Reviewerly/exHarmony huggingface dataset:
Detection, Tracking and Classification of Aircraft and Drones in Digital Towers Using Machine Learning on Motion Patterns

Remote digital towers using high-resolution cameras that cover a 360-degree view of airports have recently been a solution to some airports in replacing physical towers and in introducing digital technologies as tools to assist tower controllers. We propose the use of machine learning techniques to automatically detect and track small moving objects in the airfield from their motion patterns, i.e. the ways an object moves. A video dataset comprising aircraft in an airfield and drones captured from a fixed angle is constructed. To the dataset, we apply Harris Detection and Convolutional Neural Network followed by Optical Flow to locate and track very small moving objects in the wide-area scene. Motion-based features are extracted from their trajectories after which a K-Nearest Neighbor classifier is applied to classify objects into drones or aircraft. Our tests show that the accuracy and execution time are appropriate for real-time operation.
Do You Have to Pee?

The management of bodily excretion is an everyday biological function necessary for our physiological and psychological well-being. In this paper, I investigate interaction design opportunities for and implications of leveraging intimate and somatic data to manage urination. This is done by detailing a design space that includes (1) a critique of market exemplars, (2) three conceptual design provocations, and (3) autobiographical data-gathering and labeling from excretion routines. To conclude, considerations within the labeling of somatic data, the actuating of bodily experiences, and the scaling of intimate interactions are contributed for designers who develop data-driven technology for intimate and somatic settings.
Sequential Time-Optimal Path-Tracking Algorithm for Robots

This paper focuses on minimum-time path tracking, a subproblem in motion planning of robotic systems. We generate a time-optimal velocity profile for robotic manipulators taking into account kinematic and dynamic constraints. Based on the special structure of the constraints (called peaked constraints), profile generation is formulated as a linear programming (LP) problem. The LP-based control problem is solved by a sequential optimization method. The presented algorithm has reduced computational time compared to a general LP solver.
Towards Secure and Efficient Outsourcing of Machine Learning Classification

Machine learning classification has been successfully applied in numerous applications, such as healthcare, finance, and more. Outsourcing classification services to the cloud has become an intriguing practice as this brings many prominent benefits like ease of management and scalability. Such outsourcing, however, raises critical privacy concerns to both the machine learning model provider and the client interested in using the classification service. In this paper, we focus on classification outsourcing with decision trees, one of the most popular classifiers. We propose for the first time a secure framework allowing decision tree based classification outsourcing while maintaining the confidentiality of the provider’s model (parameters) and the client’s input feature vector. Our framework requires no interaction from the provider and the client—they can go offline after the initial submission of their respective encrypted inputs to the cloud. This is a distinct advantage over prior art for practical deployment, as they all work under the client-provider setting where synchronous online interactions between the provider and client is required. Leveraging the lightweight additive secret sharing technique, we build our protocol from the ground up to enable secure and efficient outsourcing of decision tree evaluation, tailored to address the challenges posed by secure in-the-cloud dealing with versatile components including input feature selection, decision node evaluation, path evaluation, and classification generation. Through evaluation we show the practical performance of our design, and the substantial client-side savings over prior art, say up to four orders of magnitude in computation and 163 $$\times $$ in communication.
Flow Fields: Dense Correspondence Fields for Highly Accurate Large Displacement Optical Flow Estimation

Modern large displacement optical flow algorithms usually use an initialization by either sparse descriptor matching techniques or dense approximate nearest neighbor fields. While the latter have the advantage of being dense, they have the major disadvantage of being very outlier-prone as they are not designed to find the optical flow, but the visually most similar correspondence. In this article we present a dense correspondence field approach that is much less outlier-prone and thus much better suited for optical flow estimation than approximate nearest neighbor fields. Our approach does not require explicit regularization, smoothing (like median filtering) or a new data term. Instead we solely rely on patch matching techniques and a novel multi-scale matching strategy. We also present enhancements for outlier filtering. We show that our approach is better suited for large displacement optical flow estimation than modern descriptor matching techniques. We do so by initializing EpicFlow with our approach instead of their originally used state-of-the-art descriptor matching technique. We significantly outperform the original EpicFlow on MPI-Sintel, KITTI 2012, KITTI 2015 and Middlebury. In this extended article of our former conference publication we further improve our approach in matching accuracy as well as runtime and present more experiments and insights.
Rational Isogenies from Irrational Endomorphisms

