestrated together to support complex business logic. But there is a fundamental cost associated with this enhanced flexibility. Internal network connections between functions are now initiated frequently, to support serverless features such as agile autoscaling and function chains, raising communication latency. To alleviate this cost, current serverless providers sacrifice security for performance, keeping internal function communications unencrypted.
Save the Yummy Candyland: An Asymmetric Virtual Reality Game

This preliminary study explored how to make otherwise passive bystanders into an integral part of a virtual reality (VR) experience using asymmetric game design. We present a fully implemented collaborative VR game, co-designed by Namibian school children. The team needs to solve puzzles, whereby one player, using a VR headset, enters the digital world of Candyland, communicating with the other teammates, who are using a printed puzzle book. Ten Namibian school children, split into three groups, evaluated the game. Both the children inside and outside of VR were excited and eager to play the game through, despite the game being too complex, with too little feedback and a number of design issues, which required much guidance from the researchers.
Understanding Player Performance and Gaming Experience while Playing a First-Person Shooter with Auditory Latency

Visual latency is known to decrease player performance and experience starting at 25 ms. Less is known about the effects of auditory latency in video games. To investigate the effects of auditory latency, we added auditory latency to a publicly available and latency-sensitive first-person shooter game. Using the game, we conducted a study with 24 participants playing the game with four different levels of auditory latency (0 ms, 50 ms, 100 ms, 200 ms). The results of a Bayesian analysis support a model with no true effect of auditory latency on game experience and player performance in first-person shooter games. Hence, our preliminary results indicate that auditory latency may not affect gamers with the same magnitude as visual latency.
Because the Night - Immersive Theatre for Digital Audiences:

Immersive theatre is a highly interactive format that raises the audience's agency over their experience, and, depending on their actions, provides each member with their own understanding of the overarching narrative that developed based on the spatial path they undertook. This theatre format challenges traditional theatre digitisation techniques as current archiving and reproduction methods cannot authentically replicate the affordances (or ‘actions possible’) that audiences have in the original performance. Theatres need to explore new methods of digitisation and archiving, to preserve past experiences and cater to an audience who will access content digitally. This report documents the design decisions and their impact for the interactive prototype digitisation and archival of the 2021 immersive-theatre production “Because the Night” for a digital audience. This interactive digital archive utilises game design and game engine technology to map and transfer the affordances of audiences from the original immersive theatre play to a digital platform, leveraging the game engine's innate affordances, including freedom of movement, spatialised audio, and user intractability. To deliver these research outcomes, the project captured 360-degree video of the performance, music, images of props, and 360-degree photographs of the set, all embedded into a game engine system to authentically preserve the immersive, interactive, and ambient experience of the original production. The resulting system was able to successfully map several of the performances original affordances, including environmental exploration, spatially emergent narratives, object inspection and exploration, and interactive environmental challenges. This document reports on three matters: the design process and decisions to authentically capture the affordances of the theatre production in a digital gameplay experience; the current status of this research project; and the proposed approach to testing the prototype with users.
Characterizing and Detecting Non-Consensual Photo Sharing on Social Networks

Photo capturing and sharing have become routine daily activities for social platform users. Alongside the entertainment of social interaction, we are experiencing tremendous visual violation and photo abusing. Especially, users may be unconsciously filmed and exposed online, which is termed as the non-consensual sharing issue. Unfortunately, this problem cannot be well handled with proactive access control or dedicated bystander detection, as users are unaware of their situations and may be filmed stealthily. We propose Videre on behalf of the privacy of the unaware parties in a way that they would be automatically identified and warned before such photos go public. For this, we first elaborate on the predominant features encountered in non-consensual captured photos via a thorough user study. Then we establish a dataset for this context and build a classifier as a proactive detector based on multi-deep-feature fusion. To relieve the burden of person-wise unawareness detection, we further design a signature-based filter for local pre-authorization, which can also implicitly avoid classification errors. We implement and test Videre in various field settings to demonstrate its effectiveness and performance.
Shifted Inverse

While most work on differential privacy has focused on protecting the privacy of tuples, it has been realized that such a simple model cannot capture the complex user-tuple relationships in many real-world applications. Thus, user differential privacy (user-DP) has recently gained more attention, which includes node-DP for graph data as a special case. Most existing work on user-DP has only studied the sum estimation problem. In this work, we design a general DP mechanism for any monotonic function under user-DP with strong optimality guarantees. While our general mechanism may run in super-polynomial time, we show how to instantiate an approximate version in polynomial time on some common monotonic functions, including sum, k-selection, maximum frequency, and distinct count. Finally, we conduct experiments on all these functions and the results show that our framework is more general and obtains better results in many cases.
Vizard

