Reviewerly/exHarmony huggingface dataset:
SYSTEMATIC DEFINITION OF COMPLEXITY ASSEMBLY IN FRACTAL POROUS MEDIA

Microstructures dominate the physical properties of fractal porous media, which means the clarification of complexity types and their assembly are of fundamental importance for static or dynamic purposes. In this work, we identified fractal porous media to dual-complexity systems composed of stationary and scale-invariant complexities as per fractal topography theory, and proposed an open mathematical framework to characterize complexity assembly in microstructures, realized the original complexity, such as random, multi-phase, and multi-type features by the quartet structure generation set (QSGS) algorithm, and unified the behavioral complexity, including the self-similar and self-affine properties by fractal topography model. For demonstration, the control mechanisms on the microstructures from different complexities are discussed, with their physical implications and relations to the physical properties of porous media clarified in principle. The results indicate that our framework is open to arbitrary original and behavioral complexities, and eases the modeling of multi-scale microstructures and the property estimation significantly.
R2DE

The main objective of exams consists in performing an assessment of students' expertise on a specific subject. Such expertise, also referred to as skill or knowledge level, can then be leveraged in different ways (e.g., to assign a grade to the students, to understand whether a student might need some support, etc.). Similarly, the questions appearing in the exams have to be assessed in some way before being used to evaluate students. Standard approaches to questions' assessment are either subjective (e.g., assessment by human experts) or introduce a long delay in the process of question generation (e.g., pretesting with real students). In this work we introduce R2DE (which is a Regressor for Difficulty and Discrimination Estimation), a model capable of assessing newly generated multiple-choice questions by looking at the text of the question and the text of the possible choices. In particular, it can estimate the difficulty and the discrimination of each question, as they are defined in Item Response Theory. We also present the results of extensive experiments we carried out on a real world large scale dataset coming from an e-learning platform, showing that our model can be used to perform an initial assessment of newly created questions and ease some of the problems that arise in question generation.
Automatic Localization and Segmentation of the Ventricles in Magnetic Resonance Images

Automatic localization and segmentation of the ventricles in magnetic resonance (MR) images is important for clinical applications. In this paper, we propose a new ventricle localization method based on slope difference distribution (SDD) clustering and threshold selection to calculate the positions of the left ventricle (LV) and the right ventricle (RV) automatically. Then, we utilize the SDD threshold selection to segment the LV and the RV in the automatically localized region of interest (ROI). To calculate the optimal threshold, we propose a coarse-to-fine SDD threshold selection method. Experimental results verified that both the proposed automatic ventricle localization method and the proposed coarse-to-fine SDD threshold selection method are more robust than state of the art methods.
Improving Fashion Landmark Detection by Dual Attention Feature Enhancement

Fashion landmark detection is a fundamental problem in visual fashion analyze, which aims at locating the precise coordinates of functional key points defined on clothes. Dozens of deep learning-based methods are proposed to address this problem. How to extract adequate and effective features is a critical point for this challenging task. In this paper, we propose the Dual Attention Feature Enhancement(DAFE) module, which strengthens the extracted features by adaptively reusing low-level image details and emphasizing informative parts. First, DAFE enhances the pixel-wise information through capturing the spatial details from low-level features by the guidance of attention matrix, which is generated from high-level ones. Second, DAFE emphasizes task-related features by modeling long-range relationships between channels. Experimental experiments on Deepfashion and FLD datasets demonstrate that our method achieves state-of-the-art performance, and our approach also achieves competitive results on Deepfashion2 Landmark Estimation Challenge.
Ethereum smart contracts: Analysis and statistics of their source code and opcodes

