Reviewerly/exHarmony huggingface dataset:
Mutual Context Network for Jointly Estimating Egocentric Gaze and Action

In this work, we address two coupled tasks of gaze prediction and action recognition in egocentric videos by exploring their mutual context: the information from gaze prediction facilitates action recognition and vice versa. Our assumption is that during the procedure of performing a manipulation task, on the one hand, what a person is doing determines where the person is looking at. On the other hand, the gaze location reveals gaze regions which contain important and information about the undergoing action and also the non-gaze regions that include complimentary clues for differentiating some fine-grained actions. We propose a novel mutual context network (MCN) that jointly learns action-dependent gaze prediction and gaze-guided action recognition in an end-to-end manner. Experiments on multiple egocentric video datasets demonstrate that our MCN achieves state-of-the-art performance of both gaze prediction and action recognition. The experiments also show that action-dependent gaze patterns could be learned with our method.
Towards more-than-human digital data studies: developing research-creation methods

As the number of digital technologies expands, entering more domains of everyday life, people’s activities, bodies and preferences are rendered into constantly changing flows of digitised information. The interdisciplinary field of critical data studies has emerged in response. In this article, we outline the design and development of methods employed in our new project ‘Living with Personal Data’ as a move towards expanding the knowledge base and methodological approaches of critical data studies. Our approach takes up more-than-human theoretical perspectives and research-creation methods to elicit the affective and multisensory contexts of people’s feelings, practices and imaginaries concerning their digital data. We describe a set of workshops established to experiment with some new methods we have devised for our project’s fieldwork. The article ends with some reflections on what these theories and methods can offer for a reimagined digital data studies that can acknowledge and surface more-than-human dimensions.
Tracking the deployment of TLS 1.3 on the web

Transport Layer Security (TLS) 1.3 is a redesign of the Web's most important security protocol. It was standardized in August 2018 after a four year-long, unprecedented design process involving many cryptographers and industry stakeholders. We use the rare opportunity to track deployment, uptake, and use of a new mission-critical security protocol from the early design phase until well over a year after standardization. For a profound view, we combine and analyze data from active domain scans, passive monitoring of large networks, and a crowd-sourcing effort on Android devices. In contrast to TLS 1.2, where adoption took more than five years and was prompted by severe attacks on previous versions, TLS 1.3 is deployed surprisingly speedily and without security concerns calling for it. Just 15 months after standardization, it is used in about 20% of connections we observe. Deployment on popular domains is at 30% and at about 10% across the com/net/org top-level domains (TLDs). We show that the development and fast deployment of TLS 1.3 is best understood as a story of experimentation and centralization. Very few giant, global actors drive the development. We show that Cloudflare alone brings deployment to sizable numbers and describe how actors like Facebook and Google use their control over both client and server endpoints to experiment with the protocol and ultimately deploy it at scale. This story cannot be captured by a single dataset alone, highlighting the need for multi-perspective studies on Internet evolution.
A novel group recommender system based on members’ influence and leader impact

Group recommender systems have been designed which, instead of suggesting one or more items to people individually, concurrently recommend them to a group of people who have a common interest, with a view to satisfying each of them. One of the most important issues in these systems is social relationships and the influence of individuals on each other in groups. In this article, a new method has been proposed to compute members’ influence on each other based on similarity and trust. Normally in groups, there are some people called Leaders who are trusted more than other members and have a significant impact on the members. Therefore, this study has attempted to compute the leader’s impact on the members’ preferences. One remarkable aspect of this method is the use of a combination of fuzzy clustering and similarity measure to find users who have similar interests. Furthermore, an implicit trust metric has been formulated to improve the efficiency of the influence process and leader identification. Eventually, the proposed method which has been evaluated utilizing a MovieLens 100k dataset showed significant results by MAE, RMSE, Precision, and a group-satisfaction-measure compared to state-of-the-art techniques. Further, the proposed trust metric has shown better efficiency compared to some state-of-the-art methods.
Blockchain for precision irrigation: Opportunities and challenges

