Reviewerly/exHarmony huggingface dataset:
Adaptive Rotated Convolution for Rotated Object Detection

Rotated object detection aims to identify and locate objects in images with arbitrary orientation. In this scenario, the oriented directions of objects vary considerably across different images, while multiple orientations of objects exist within an image. This intrinsic characteristic makes it challenging for standard backbone networks to extract high-quality features of these arbitrarily orientated objects. In this paper, we present Adaptive Rotated Convolution (ARC) module to handle the afore-mentioned challenges. In our ARC module, the convolution kernels rotate adaptively to extract object features with varying orientations in different images, and an efficient conditional computation mechanism is introduced to accommodate the large orientation variations of objects within an image. The two designs work seamlessly in rotated object detection problem. Moreover, ARC can conveniently serve as a plug-and-play module in various vision backbones to boost their representation ability to detect oriented objects accurately. Experiments on commonly used benchmarks (DOTA and HRSC2016) demonstrate that equipped with our proposed ARC module in the backbone network, the performance of multiple popular oriented object detectors is significantly improved (e.g. +3.03% mAP on Rotated RetinaNet and +4.16% on CFA). Combined with the highly competitive method Oriented R-CNN, the proposed approach achieves state-of-the-art performance on the DOTA dataset with 81.77% mAP. Code is available at https://github.com/LeapLabTHU/ARC.
COVID-19 and the secret virtual assistants: the social weapons for a state of emergency

Technologies are ubiquitous in modern Britain, gradually infiltrating many areas of our working and personal lives. But what role can technology play in the current COVID-19 pandemic? At a time when our usual face to face social interactions are temporarily suspended, many of us have reached out to technology (e.g. Skype, WhatsApp, Facebook, Zoom) to help maintain a sense of closeness and connection to friends, family and vital services. One largely unsung technology is the virtual assistant (VA), a cost-efficient technology enabling users to access the Internet of Things using little more than voice. Deploying an ecological framework, in the context of smart age-friendly cities, this paper explores how VA technology can function as an emergency response system, providing citizens with systems to connect with friends, family, vital services and offering assistance in the diagnosis of COVID-19. We provide an illustration of the potentials and challenges VAs present, concluding stricter regulation and controls should be implemented before VAs can be safely integrated into smart age-friendly cities across the globe.
Web Services Efficiency in Disadvantaged Environment

The article presents results of web services (WSs) efficiency tests carried out in the testbed emulating disadvantaged network environment. The authors discuss the advantage of different WS adaptation techniques that allow to minimize the XML message size (i.e. compression, filtering and binary coding) and the size of JPEG image attachment (i.e., resolution reduction, decreasing colour depth, JPEG compression). The presented results show the efficiency of selected methods that adapt the web services realization to the possibilities of the network. The article is summarized by conclusions and recommendations in terms of sending XML SOAP messages in disadvantaged networks.
Application-Specific Configuration of Telecommunication Systems

Telecommunication systems are evolving rapidly. The need for new configuration systems in the telecommunication area matches well with advances in the AI domain, especially in the configuration area. This paper outlines the requirements a new knowledge-based configuration system will have to fulfil to configure complete and ambitious telecommunication systems. It describes the configuration process, embedded in the procurement process, the kind of knowledge needed and the interaction between the configuration system, the operator and the potential user (customer). Furthermore, we discuss our experimental configuration system KIKon.
Approximating the Sum Operation for Marginal-MAP Inference

We study the marginal-MAP problem on graphical models, and present a novel approximation method based on direct approximation of the sum operation. A primary difficulty of marginal-MAP problems lies in the non-commutativity of the sum and max operations, so that even in highly structured models, marginalization may produce a densely connected graph over the variables to be maximized, resulting in an intractable potential function with exponential size. We propose a chain decomposition approach for summing over the marginalized variables, in which we produce a structured approximation to the MAP component of the problem consisting of only pairwise potentials. We show that this approach is equivalent to the maximization of a specific variational free energy, and it provides an upper bound of the optimal probability. Finally, experimental results demonstrate that our method performs favorably compared to previous methods.
Dealing with Semantic Heterogeneity Issues on the Web

To operate effectively, the Semantic Web must be able to make explicit the semantics of Web resources via ontologies, which software agents use to automatically process these resources. The Web's natural semantic heterogeneity presents problems, however - namely, redundancy and ambiguity. The authors' ontology matching, clustering, and disambiguation techniques aim to bridge the gap between syntax and semantics for Semantic Web construction. Their approach discovers and represents the intended meaning of words in Web applications in a nonredundant way, while considering the context in which those words appear.
Ethics and Bias in Machine Learning: A Technical Study of What Makes Us “Good”