In this paper, we introduce a polynomial-time algorithm to compute a connecting $$\mathcal {O}$$ -ideal between two supersingular elliptic curves over $$\mathbb {F}_p$$ with common $$\mathbb {F}_p$$ -endomorphism ring $$\mathcal {O}$$ , given a description of their full endomorphism rings. This algorithm provides a reduction of the security of the CSIDH cryptosystem to the problem of computing endomorphism rings of supersingular elliptic curves. A similar reduction for SIDH appeared at Asiacrypt 2016, but relies on totally different techniques. Furthermore, we also show that any supersingular elliptic curve constructed using the complex-multiplication method can be located precisely in the supersingular isogeny graph by explicitly deriving a path to a known base curve. This result prohibits the use of such curves as a building block for a hash function into the supersingular isogeny graph.
On the Effect of Low-Frequency Terms on Neural-IR Models

Low-frequency terms are a recurring challenge for information retrieval models, especially neural IR frameworks struggle with adequately capturing infrequently observed words. While these terms are often removed from neural models - mainly as a concession to efficiency demands - they traditionally play an important role in the performance of IR models. In this paper, we analyze the effects of low-frequency terms on the performance and robustness of neural IR models. We conduct controlled experiments on three recent neural IR models, trained on a large-scale passage retrieval collection. We evaluate the neural IR models with various vocabulary sizes for their respective word embeddings, considering different levels of constraints on the available GPU memory. We observe that despite the significant benefits of using larger vocabularies, the performance gap between the vocabularies can be, to a great extent, mitigated by extensive tuning of a related parameter: the number of documents to re-rank. We further investigate the use of subword-token embedding models, and in particular FastText, for neural IR models. Our experiments show that using FastText brings slight improvements to the overall performance of the neural IR models in comparison to models trained on the full vocabulary, while the improvement becomes much more pronounced for queries containing low-frequency terms.
An Image Authentication Scheme Using Merkle Tree Mechanisms

Research on digital image processing has become quite popular and rapid in recent years, and scholars have proposed various image verification mechanisms. Similarly , blockchain technology has also become very popular in recent years. This paper proposes a new image verification mechanism based on the Merkle tree technique in the blockchain. The Merkle tree root in the blockchain mechanism provides a reliable environment for storage of image features. In image verification, the verification of each image can be performed by the Merkle tree mechanism to obtain the hash value of the Merkle tree node on the path. In addition, the method combines the Inter-Planetary File System (IPFS) to improve the availability of images. The main purpose of this paper is to achieve the goal of image integrity verification. The proposed method can not only verify the integrity of the image but also restore the tampered area in the case of image tampering. Since the proposed method employs the blockchain mechanism, the image verification mechanism does not need third party resources . The verification method is performed by each node in the blockchain network. The experimental results demonstrate that the proposed method successfully achieved the goal of image authentication and tampered area restoration.
The Risks and Challenges of Implementing Ethereum Smart Contracts

Smart contracts are designed to facilitate the performance of trackable and irreversible transactions without the need for third party involvement. Therefore, as a result of this lack of oversight, it is essential that these smart contracts are written and properly tested. In this paper, we examine some of the prominent risks and challenges involved with writing and implementing smart contracts and discuss how each of these challenges can be overcome. We focus on contracts executed on Ethereum, the most prominent smart contract platform.
LU-NET: An Improved U-Net for Ventricular Segmentation

In order to solve the problem of low accuracy of the U-Net in cardiac ventricular segmentation, we propose an improved U-net named LU-Net by the following three methods. First, in order to improve the efficiency and effectiveness of extracting the features of the original image, we combine U-net with SE-Net model. This model reweights the channels of the feature map, which can give higher weight to the useful information and lower weight to the invalid information. Second, in order to alleviate the extent of losing the pixel-location information when using the encoder to dawn sample, we combine multi-scale input with U-net’s encoder. Third, in order to solve the problem of low accuracy in traditional U-net, we replace the transposed convolution layer, used by the traditional U-Net’s encoder during upsampling, with an unsampling layer. During the process of unsampling, it can put pixels to their original location using the pixel-location information reserved by the encoder during the sampling process, which can reduce errors caused by losing pixel-location information. Besides, using the unsampling layer during unsampling can also avoid producing checkerboard artifacts during transposed convolution and improve the segmentation accuracy. To verify the effectiveness of LU-Net, we apply it to the ACDC Stacom 2017 dataset. The experimental results show that the evaluation criteria of prediction results are 92.4%, 86.4%, and 92.5% on Dice coefficient, Jaccard similarity coefficient, and F1-ccore respectively, which are better than U-Net, SegNet, and IU-Net and remarkably better than the traditional neural convolution network model, FCN8s.
Language-aware weak supervision for salient object detection