Abstract In modern cities, smart irrigation systems are designed to operate via Internet of things (IoT) based sensor units having precise measurements of irrigation requirements such as amount of water, crop temperature, and humidity to build a robust supply chain ecosystem. The usage of sensors and networking units enable the optimal usage of irrigation resources, and is termed as precision irrigation (PI). Thus, PI leverage an efficient solution to handle the scarcity of essential resources such as food, water, land units, and crop yields. Thus, farmers gets better returns in the market due to high production. However, in PI, the exchange of crop readings from sensor units to actuators are processed through open channels, that is, Internet. Thus, it open the doors for malicious intruders to deploy network and sensor‐based attacks on PI‐sensors, to drain the available resources, and battery power of sensor nodes in the network. This reduces the optimum and precise utilization of irrigation resources, low‐yield crops and damaged crops in supply chain systems. This leads to dissatisfaction among agriculture stakeholders such as quality control units, logistics, suppliers, and customers. Motivated from the above discussions, the survey presents the advantages of integrating blockchain (BC) with PI to handle issues pertaining to security, trust, and transactional payments among agriculture stakeholders. The survey is directed to achieve threefold objective‐ attack models and countermeasures in PI systems, integration of BC in PI to mitigate attack models, and research challenges in deploying BC in PI. To address the first objective, the survey proposes an in‐depth comparative analysis of traditional irrigation systems with PI, with discussions on attack models. To address the second objective, the survey proposes an integration model of BC with PI to secure IoT sensors, and maintain trust and transparency among stakeholders. Finally, the survey addresses the open research challenges of deploying BC in PI‐based irrigation systems, and presents a case‐study of AgriChain as an industry ready‐solution that envisions BC with PI ecosystem. Thus, the proposed survey acts as a roadmap for agriculture industry stakeholders, researchers, to deploy BC in IoT‐based PI that leverages an efficient, robust, trust‐worthy, and secure ecosystem.
Diversified and Scalable Service Recommendation With Accuracy Guarantee

As one of the most successful recommendation techniques, neighborhood-based collaborative filtering (CF), which recommends appropriate items to a target user by identifying similar users or similar items, has been widely applied to various recommender systems. Although many neighbor-based CF methods have been put forward, there are still some open issues that have remained unsolved. First, the ever-increasing volume of user–item rating data decreases the recommendation efficiency significantly as a recommender system needs to analyze all the rating data when searching for similar neighbors or similar items. In this situation, users’ requirements on quick response may not be met. Second, in neighbor-based CF methods, more attention is paid to the recommendation accuracy while other key indicators of recommendation performances are often ignored, i.e., recommendation diversity (RD), which probably produces similar or redundant items in the recommended list and decreases users’ satisfaction. Considering these issues, a diversified and scalable recommendation method (called DR_LT) based on locality-sensitive hashing and cover tree is proposed in this article, where the item topic information is used to optimize the final recommended list. We show the effectiveness of our proposed method through a set of experiments on MovieLens data set that clearly shows the feasibility of our proposal in terms of item recommendation accuracy, diversity, and scalability.
Implementing AES Encryption on Programmable Switches via Scrambled Lookup Tables

AES is a symmetric encryption algorithm widely used in many applications. An AES implementation in the data plane can help us build in-network security and privacy applications, such as IP header encryption or onion routing. However, it is not straightforward to implement AES on today's commodity programmable switches, which may not include a dedicated cryptography co-processor and support only simple arithmetic operation and table lookup. In this paper, we present the Scrambled Lookup Table technique for reducing the number of sequential arithmetic operations required for AES encryption, by utilizing the table matching capability available on programmable switches. We demonstrate an efficient implementation of AES on the Barefoot Tofino programmable switch that encrypts 10.92Gbit, 8.76Gbit, and 7.37Gbit of data per second, for AES-128, -192, and -256 respectively, using less than 15% of the switch memory.
Scene perception guided crowd anomaly detection

Crowd anomaly detection has been a research hotspot in the field of video surveillance in recent years. In most existing methods, the accuracy of anomaly detection dominantly relies on the acquisition of regions of interest (ROI) and feature extraction. However, the randomness of ROI segmentation and crowd group selection usually cannot guarantee a robust performance and thus may lead to false detection sometimes. To address these issues, this paper proposes a scene perception-based approach combining the fluid forces expression and psychological theory. The proposed method firstly introduces a flow field Visualization technology called line integral convolution to segment the moving pedestrians in the scene. Then, a scene perception-guided clustering strategy is proposed to cluster the consistency crowd group. Scene perception strategy is in line with the psychological criteria of human cognition. In clustering, it makes more reasonable use of various attributes of the pedestrians. To ensure a robust detection of the pedestrian group, we propose a fluid feature concept which considers both mass force and surface force. For each consistency group, two types of features including the image appearance feature and fluid feature are combined to describe pedestrian motion. The experimental results show that the proposed method achieves higher accuracy in comparison with some existing methods in terms of both frame-level and pixel-level measurements.
On the Design of Blockchain-Based Access Control Protocol for IoT-Enabled Healthcare Applications