The topic of machine ethics is growing in recognition and energy, but bias in machine learning algorithms outpaces it to date. Bias is a complicated term with good and bad connotations in the field of algorithmic prediction making. Especially in circumstances with legal and ethical consequences, we must study the results of these machines to ensure fairness. This paper attempts to address ethics at the algorithmic level of autonomous machines. There is no one solution to solving machine bias, it depends on the context of the given system and the most reasonable way to avoid biased decisions while maintaining the highest algorithmic functionality. To assist in determining the best solution, we turn to machine ethics.
CapsGAN: Using Dynamic Routing for Generative Adversarial Networks

In this paper, we propose a novel technique for generating images in the 3D domain from images with high degree of geometrical transformations. By coalescing two popular concurrent methods that have seen rapid ascension to the machine learning zeitgeist in recent years: GANs (Goodfellow et al.) and Capsule networks (Sabour, Hinton et al.) - we present: CapsGAN. We show that CapsGAN performs better than or equal to traditional CNN based GANs in generating images with high geometric transformations using rotated MNIST. In the process, we also show the efficacy of using capsules architecture in the GANs domain. Furthermore, we tackle the Gordian Knot in training GANs - the performance control and training stability by experimenting with using Wasserstein distance (gradient clipping, penalty) and Spectral Normalization. The experimental findings of this paper should propel the application of capsules and GANs in the still exciting and nascent domain of 3D image generation, and plausibly video (frame) generation.
From fine- to coarse-grained dynamic information flow control and back

We show that fine-grained and coarse-grained dynamic information-flow control (IFC) systems are equally expressive. To this end, we mechanize two mostly standard languages, one with a fine-grained dynamic IFC system and the other with a coarse-grained dynamic IFC system, and prove a semantics-preserving translation from each language to the other. In addition, we derive the standard security property of non-interference of each language from that of the other, via our verified translation. This result addresses a longstanding open problem in IFC: whether coarse-grained dynamic IFC techniques are less expressive than fine-grained dynamic IFC techniques (they are not!). The translations also stand to have important implications on the usability of IFC approaches. The coarse- to fine-grained direction can be used to remove the label annotation burden that fine-grained systems impose on developers, while the fine- to coarse-grained translation shows that coarse-grained systems---which are easier to design and implement---can track information as precisely as fine-grained systems and provides an algorithm for automatically retrofitting legacy applications to run on existing coarse-grained systems.
QR code–based ECG signal encryption/decryption algorithm

The paper presents a novel and enhanced bio-signal (ECG) cryptographic technique in which a QR code has been successfully utilized as a data container. The bio-signal encryption process considers ECG samples in the form of integers in order to create corresponding binary pair sequences that are converted as equivalent ciphertext information. Subsequently, the generated ciphertexts are transformed into QR code by using a QR code generator/reader in order to ensure further security. The encryption process generates a binary key for each binary pair that will be used during signal decryption. The imperceptibility of a reconstructed ECG signal is evaluated by analyzing performance metrics in terms of PSNR and PRD values. The average PSNR values are found to be above 42 dB, whereas PRD values observed were less than 3.43 percent. It is further proved that the proposed technique can encrypt a maximum of 226 kb of diagnose data by using merely seven QR codes.
Clone-World: A visual analytic system for large scale software clones

With the era of big data approaching, the number of software systems, their dependencies, as well as the complexity of the individual system is becoming larger and more intricate. Understanding these evolving software systems is thus a primary challenge for cost-effective software management and maintenance. In this paper we perform a case study with evolving code clones. The programmers often need to manually analyze the co-evolution of clone fragments to decide about refactoring, tracking, and bug removal. However, manual analysis is time consuming, and nearly infeasible for a large number of clones, e.g., with millions of similarity pairs, where clones are evolving over hundreds of software revisions. We propose an interactive visual analytics system, Clone-World, which leverages big data visualization approach to manage code clones in large software systems. Clone-World, gives an intuitive yet powerful solution to the clone analytic problems. Clone-World combines multiple information-linked zoomable views, where users can explore and analyze clones through interactive exploration in real time. User studies and experts’ reviews suggest that Clone-World may assist developers in many real-life software development and maintenance scenarios. We believe that Clone-World will ease the management and maintenance of clones, and inspire future innovation to adapt visual analytics to manage big software systems.
Proposed Smooth-STC Algorithm for Enhanced Coverage Path Planning Performance in Mobile Robot Applications