Natural Language Processing has achieved remarkable performance in multitudinous computer tasks, but the potential capability of textual information has not been completely explored in visual saliency detection. In this paper, we learn to detect salient object from natural language by addressing the two essential issues: finding a semantic content matching the corresponding linguistic concept and recovering fine details without any pixel-level annotations. We first propose the Feature Matching Network (FMN) to explore the internal relation between the linguistic concept and visual image in the semantic space. The FMN simultaneously establishes the textual-visual pairwise affinities and generates a language-aware coarse saliency map. to refine the coarse map, the Recurrent Fine-tune Network (RFN) is proposed to enhance its predicted performance progressively by self-supervision. Our approach only leverages the caption to provide important cues of salient object, but generates a fine-detailed foreground map at a detecting speed of 72 FPS without any post-processing. Extensive experiments demonstrate that our method takes full advantage of textual information of natural language in saliency detection, and performs favorably against state-of-the-art approaches on the most existing datasets.
A Platform-independent, Generic-purpose, and Blockchain-based Supply Chain Tracking

Supply Chain Tracking (SCT) is considered as a major challenge for stakeholders of heterogeneous production, processing, transporting, storing, and selling systems. Studies on solutions in SCT reveal that many centralized SCT applications lack a design, which can support or even enable main features of a reliable, transparent, and publicly accessible SCT system for end users. This work demonstrates the implementation of a SCT application which, employs SC on the Ethereum blockchain (BC). This Decentralized Application (Dapp) provides a hardware-and platform-independent approach that flexibly enables multiple object combinations and transformations to be tracked with a use case-agnostic design and utilization.
The Changing Landscape: High-Level Influences on Eye Movement Guidance in Scenes

The use of eye movements to explore scene processing has exploded over the last decade. Eye movements provide distinct advantages when examining scene processing because they are both fast and spatially measurable. By using eye movements, researchers have investigated many questions about scene processing. Our review will focus on research performed in the last decade examining: (1) attention and eye movements; (2) where you look; (3) influence of task; (4) memory and scene representations; and (5) dynamic scenes and eye movements. Although typically addressed as separate issues, we argue that these distinctions are now holding back research progress. Instead, it is time to examine the intersections of these seemingly separate influences and examine the intersectionality of how these influences interact to more completely understand what eye movements can tell us about scene processing.
An ubiquitous smart guitar system for collaborative musical practice

Ubiquitous music (UbiMus) proposes to study how social interaction with mobile and distributed technologies can converge to form novel creativity support tools and music artistic practices.A recent field overlapping with UbiMus is the Internet of Musical Things, which refers to ecosystems of interconnected embedded computers (Musical Things) enabling users to produce, interact with or experience musical content.Musical Things embed electronics, sensors, data forwarding and processing software into physical or virtual objects.Smart musical instruments (SMIs) are an emerging class of Musical Things provided with capabilities of capturing and receiving data supporting instrumental musical practice.Due to their portability and self-containdeness, SMIs enable novel ubiquitous interactions between performers of acoustic and digital musical instruments.After a review of current trends in SMI research, we propose an ubiquitous smart guitar system which uses the guitar as a hub for collaborative music making.We then present a survey conducted with 18 performers to assess the usability, creativity support and engagement with the system.Results show a positive emotional engagement with the system which overall was found easy to use and novel.We also discuss several barriers to creative interaction related to the size of the user interface, creative agency and personalisation.
Privacy Provision in Collaborative Ehealth With Attribute-Based Encryption: Survey, Challenges and Future Directions

Collaborative eHealth that enables the collection and sharing of patients' data is eliminating the location and accessibility barriers in healthcare service delivery. With advances in technology, patients' data can be collected from anywhere, at any time, stored in a central location, and shared across multiple service providers to improve the quality of healthcare service delivery. Maintaining patients' data privacy during the storage and sharing process is critical for collaborative eHealth success. The attribute-based encryption, which has shown potential in data privacy protection in the cloud, has the necessary ingredients to be used in guarding against privacy violations in collaborative eHealth. In this paper, we survey the different attribute-based encryption schemes for usage in collaborative eHealth. We discuss some challenges associated with the use of attribute-based encryption schemes in collaborative eHealth and point out some future research directions. Also, we perform a comparative analysis of the surveyed schemes in terms of security, revocation ability, and efficiency.
Impact of Capacity Building and Managerial Support on Employees’ Performance: The Moderating Role of Employees’ Retention

