Reviewerly/exHarmony huggingface dataset:
Hybrid Approaches for Smart Contracts in Land Administration: Lessons from Three Blockchain Proofs-of-Concept

The emergence of “blockchain” technology as an alternative data management technique has spawned a myriad of conceptual and logical design work across multiple industries and sectors. It is also argued to enable operationalisation of the earlier “smart contract” concept. The domain of land administration has actively investigated these opportunities, albeit also largely at the conceptual level, and usually with a whole-of-sector or “big bang” industry transformation perspective. Less reporting of applied case applications is evident, particularly those undertaken in collaboration with practicing land sector actors. That said, pilots and test cases continue to act as a basis for understanding the relative merits, drawbacks, and implementation challenges of the smart contract concept in land administration. In this vein, this paper extends upon and further refines the existing discourse on smart contracts within the land sector, by giving an updated, if not more nuanced, view of example applications, opportunities, and barriers. In contrast to the earlier works, a hybrid solution that mixes smart contract use with existing technology infrastructure—enabling preservation of the role of a land registry agency as the ultimate arbiter of valid claims—is proposed. This is hypothesised to minimise disruptions, whilst maximising the benefits. Examination of proof-of-concept work on smart contract and blockchain applications in Sweden, Australia (State of New South Wales), and Canada (Province of British Columbia) is undertaken. Comparative analysis is undertaken using several frameworks including: (i) business requirements adherence, (ii) technology readiness and maturity assessment, and (iii) strategic grid analysis. Results show that the hybrid approach enables adherence to land dealing business requirements and that the proofs-of-concept are a necessary step in the development trajectory. Furthering the uptake will likely depend on again taking a whole-of-sector perspective, and attending to remaining issues around business models, stakeholder acceptance, partnerships and trust building, and legal issues linked to data decentralisation and security.
Real-time and dynamic fault-tolerant scheduling for scientific workflows in clouds

Cloud computing has become a popular technology for executing scientific workflows. However, with a large number of hosts and virtual machines (VMs) being deployed, the cloud resource failures, such as the permanent failure of hosts (HPF), the transient failure of hosts (HTF), and the transient failure of VMs (VMTF), bring the service reliability problem. Therefore, fault tolerance for time-consuming scientific workflows is highly essential in the cloud. However, existing fault-tolerant (FT) approaches consider only one or two above failure types and easily neglect the others, especially for the HTF. This paper proposes a Real-time and dynamic Fault-tolerant Scheduling (ReadyFS) algorithm for scientific workflow execution in a cloud, which guarantees deadline constraints and improves resource utilization even in the presence of any resource failure. Specifically, we first introduce two FT mechanisms, i.e., the replication with delay execution (RDE) and the checkpointing with delay execution (CDE), to cope with HPF and VMTF, simultaneously. Additionally, the rescheduling (ReSC) is devised to tackle the HTF that affects the resource availability of the entire cloud datacenter. Then, the resource adjustment (RA) strategy, including the resource scaling-up (RS-Up) and the resource scaling-down (RS-Down), is used to adjust resource demands and improve resource utilization dynamically. Finally, the ReadyFS algorithm is presented to schedule real-time scientific workflows by combining all the above FT mechanisms with RA strategy. We conduct the performance evaluation with real-world scientific workflows and compare ReadyFS with five vertical comparison algorithms and three horizontal comparison algorithms. Simulation results confirm that ReadyFS is indeed able to guarantee the fault tolerance of scientific workflow execution and improve cloud resource utilization.
Transfer of training—Virtual reality training with augmented multisensory cues improves user experience during training and task performance in the real world

Virtual reality (VR) can create safe, cost-effective, and engaging learning environments. It is commonly assumed that improvements in simulation fidelity lead to better learning outcomes. Some aspects of real environments, for example vestibular or haptic cues, are difficult to recreate in VR, but VR offers a wealth of opportunities to provide additional sensory cues in arbitrary modalities that provide task relevant information. The aim of this study was to investigate whether these cues improve user experience and learning outcomes, and, specifically, whether learning using augmented sensory cues translates into performance improvements in real environments. Participants were randomly allocated into three matched groups: Group 1 (control) was asked to perform a real tyre change only. The remaining two groups were trained in VR before performance was evaluated on the same, real tyre change task. Group 2 was trained using a conventional VR system, while Group 3 was trained in VR with augmented, task relevant, multisensory cues. Objective performance, time to completion and error number, subjective ratings of presence, perceived workload, and discomfort were recorded. The results show that both VR training paradigms improved performance for the real task. Providing additional, task-relevant cues during VR training resulted in higher objective performance during the real task. We propose a novel method to quantify the relative performance gains between training paradigms that estimates the relative gain in terms of training time. Systematic differences in subjective ratings that show comparable workload ratings, higher presence ratings and lower discomfort ratings, mirroring objective performance measures, were also observed. These findings further support the use of augmented multisensory cues in VR environments as an efficient method to enhance performance, user experience and, critically, the transfer of training from virtual to real environment scenarios.
Deep sequential model for anchor recommendation on live streaming platforms