Robotic path planning is a field of research which is gaining traction given the broad domains of interest to which path planning is an important systemic requirement. The aim of path planning is to optimise the efficacy of robotic movement in a defined operational environment. For example, robots have been employed in many domains including: Cleaning robots (such as vacuum cleaners), automated paint spraying robots, window cleaning robots, forest monitoring robots, and agricultural robots (often driven using satellite and geostationary positional satellite data). Additionally, mobile robotic systems have been utilised in disaster areas and locations hazardous to humans (such as war zones in mine clearance). The coverage path planning problem describes an approach which is designed to determine the path that traverses all points in a defined operational environment while avoiding static and dynamic (moving) obstacles. In this paper we present our proposed Smooth-STC model, the aim of the model being to identify an optimal path, avoid all obstacles, prevent (or at least minimise) backtracking, and maximise the coverage in any defined operational environment. The experimental results in a simulation show that, in uncertain environments, our proposed smooth STC method achieves an almost absolute coverage rate and demonstrates improvement when measured against alternative conventional algorithms.
Personal Data Broker Instead of Blockchain for Students’ Data Privacy Assurance

Data logs about learning activities are being recorded at a growing pace due to the adoption and evolution of educational technologies (Edtech). Data analytics has entered the field of education under the name of learning analytics. Data analytics can provide insights that can be used to enhance learning activities for educational stakeholders, as well as helping online learning applications providers to enhance their services. However, despite the goodwill in the use of Edtech, some service providers use it as a means to collect private data about the students for their own interests and benefits. This is showcased in recent cases seen in media of bad use of students’ personal information. This growth in cases is due to the recent tightening in data privacy regulations, especially in the EU. The students or their parents should be the owners of the information about them and their learning activities online. Thus they should have the right tools to control how their information is accessed and for what purposes. Currently, there is no technological solution to prevent leaks or the misuse of data about the students or their activity. It seems appropriate to try to solve it from an automation technology perspective. In this paper, we consider the use of Blockchain technologies as a possible basis for a solution to this problem. Our analysis indicates that the Blockchain is not a suitable solution. Finally, we propose a cloud-based solution with a central personal point of management that we have called Personal Data Broker.
Digital Transformation of Healthcare

The digital transformation needs to go a lot further to provide a seamless but secure and protected data interchange where a multitude of beneficial impacts can be gained such as longitudinal medical data, interoperability, secure patient centric generated data and its use cases, to name a few. Positioned with the increasing threat of data breaches, the healthcare sector has started to make deeper inroads into the beneficial uses of blockchain as the mechanism to provide immutability, audit trail, security and protect the privacy of data to ensure a better way forwards for healthcare transformation. This chapter aims to discuss the impact of digital transformation on the healthcare industry.
BlockP2P: Enabling Fast Blockchain Broadcast with Scalable Peer-to-Peer Network Topology

Blockchain technology offers an intelligent amalgamation of distributed ledger, Peer-to-Peer (P2P), cryptography, and smart contracts to enable trustworthy applications without any third parties. Existing blockchain systems have successfully either resolved the scalability issue by advancing the distributed consensus protocols from the control plane, or complemented the security issue by updating the block structure and encryption algorithms from the data plane. Yet, we argue that the underlying P2P network plane remains as an important but unaddressed barrier for accelerating the overall blockchain system performance. Our key insights from comparative assessments reveal the fact that P2P topology highly affects the broadcast speed of blockchain data, leading to poor performance and vulnerable to double spending attacks. In this paper, we introduce BlockP2P, a novel optimization design to accelerate broadcast efficiency and meanwhile retain the security. BlockP2P first operates the geographical proximity sensing clustering, which leverages K-Means algorithm for gathering proximity peer nodes into clusters. It follows by the hierarchical topological structure that ensures strong connectivity and small diameter based on node attribute classification. We finally propose the parallel spanning tree broadcast algorithm to enable fast data broadcast among nodes both in the intra- and inter- clusters. To clarify the influence of each tier, we carefully design and implement a blockchain network simulator. Evaluation results show that BlockP2P can exhibit promising performance compared to Bitcoin and Ethereum.
An improved phase-coding method for absolute phase retrieval based on the path-following algorithm