Access control is one of the important security services that is essential for an Internet of Things (IoT)-enabled authorized user using his/her smart mobile device to authenticate with the trusted Hospital Authority (HA) in a hospital. After mutual authentication, a secret key is established among the user and HA for secure data transmission. The secure data (transactions) gathered by the HA from the users in the hospital is encrypted using a shared key among various trusted hospital authorities involved in the private blockchain network of hospitals. The HA of each hospital is responsible for constructing the blocks in the blockchain using the encrypted transactions because the data in healthcare application is treated as confidential and private. To deal with this important problem, we design a novel access control scheme using private blockchain technology. The proposed scheme is shown to be secure against various well-known attacks. Moreover, the proposed scheme provides better security and functionality features, and also requires low communication and computational costs as compared to relevant approaches.
Covert Hate Speech: White Nationalists and Dog Whistle Communication on Twitter

Dog whistling, a form of symbolic communication through seemingly innocuous terms is a common practice for members of far-right movements. This chapter examines how dog whistling was used on Twitter during the 2016 presidential election through a virtual ethnographic approach. Dog whistling serves to circumvent censorship by automated moderation, and adapts historical markers of the far-right as well as symbols used in other media to work within Twitter’s affordances. Thus, Twitter is employed as a channel to spread hate and signal belonging among far-right communities. In doing so, creative use is made of the platform’s technology, in the face of the site’s moderation techniques, to convey white supremacist ideas to a broader audience while staying under the radar of detection.
A self-adaptive spherical search algorithm for real-world constrained optimization problems

Determination of the global optimum of complex non-convex optimization problems of the real-world applications has remained a challenging task. Many researchers have been developing various types of effective direct search-based methods to tackle these problems. In this paper, we introduce a new variant of the recently developed Spherical Search (SS) algorithm, which contains a powerful and effective self-adaptation structure to enhance the performance. To analyze the performance, proposed algorithm is tested on the 57 test problems collected from different real-world applications. The obtained results statistically confirm the efficacy and efficiency of the proposed algorithm.
On the Linear Convergence of Random Search for Discrete-Time LQR

Model-free reinforcement learning techniques directly search over the parameter space of controllers. Although this often amounts to solving a nonconvex optimization problem, for benchmark control problems simple local search methods exhibit competitive performance. To understand this phenomenon, we study the discretetime Linear Quadratic Regulator (LQR) problem with unknown state-space parameters. In spite of the lack of convexity, we establish that the random search method with two-point gradient estimates and a fixed number of roll-outs achieves E-accuracy in O(log (1/ϵ)) iterations. This significantly improves existing results on the model-free LQR problem which require O(1/ϵ) total roll-outs.
Importance-Aware Data Selection and Resource Allocation in Federated Edge Learning System

The implementation of artificial intelligence (AI) in wireless networks is becoming more and more popular because of the growing number of mobile devices and the availability of huge amount of data. However, directly transmitting data for centralized learning will cause long communication latency owing to the limited communication resource and may incur severe privacy issue as well. To address these issues, we consider the federated edge learning (FEEL) system in this paper and develop an importance-aware joint data selection and resource allocation algorithm to maximize the learning efficiency. Aiming at selecting important data for local training, we first analyze the relation between loss decay and gradient norm, which indicates that larger gradient norm generally leads to faster learning speed. Based on this, a learning efficiency maximization problem is formulated by jointly considering the communication resource allocation and data selection. The closed-form results for optimal communication resource allocation and data selection are both developed, where some insights are also highlighted. Furthermore, an optimal algorithm with low computational complexity is developed to obtain the optimal end-to-end latency in one training period. We show that the sample size should be set to its upper limit in order to maximize the learning performance. Finally, we conduct extensive experiments on three popular convolutional neural network (CNN) models. The results show that the proposed algorithm can effectively reduce the training latency and improve the learning accuracy as compared with some benchmark algorithms.
Interactive video stylization using few-shot patch-based training