Owner-centric control is a widely adopted method for easing owners' concerns over data abuses and motivating them to share their data out to gain collective knowledge. However, while many control enforcement techniques have been proposed, privacy threats due to the metadata leakage therein are largely neglected in existing works. Unfortunately, a sophisticated attacker can infer very sensitive information based on either owners' data control policies or their analytic task participation histories (e.g., participating in a mental illness or cancer study can reveal their health conditions). To address this problem, we introduce Vizard, a metadata-hiding analytic system that enables privacy-hardened and enforceable control for owners. Vizard is built with a tailored suite of lightweight cryptographic tools and designs that help us efficiently handle analytic queries over encrypted data streams coming in real-time (like heart rates). We propose extension designs to further enable advanced owner-centric controls (with AND, OR, NOT operators) and provide owners with release control to additionally regulate how the result should be protected before deliveries. We develop a prototype of Vizard that is interfaced with Apache Kafka, and the evaluation results demonstrate the practicality of Vizard for large-scale and metadata-hiding analytics over data streams.
Time-space domain assignment for generalized covariance intersection fusion with labeled multitarget densities

This paper addresses the generalized covariance intersection (GCI) fusion for labeled random finite sets, avoiding the label inconsistency sensitivity problem and the information loss for target identities. In this paper, the label assignment is considered from the perspective of space and time. First, to solve the label inconsistency sensitivity problem, the joint label space for the support of fused labeled random finite sets is proposed to represent the target identity and the assignments of labels of different local label spaces, followed by obtaining the multitarget density over the joint label space via the GCI fusion. To avoid the information loss for target identities so as to improve the performance of the track continuity, we utilize all the label information to establish the assignment of targets across the time steps. Specifically, the identity distribution for each target is extracted from the global multitarget density and then utilized for computing the Cauchy-Schwarz divergence (CSD) of targets across the time steps. The assignment between the target identities across the time steps is obtained by formulating a linear assignment problem, which can be effectively solved by the Hungarian algorithm. Simulation results exhibit the effectiveness of the proposed approach in challenging scenarios.
The Reality of Virtual Reality in Echocardiography Education?

VIRTUAL REALITY (VR) is a technology that immerses the user in an artificial 3-dimensional (3D) environment via wearable technology, namely VR headsets. Lately, VR systems have evolved to be portable, realistic, and smoother to navigate in real-time. Dropping costs has made handheld devices more accessible, and finding use in a variety of areas in healthcare. The VR experience comes from a feeling of being connected to the environment, the ability to manipulate it and receive feedback that can be visual, auditory, and even sensory.
The Performance of Single-Image Super-Resolution Algorithm: EDSR

Single-Image Super-Resolution (SISR) is a technology to enhance the resolution of images. This paper discusses the performance of EDSR, one of the best SISR algorithms, under different conditions. B100, Set14, Urban100, Pixiv Top Daily Illustration 2018 and Art Images: Drawing/Painting/Sculptures/Engravings are imported as experiment datasets. According to the analysis, results are evaluated with SSIM and PSNR index. This research widens the knowledge of the performance of SISR method, EDSR in particular, under different circumstances. These results shed light on guiding further exploration for SISR.
RetainEXT: Enhancing Rare Event Detection and Improving Interpretability of Health Records using Temporal Neural Networks

A recurring theme during the pandemic was the shortage of hospital beds. Despite all efforts, the healthcare system still faces 25 % of resource strain felt during the first peak of coronavirus. Digitisation of Electronic Healthcare Records (EHRs) and the pandemic have brought about many successful applications of Recurrent Neural Networks (RNNs) to predict patients' current and future states. Despite their strong per-formance, it remains a challenge for users to delve into the black box which has heavily influenced researchers to utilise more interpretable techniques such as ID-Convolutional neural networks. Others focus on using more interpretable machine learning techniques but only achieve high performance on a select subset of patients. By collaborating with medical experts and artificial intelligence scientists, our study improves on the REverse Time AttentIoN EX model, a feature and visit level attention network, for increased interpretability and usability of RNNs in predicting COVID-19-related hospitalisations. We achieved 82.40 % area under the receiver operating characteristic curve and showcased effective use of the REverse Time AttentIoN EXTension model and EHRs in understanding how individual medical codes contribute to hospitalisation risk prediction. This study provides a guideline for researchers aiming to design interpretable temporal neural networks using the power of RNNs and data mining techniques.