Live streaming has grown rapidly in recent years, attracting increasingly more participation. As the number of online anchors is large, it is difficult for viewers to find the anchors they are interested in. Therefore, a personalized recommendation system is important for live streaming platforms. On live streaming platforms, the viewer's and anchor's preferences are dynamically changing over time. How to capture the user's preference change is extensively studied in the literature, but how to model the viewer's and anchor's preference changes and how to learn their representations based on their preference matching are less studied. Taking these issues into consideration, in this paper, we propose a deep sequential model for live streaming recommendation. We develop a component named the multi-head related-unit in the model to capture the preference matching between anchor and viewer and extract related features for their representations. To evaluate the performance of our proposed model, we conduct experiments on real datasets, and the results show that our proposed model outperforms state-of-the-art recommendation models.
CMUA-Watermark: A Cross-Model Universal Adversarial Watermark for Combating Deepfakes

Malicious applications of deepfakes (i.e., technologies generating target facial attributes or entire faces from facial images) have posed a huge threat to individuals' reputation and security. To mitigate these threats, recent studies have proposed adversarial watermarks to combat deepfake models, leading them to generate distorted outputs. Despite achieving impressive results, these adversarial watermarks have low image-level and model-level transferability, meaning that they can protect only one facial image from one specific deepfake model. To address these issues, we propose a novel solution that can generate a Cross-Model Universal Adversarial Watermark (CMUA-Watermark), protecting a large number of facial images from multiple deepfake models. Specifically, we begin by proposing a cross-model universal attack pipeline that attacks multiple deepfake models iteratively. Then, we design a two-level perturbation fusion strategy to alleviate the conflict between the adversarial watermarks generated by different facial images and models. Moreover, we address the key problem in cross-model optimization with a heuristic approach to automatically find the suitable attack step sizes for different models, further weakening the model-level conflict. Finally, we introduce a more reasonable and comprehensive evaluation method to fully test the proposed method and compare it with existing ones. Extensive experimental results demonstrate that the proposed CMUA-Watermark can effectively distort the fake facial images generated by multiple deepfake models while achieving a better performance than existing methods. Our code is available at https://github.com/VDIGPKU/CMUA-Watermark.
Face-to-Face vs. E-Learning Models in the COVID-19 Era: Survey Research in a Spanish University

This study shows the results of an autobiographical questionnaire of Spanish university students regarding two different educational models caused by the COVID-19 pandemic: face-to-face and e-learning. The aim is to discover their perceptions and opinions about their experiences during the learning process and what they have experienced during this global emergency and period of home confinement. The sample is made up of 100 students from the Primary Education Degree programme and the research was carried out through a qualitative study of the questionnaire. The results, divided into categories of each educational model, show the interpretation that the students make of the current reality and their own learning process. The most important aspect of the face-to-face learning model, according to 75% of the students, is direct communication with the teacher, and for 88% of them this model was effective. For the e-learning model, the flexible schedule, the economic savings and explanatory videos are the relevant ideas that the students express, with 68% stating that it was an effective model. The main conclusion is that the students prefer to continue with the face-to-face learning process (49%) rather than online teaching (7%) or, failing that, mixed or blended learning (44%), where the theoretical classes could be online and the practical classes could be face-to-face.
The Role of User Behaviour in Improving Cyber Security Management

Information security has for long time been a field of study in computer science, software engineering, and information communications technology. The term ‘information security’ has recently been replaced with the more generic term cybersecurity. The goal of this paper is to show that, in addition to computer science studies, behavioural sciences focused on user behaviour can provide key techniques to help increase cyber security and mitigate the impact of attackers’ social engineering and cognitive hacking methods (i.e., spreading false information). Accordingly, in this paper, we identify current research on psychological traits and individual differences among computer system users that explain vulnerabilities to cyber security attacks and crimes. Our review shows that computer system users possess different cognitive capabilities which determine their ability to counter information security threats. We identify gaps in the existing research and provide possible psychological methods to help computer system users comply with security policies and thus increase network and information security.
Delving into Data: Effectively Substitute Training for Black-box Attack