The purpose of this study is to investigate the effects of capacity building and managers’ support on employee performance in the textile industry. Moreover, this article also investigates the moderating effect exerted by employee retention on the effects of capacity building and managers’ support on employee performance. Data were collected through a convenience sampling technique. A self-administered questionnaire survey was conducted for data collection. Two hundred copies of questionnaires were distributed, and data were collected from the lower to middle-level employees who are working in the textile sector of Pakistan. SPSS 23 and SmartPLS-3 software were used for analysis. The results indicate that capacity building has a positive and significant impact on employee performance. In contrast, managerial support has an insignificant impact on employees’ performance. However, the impact of capacity building and managerial support on employee performance is positive and significantly moderated by employee retention. The results of this study will motivate owners/policymakers to invest in capacity building to enhance their organizational performance/productivity.
FCMDT: A novel fuzzy cognitive maps dynamic trust model for cloud federated identity management

Efficient identity management system has become one of the fundamental requirements for ensuring safe, secure and transparent use of cloud services. In such a borderless environment, entities belonging to different network domains need to cooperate dynamically with each other by exchanging and sharing a significant amount of personal information in a scalable, effective and seamless manner. The traditional approach to address this challenge has been identity federation, aiming to simplify the user experience by aggregating distributed rights and permissions. However, the current federated identity management solutions are missing mechanisms to achieve agile and dynamic trust management, which remains one of the biggest obstacles to their wide adoption in cloud computing. In this paper, we aim to address this issue by introducing a novel dynamic trust model for Federated Identity Management. The proposed model relies on fuzzy cognitive maps for modeling and evaluating trust relationships between the involved entities in federated identity management systems. This trust mechanism facilitates the creation of trust relationships between prior unknown entities in a secure and dynamic way and makes Federated Identity Management systems more scalable and flexible to deploy and maintain in cloud computing environments. In addition, we propose a set of trust features for Federated Identity Management, which serves as a basis for modeling and quantifying the trust level of unknown entities. The effectiveness of the proposed trust model is proven through performance analysis and experimental results.
Navigation Comparison between a Real and a Virtual Museum: Time-dependent Differences using a Head Mounted Display

Abstract The validity of environmental simulations depends on their capacity to replicate responses produced in physical environments. However, very few studies validate navigation differences in immersive virtual environments, even though these can radically condition space perception and therefore alter the various evoked responses. The objective of this paper is to validate environmental simulations using 3D environments and head-mounted display devices, at behavioural level through navigation. A comparison is undertaken between the free exploration of an art exhibition in a physical museum and a simulation of the same experience. As a first perception validation, the virtual museum shows a high degree of presence. Movement patterns in both ‘museums’ show close similarities, and present significant differences at the beginning of the exploration in terms of the percentage of area explored and the time taken to undertake the tours. Therefore, the results show there are significant time-dependent differences in navigation patterns during the first 2 minutes of the tours. Subsequently, there are no significant differences in navigation in physical and virtual museums. These findings support the use of immersive virtual environments as empirical tools in human behavioural research at navigation level. Research highlights The latest generation HMDs show a high degree of presence. There are significant differences in navigation patterns during the first 2 minutes of a tour. Adaptation time need to be considered in future research. Training rooms need to be realistic, to avoid the ‘wow’ effect in the main experiment. Results support the use of Virtual Reality and the latest HMDs as empirical tools in human behavioural research at navigation level.
Modeling of Bitcoin's Blockchain Delivery Network

In this paper, we provide a comprehensive analytical model for Bitcoin's blockchain distribution network. Components of the model are derived from recent measurements and business analysis reports. We model the data distribution algorithm using branching processes in the network with random distribution of node connectivity. Then, we apply Jackson network model to the entire network in which individual nodes operate as priority M/G/1 queuing systems. Data arrival to the nodes is modeled as a non-homogeneous Poisson process where the distribution of arrival rate to the nodes is derived from the analytical model of data delivery protocol. Within performance results, we present probability distributions of block and transaction distribution time, node response time, forking probabilities, network partition sizes, and duration of ledger's inconsistency period.