Although the traditional phase-coding method for absolute phase retrieval possesses strong robustness, the method leads to unwrapping artifacts as a mass of codewords required. To solve this problem, a novel improved method is proposed in this paper. The incorrect points in the recovered phase via the traditional method are located using a computational framework, and the path-following algorithm is adopted as an aid to correct the unwrapping artifacts. The effectiveness of the proposed method is experimentally verified by an established measurement system. The reconstructed geometry of standard sphere has sufficient accuracy and the absolute phase retrieval of a complex star object has high quality.
Mining Scholarly Publications for Scientific Knowledge Graph Construction

In this paper, we present a preliminary approach that uses a set of NLP and Deep Learning methods for extracting entities and relationships from research publications and then integrates them in a Knowledge Graph. More specifically, we (i) tackle the challenge of knowledge extraction by employing several state-of-the-art Natural Language Processing and Text Mining tools, (ii) describe an approach for integrating entities and relationships generated by these tools, and (iii) analyse an automatically generated Knowledge Graph including 10, 425 entities and 25, 655 relationships in the field of Semantic Web.
Experimental Point-to-Multipoint Plug-and-Play Measurement-Device-Independent Quantum Key Distribution Network*

Measurement-device-independent quantum key distribution (MDI-QKD) offers a practical way to realize a star-type quantum network. Previous experiments on MDI-QKD networks can only support the point-to-point communication. We experimentally demonstrate a plug-and-play MDI-QKD network which can support the point-to-multipoint communication among three users. Benefiting from the plug-and-play MDI-QKD architecture, the whole network is automatically stabilized in spectrum, polarization, arrival time, and phase reference. The users only need the encoding devices, which means that the hardware requirements are greatly reduced. Our experiment shows that it is feasible to establish a point-to-multipoint MDI-QKD network .
Position-Based Visual Servoing of a Mobile Robot with an Automatic Extrinsic Calibration Scheme

SUMMARY In this paper, we present and implement a novel approach for position-based visual servoing. The challenge of controlling the mobile robot while simultaneously estimating the camera to mobile robot transformation is solved. This is achieved using gradient descent (GD)-based estimation and the sliding-mode approach. The GD approach allows online parameter estimation for controlling the robot to achieve a desired position and orientation. The adaptive nature of the parameters demonstrates the robustness of the system. In contrast to existing work, the proposed technique achieves both estimation and control tasks in a single experiment. Simulation and experimental results are provided to validate the performance of the proposed scheme.
Synthesizing symmetric lenses

Lenses are programs that can be run both "front to back" and "back to front," allowing updates to either their source or their target data to be transferred in both directions. Since their introduction by Foster et al., lenses have been extensively studied, extended, and applied. Recent work has also demonstrated how techniques from type-directed program synthesis can be used to efficiently synthesize a simple class of lenses---so-called bijective lenses over string data---given a pair of types (regular expressions) and a small number of examples. We extend this synthesis algorithm to a much broader class of lenses, called simple symmetric lenses, including all bijective lenses, all of the popular category of "asymmetric" lenses, and a rich subset of the more powerful "symmetric lenses" proposed by Hofmann et al. Intuitively, simple symmetric lenses allow some information to be present on one side but not the other and vice versa. They are of independent theoretical interest, being the largest class of symmetric lenses that do not rely on persistent internal state. Synthesizing simple symmetric lenses is substantially more challenging than synthesizing bijective lenses: Since some of the information on each side can be "disconnected" from the other side, there will, in general, be many lenses that agree with a given example. To guide the search process, we use stochastic regular expressions and ideas from information theory to estimate the amount of information propagated by a candidate lens, generally preferring lenses that propagate more information, as well as user annotations marking parts of the source and target data structures as either irrelevant or essential. We describe an implementation of simple symmetric lenses and our synthesis procedure as extensions to the Boomerang language. We evaluate its performance on 48 benchmark examples drawn from Flash Fill, Augeas, the bidirectional programming literature, and electronic file format synchronization tasks. Our implementation can synthesize each of these lenses in under 30 seconds.
Foundational principles &amp; technologies for the metaverse

Science-fiction notions of "the metaverse" are slowly becoming a reality as products such as Fortnite, Minecraft, and Roblox bring immersive social experiences to hundreds of millions of people and blur the boundaries between games and social networks. This talk will explore the foundational technologies, economies, and freedoms required to build this future medium as a force for good.