Smart contracts are programs that are used for verifying and enforcing the terms of an agreement. Ethereum is an extensively used platform that can be used to execute smart contracts. These can be defined by using high-level languages such as Solidity, Vyper and Bamboo that are compiled into low-level machine instructions, i.e., bytecode. These instructions define a Turing complete language and are often represented by using a readable format that is referred to as opcodes. Here, we present a novel, more comprehensive, study that aims at gaining a precise understanding on how programmers use the linguistic instructions supported by Ethereum. More precisely, the contracts’ source code has been also analysed, in order to understand the high-level control structures and core instructions that are used for smart contract definitions, and how they are reflected on the opcode level. We have gathered ten of thousands of verified Ethereum smart contracts that have been written by using the Solidity language. This study can lay the groundwork for defining new formalisms and new domain specific languages (DSL). These can support the users in the development of decentralised applications (DAPPs).
Detection of Encrypted Malicious Network Traffic using Machine Learning

The proliferation of encrypted network traffic necessitates an innovative machine learning traffic analysis approach which does not rely on pattern matching or the payload content of the packets to detect malicious/suspicious communications. Encryption of Internet traffic has increasingly become a typical best practice, making network packet content analysis yield diminishing returns. A majority of internet traffic is now protected using the cryptographic protocol known as Transport Layer Security (TLS). Malware authors have also followed this trend with the use of TLS to hide malicious network communications. We propose a malicious communication detection mechanism using a Support Vector Machine (SVM) and an alternative with a Convolutional Neural Network (CNN). Both methods achieve respectable results and a low False Positive Rate (FPR). However, the SVM method outperforms the CNN method in all evaluation metrics presented. Lastly, we propose future work to experiment with transport layer size and direction as features and automate feature engineering by using raw packet traffic with a CNN augmented with a Long Short-term Memory (LSTM) for detection of malicious traffic.
A Traffic Surveillance Multi-Scale Vehicle Detection Object Method Base on Encoder-Decoder

Aiming at the problem that it is difficult for traffic monitoring videos to detect multi-scale vehicle targets, especially small vehicle targets in complex scenarios, a codec-based vehicle detection algorithm is proposed.This algorithm is based on YOLOv3.In order to solve the multi-scale vehicle target detection problem, a new multi-level feature pyramid structure added with the codec module to detect vehicle targets of different scales.The experimental results on the KITTI dataset and UA-DETRAC dataset confirm that the algorithm in this paper has achieved good detection results for vehicle targets in various environments and at various scales in the surveillance video, especially for small vehicle targets, which can better meet the actual application demand.
CirclePIN

In the last months, the market for personal wearable devices has been booming significantly, and, in particular, smartwatches are starting to assume a fundamental role in the Bring Your Own Device (BYOD) arena as well as in the more general Internet of Things (IoT) ecosystem, by acting both as sensitive data sources and as user identity proxies. These new roles, complementing the more traditional personal assistance and telemetry/tracking ones, open new perspectives in their integration in complex IoT-based critical infrastructures such as e-payment, health care monitoring, and emergency systems, as well as in their usage as remote control facilities in smart services. Users can access their IoT devices at any time from any place through smartwatches. We argue that this new scenario calls for a strengthened and more resilient authentication of users on these devices, despite their limitations in terms of dimensions and hardware constraints that may considerably affect the usability of security mechanisms. In this article, we present an innovative authentication scheme targeted at smartwatches, namely CirclePIN, that provides both resilience to most common attacks and a high level of usability in tests with real users.
User-centred design and evaluation of a tele-operated echocardiography robot

Abstract We present the collected findings of a user-centred approach for developing a tele-operated robot for remote echocardiography examinations. During the three-year development of the robot, we involved users in all development stages of the robot, to increase the usability of the system for the doctors. For requirement compilation, we conducted a literature review, observed two traditional examinations, arranged focus groups with doctors and patients, and conducted two online surveys. During the development of the robot, we regularly involved doctors in usability tests to receive feedback from them on the user interface for the robot and on the robot’s hardware. For evaluation of the robot, we conducted two eye tracking studies. In the first study, doctors executed a traditional echocardiography examination. In the second study, the doctors conducted a remote examination with our robot. The results of the studies show that all doctors were able to successfully complete a correct ultrasonography examination with the tele-operated robot. In comparison to a traditional examination, the doctors on average only need a short amount of additional time to successfully examine a patient when using our remote echocardiography robot. The results also show that the doctors fixate considerably more often, but with shorter fixation times, on the USG screen in the traditional examination compared to the remote examination. We found further that some of the user-centred design methods we applied had to be adjusted to the clinical context and the hectic schedule of the doctors. Overall, our experience and results suggest that the usage of user-centred design methodology is well suited for developing medical robots and leads to a usable product that meets the end users’ needs.
Understanding security in the government's use of blockchain technology with value focused thinking approach