Deep models have shown their vulnerability when processing adversarial samples. As for the black-box attack, without access to the architecture and weights of the attacked model, training a substitute model for adversarial attacks has attracted wide attention. Previous substitute training approaches focus on stealing the knowledge of the target model based on real training data or synthetic data, without exploring what kind of data can further improve the transferability between the substitute and target models. In this paper, we propose a novel perspective substitute training that focuses on designing the distribution of data used in the knowledge stealing process. More specifically, a diverse data generation module is proposed to synthesize large-scale data with wide distribution. And adversarial substitute training strategy is introduced to focus on the data distributed near the decision boundary. The combination of these two modules can further boost the consistency of the substitute model and target model, which greatly improves the effectiveness of adversarial attack. Extensive experiments demonstrate the efficacy of our method against state-of-the-art competitors under non-target and target at-tack settings. Detailed visualization and analysis are also provided to help understand the advantage of our method.
Autonomous Quadrotor Navigation With Vision Based Obstacle Avoidance and Path Planning

Due to the high demands on military and commercial applications, the development of UAVs (unmanned aerial vehicles) has become increasingly important in recent years.In this paper, we present a vision guided autonomous navigation approach for quadrotor UAVs.A map-based offline path planning technique is developed to generate an initial path, followed by the waypoints of the trajectory for flight guidance.During the navigation, an onboard camera is utilized to acquire a sequence of monocular images for environment perception.A vision-based obstacle detection technique using optical flow is proposed for collision avoidance.The optical flow field constructed from the image sequence is used to provide the depth cues for the incoming obstacle detection.A single-board computer is adopted as a control platform, and the proposed algorithms are implemented for online and real-time processing.Several experiments are carried out in the outdoor environment for obstacles avoidance and visual guidance.The results have demonstrated the feasibility of our proposed method for path planning and autonomous navigation.
GANterfactual—Counterfactual Explanations for Medical Non-experts Using Generative Adversarial Learning

With the ongoing rise of machine learning, the need for methods for explaining decisions made by artificial intelligence systems is becoming a more and more important topic. Especially for image classification tasks, many state-of-the-art tools to explain such classifiers rely on visual highlighting of important areas of the input data. Contrary, counterfactual explanation systems try to enable a counterfactual reasoning by modifying the input image in a way such that the classifier would have made a different prediction. By doing so, the users of counterfactual explanation systems are equipped with a completely different kind of explanatory information. However, methods for generating realistic counterfactual explanations for image classifiers are still rare. Especially in medical contexts, where relevant information often consists of textural and structural information, high-quality counterfactual images have the potential to give meaningful insights into decision processes. In this work, we present GANterfactual, an approach to generate such counterfactual image explanations based on adversarial image-to-image translation techniques. Additionally, we conduct a user study to evaluate our approach in an exemplary medical use case. Our results show that, in the chosen medical use-case, counterfactual explanations lead to significantly better results regarding mental models, explanation satisfaction, trust, emotions, and self-efficacy than two state-of-the art systems that work with saliency maps, namely LIME and LRP.
DCU-Net: a dual-channel U-shaped network for image splicing forgery detection

The detection and location of image splicing forgery are a challenging task in the field of image forensics. It is to study whether an image contains a suspicious tampered area pasted from another image. In this paper, we propose a new image tamper location method based on dual-channel U-Net, that is, DCU-Net. The detection framework based on DCU-Net is mainly divided into three parts: encoder, feature fusion, and decoder. Firstly, high-pass filters are used to extract the residual of the tampered image and generate the residual image, which contains the edge information of the tampered area. Secondly, a dual-channel encoding network model is constructed. The input of the model is the original tampered image and the tampered residual image. Then, the deep features extracted from the dual-channel encoding network are fused for the first time, and then the tampered features with different granularity are extracted by dilation convolution, and then, the secondary fusion is carried out. Finally, the fused feature map is input into the decoder, and the predicted image is decoded layer by layer. The experimental results on Casia2.0 and Columbia datasets show that DCU-Net performs better than the latest algorithm and can accurately locate tampered areas. In addition, the attack experiments show that DCU-Net model has good robustness and can resist noise and JPEG recompression attacks.
Real-world trajectory sharing with local differential privacy