In this paper, we present a learning-based method to the keyframe-based video stylization that allows an artist to propagate the style from a few selected keyframes to the rest of the sequence. Its key advantage is that the resulting stylization is semantically meaningful, i.e., specific parts of moving objects are stylized according to the artist's intention. In contrast to previous style transfer techniques, our approach does not require any lengthy pre-training process nor a large training dataset. We demonstrate how to train an appearance translation network from scratch using only a few stylized exemplars while implicitly preserving temporal consistency. This leads to a video stylization framework that supports real-time inference, parallel processing, and random access to an arbitrary output frame. It can also merge the content from multiple keyframes without the need to perform an explicit blending operation. We demonstrate its practical utility in various interactive scenarios, where the user paints over a selected keyframe and sees her style transferred to an existing recorded sequence or a live video stream.
Cognitive load management in mobile learning systems: principles and theories

With the widespread adoption of mobile technologies, mobile-assisted learning is gaining lots of momentum. This new learning paradigm promotes education across different contexts, which is a key factor that contributes to enhancing learning irrespective of the conditions and location of the learner. Therefore, it creates an authentic learning setting whereby students can make meaningful connections to the real world while learning takes place. Previous research works in the field of mobile learning showed that improper design of learning elements is still present in mobile systems and consequently results in poor dynamic content adaptation. Some attempts to adapt learning contents with appropriate instructional design principles are conducted, but with moderate exploitation of smart technological assets in mobile learning systems and limited pedagogical reflections and cognitive factors. In this paper, a learning efficiency model chart is derived using important learning factors that can be considered to enhance mobile learning experiences. Some popular learning theories are analysed and compared with the proposed learning efficiency model chart. This investigation is considered to significantly reduce complexities that exist in mobile learning platforms and promote an enhanced mobile learning experience.
The Dynamic Privacy-Preserving Mechanisms for Online Dynamic Social Networks

Networks that constantly transmit information and change structure are becoming increasingly prevalent. However, traditional privacy models are designed to protect static information, such as records in a database or a person’s profile information, which seldom changes. This conflict between static models and dynamic environments is dramatically hindering the effectiveness and efficiency of privacy preservation in today’s dynamic world. Hence, in this paper, we formally define the concept of dynamic privacy, present two novel perspectives, privacy propagation and accumulation, on the way private information can spread through dynamic cyberspace, and develop associated theories and mechanisms for preserving privacy in advanced complex networks, such as social networking sites where data are constantly being released, shared, and exchanged.
A novel multi-stage ensemble model with enhanced outlier adaptation for credit scoring

Credit and credit-based transactions underlie the financial system. After decades of development, artificial intelligence and machine learning have brought new momentum to the credit scoring model. In this study, a novel multi-stage ensemble model with enhanced outlier adaptation is proposed to achieve good predictive power for credit scoring. To reduce the adverse effects of outliers existing in the noise-filled credit datasets, a local outlier factor algorithm is enhanced with the bagging strategy to effectively identify outliers and subsequently boost them back into the training set to construct an outlier-adapted training set that enhances the outlier adaptability of base classifiers. To improve the feature interpretability, a new dimension-reduced feature transformation method is proposed to hierarchically evolve features and extract salient features. To further strengthen the predictive power of the proposed model, a stacking-based ensemble learning method with self-adaptive parameter optimization is proposed to optimize the parameters of selected base classifiers automatically and then to construct a stacking-based multi-stage ensemble model. Ten datasets are tested with six evaluation indicators to evaluate the performance of the proposed model. The experimental results including statistical test results indicate the superior performance of the proposed model and prove its significance and effectiveness.
Attribute2Font

Font design is now still considered as an exclusive privilege of professional designers, whose creativity is not possessed by existing software systems. Nevertheless, we also notice that most commercial font products are in fact manually designed by following specific requirements on some attributes of glyphs, such as italic, serif, cursive, width, angularity, etc. Inspired by this fact, we propose a novel model, Attribute2Font, to automatically create fonts by synthesizing visually pleasing glyph images according to user-specified attributes and their corresponding values. To the best of our knowledge, our model is the first one in the literature which is capable of generating glyph images in new font styles, instead of retrieving existing fonts, according to given values of specified font attributes. Specifically, Attribute2Font is trained to perform font style transfer between any two fonts conditioned on their attribute values. After training, our model can generate glyph images in accordance with an arbitrary set of font attribute values. Furthermore, a novel unit named Attribute Attention Module is designed to make those generated glyph images better embody the prominent font attributes. Considering that the annotations of font attribute values are extremely expensive to obtain, a semi-supervised learning scheme is also introduced to exploit a large number of unlabeled fonts. Experimental results demonstrate that our model achieves impressive performance on many tasks, such as creating glyph images in new font styles, editing existing fonts, interpolation among different fonts, etc.