Purpose The purpose of the research is to assess security concerns in government's use of Blockchain technology through the lens of value focused thinking (VFT) approach. Design/methodology/approach Qualitative methods such as open ended questions and interviews were used to gather data and the data was analysed in a structured approach to formulate a means-ends network of objectives. The data were collected from eight interviews of selected participants. Findings The findings are 35 objectives of which ten are fundamental objectives related to the values of identity, trust, data privacy, transparency, integrity, public service delivery, cost, availability of public information, responsibility and usability. Practical implications The study implies that governments or private organisations building blockchain solutions for governments can benefit from insights on values to focus on in blockchain security; and the current research serves as a base to understand more on blockchain use in the governments as well as security values and application of VFT approach for future academic researchers in this area. Social implications It is a need-driven approach in which blockchain is assessed to ensure its fit to societal needs and public values. Originality/value It is first of its kind in studying security in blockchain use by government through the lens of VFT approach, and it provides insights of values that are of importance to further blockchain use in the government.
Comparison of Building Design Assessment Behaviors of Novices in Augmented- and Virtual-Reality Environments

Design and construction professionals must make well-informed decisions for every project that meets both industry standards and building codes and also the specific needs of building users and clients. In order to make effective decisions, research suggests that explicit knowledge, defined as easily codified and communicated information, and tacit knowledge, considered to be the know-how of completing a task, must be effectively applied. While there is recognition of the need for both forms of knowledge, architecture engineering and construction (AEC) education has historically focused on covering content-related explicit knowledge in the classroom. As a result, students generally develop tacit knowledge over their careers. Due to an aging AEC workforce, there is a need to support tacit knowledge development in the classroom to enable students entering the industry to supplement the collective tacit knowledge that will exit the industry as the current generation of practitioners retires. Therefore, the authors of this paper explore the use of augmented reality (AR) and virtual reality (VR) to provide immersive virtual experiences aimed at replicating the types of scenarios that students might experience in their careers that would require them to apply tacit knowledge. The authors tasked students in construction-related disciplines with assessing a building design and making judgments about how the design should be modified to support an occupant in a wheelchair in both VR and AR. Using two similar models and a counterbalanced research methodology, the authors coded the statements and behaviors of the student participants during this design assessment exercise. The results of this work indicate that both technologies elicited statements that were indicative of explicit knowledge related to the needs of a wheelchair-bound occupant. When AR and VR were found to directly encourage physical exploration in the experience, both led to behaviors that simulated the completion of tasks that might be performed by a wheelchair-bound occupant. These behaviors were frequently followed by comments that were indicative of tacit knowledge. While this type of behavior was observed in both AR and VR, AR seemed to more directly encourage this type of interaction among participants. The contribution of this work is in providing observational evidence to demonstrate how the physical exploration affordances of AR and VR may be able to support experiences that foster the use and development of tacit knowledge related to AEC-related decision-making.
A Comparative Evaluation of Local Feature Descriptors for DeepFakes Detection

The global proliferation of affordable photographing devices and readily-available face image and video editing software has caused a remarkable rise in face manipulations, e.g., altering face skin color using FaceApp. Such synthetic manipulations are becoming a very perilous problem, as altered faces not only can fool human experts but also have detrimental consequences on automated face identification systems (AFIS). Thus, it is vital to formulate techniques to improve the robustness of AFIS against digital face manipulations. The most prominent countermeasure is face manipulation detection, which aims at discriminating genuine samples from manipulated ones. Over the years, analysis of microtextural features using local image descriptors has been successfully used in various applications owing to their flexibility, computational simplicity, and performances. Therefore, in this paper, we study the possibility of identifying manipulated faces via local feature descriptors. The comparative experimental investigation of ten local feature descriptors on a new and publicly available DeepfakeTIMIT database is reported.
A Multi-Level Attention Model for Remote Sensing Image Captions