Sharing trajectories is beneficial for many real-world applications, such as managing disease spread through contact tracing and tailoring public services to a population's travel patterns. However, public concern over privacy and data protection has limited the extent to which this data is shared. Local differential privacy enables data sharing in which users share a perturbed version of their data, but existing mechanisms fail to incorporate user-independent public knowledge (e.g., business locations and opening times, public transport schedules, geo-located tweets). This limitation makes mechanisms too restrictive, gives unrealistic outputs, and ultimately leads to low practical utility. To address these concerns, we propose a local differentially private mechanism that is based on perturbing hierarchically-structured, overlapping n -grams (i.e., contiguous subsequences of length n ) of trajectory data. Our mechanism uses a multi-dimensional hierarchy over publicly available external knowledge of real-world places of interest to improve the realism and utility of the perturbed, shared trajectories. Importantly, including real-world public data does not negatively affect privacy or efficiency. Our experiments, using real-world data and a range of queries, each with real-world application analogues, demonstrate the superiority of our approach over a range of alternative methods.
Solving Aspect Category Sentiment Analysis as a Text Generation Task

Aspect category sentiment analysis has attracted increasing research attention.The dominant methods make use of pre-trained language models by learning effective aspect category-specific representations, and adding specific output layers to its pre-trained representation.We consider a more direct way of making use of pre-trained language models, by casting the ACSA tasks into natural language generation tasks, using natural language sentences to represent the output.Our method allows more direct use of pre-trained knowledge in seq2seq language models by directly following the task setting during pre-training.Experiments on several benchmarks show that our method gives the best reported results, having large advantages in few-shot and zero-shot settings.
New Frontiers in Bayesian Modeling Using the <b>INLA</b> Package in <i>R</i>

The INLA package provides a tool for computationally efficient Bayesian modeling and inference for various widely used models, more formally the class of latent Gaussian models. It is a non-sampling based framework which provides approximate results for Bayesian inference, using sparse matrices. The swift uptake of this framework for Bayesian modeling is rooted in the computational efficiency of the approach and catalyzed by the demand presented by the big data era. In this paper, we present new developments within the INLA package with the aim to provide a computationally efficient mechanism for the Bayesian inference of relevant challenging situations.
Detecting prohibited objects with physical size constraint from cluttered X-ray baggage images

X-ray baggage image inspection aims to detect prohibited objects. Existing inspection systems often rely on humans to scrutinize X-ray images. Although several deep-learning-based prohibited object detection methods have been proposed to facilitate human inspection, they often neglect the actual physical sizes of items and thus often lead to many false alarms. To address this issue, in this paper we propose a two-stage prohibited object detection network to identify prohibited objects from heavily cluttered X-ray baggage images. In particular, we first statistically analyzed the physical size distribution of different prohibited object categories and found that their physical sizes exhibit clear distinction. Therefore, we formulated this physical size constraint as a regularization term during the process of training the proposed detection network. Current X-ray datasets only provide annotations of prohibited objects while ignoring uninhibited ones. This may lead to many false positives during testing. Thus, we propose a hard-negative-sample selection scheme to generate proposals of common goods from segmented foreground regions. With these selected hard negative samples, the proposed detector can better distinguish prohibited objects while precluding overfitting on the training dataset. Extensive experimentation demonstrates that the proposed method outperforms state-of-the-art object detection methods. The source code and pre-trained models will be released at https://github.com/xraydetec/Xdet.
SemanticStyleGAN: Learning Compositional Generative Priors for Controllable Image Synthesis and Editing

Recent studies have shown that StyleGANs provide promising prior models for downstream tasks on image synthesis and editing. However since the latent codes of StyleGANs are designed to control global styles it is hard to achieve a fine-grained control over synthesized images. We present SemanticStyleGAN where a generator is trained to model local semantic parts separately and synthesizes images in a compositional way. The structure and texture of different local parts are controlled by corresponding latent codes. Experimental results demonstrate that our model provides a strong disentanglement between different spatial areas. When combined with editing methods designed for StyleGANs it can achieve a more fine-grained control to edit synthesized or real images. The model can also be extended to other domains via transfer learning. Thus as a generic prior model with built-in disentanglement it could facilitate the development of GAN-based applications and enable more potential downstream tasks.
Propping Up Virtual Reality With Haptic Proxies

Physical props serving as proxies for virtual objects (haptic proxies) offer a cheap, convenient, and compelling way of delivering a sense of touch in virtual reality (VR). To successfully use haptic proxies for VR, they have to be both similar to and colocated with their virtual counterparts. In this article, we introduce a taxonomy organizing techniques using haptic proxies for VR into eight categories based on when the techniques are deployed (offline or real-time), what reality is being manipulated (physical or virtual reality), and the purpose of the techniques (to affect object perception or the mapping between real and virtual objects). Finally, we discuss key advantages and limitations of the different categories of techniques.