The task of image captioning involves the generation of a sentence that can describe an image appropriately, which is the intersection of computer vision and natural language. Although the research on remote sensing image captions has just started, it has great significance. The attention mechanism is inspired by the way humans think, which is widely used in remote sensing image caption tasks. However, the attention mechanism currently used in this task is mainly aimed at images, which is too simple to express such a complex task well. Therefore, in this paper, we propose a multi-level attention model, which is a closer imitation of attention mechanisms of human beings. This model contains three attention structures, which represent the attention to different areas of the image, the attention to different words, and the attention to vision and semantics. Experiments show that our model has achieved better results than before, which is currently state-of-the-art. In addition, the existing datasets for remote sensing image captioning contain a large number of errors. Therefore, in this paper, a lot of work has been done to modify the existing datasets in order to promote the research of remote sensing image captioning.
Multimodal Gaze Interaction for Creative Design

We present a new application ("Sakura") that enables people with physical impairments to produce creative visual design work using a multimodal gaze approach. The system integrates multiple features tailored for gaze interaction including the selection of design artefacts via a novel grid approach, control methods for manipulating canvas objects, creative typography, a new color selection approach, and a customizable guide technique facilitating the alignment of design elements. A user evaluation (N=24) found that non-disabled users were able to utilize the application to complete common design activities and that they rated the system positively in terms of usability. A follow-up study with physically impaired participants (N=6) demonstrated they were able to control the system when working towards a website design, rating the application as having a good level of usability. Our research highlights new directions in making creative activities more accessible for people with physical impairments.
Adaptive motion estimation and sequential outline separation based moving object detection in video surveillance system

Moving object detection is an important task in the basic research field of automated video surveillance systems in computer vision and video processing. The application of moving object tracking is critical for military, surveillance systems and operational robot applications, and getting more critical day by day. Closer view gap, ghosting, and sudden lighting changes have been the primary issues in moving object detection in existing methods. To think about the above issues, this work proposes two methodologies like consolidating background subtraction and improved sequential outline separation strategies for the recognition of various moving objects from indoor and outdoor genuine video dataset. This kind of framework can be realized in the general public places such as shopping malls, air terminals and railway stations or the safety of any private premises is the primary concern. This work precisely distinguishes the moving objects with shifting object size and number in various complex situations. The simulation work is done with MATLAB software, to measure the detection error and processing time of the proposed strategy. The proposed sequential outline separation method starts with background subtraction and foreground detection for motion and object discovery and it is a procedure of separating the territory of enthusiasm from developed background. Simulation results and error rate investigation demonstrate that our proposed strategies identify the moving targets productively. As Compared to other conventional systems, our proposed adaptive motion estimation and sequential outline separation method performs better by achieving an accuracy of 97.45%, a sensitivity of 94.2%, and a specificity of 97.72%.
Sequence Data Matching and Beyond: New Privacy-Preserving Primitives Based on Bloom Filters

Bloom filter encoding has widely been used as an efficient masking technique for privacy-preserving matching functions. The existing matching techniques, however, are limited to relatively simple types such as string, categorical and signal numerical values. In this paper, we propose a new scheme that significantly extends the class of matching primitives that are based on privacy-preserving Bloom filter mechanism. These primitives include sequence data matching and popular distance-based machine learning algorithms such as KNN and SVM. Our scheme hash-maps a sequence data vector into the Bloom filter space while checking the similarity of the data points efficiently with negligible utility loss by adding a timestamp (bit) for each element in the data represented with its neighboring values. Furthermore, it includes a Laplace-like perturbation method on the constructed Bloom filters to address the weakness of deterministic probability led by encoding techniques. As a result, the proposed work guarantee the private data records are difficult to be discriminated due to collisions and differential privacy. The experimental results on three real-scenario based datasets illustrate that our method can achieve a significantly better trade-off between utility and privacy than the state-of-the-art differential privacy-based method by adding Laplace noise to the data directly.
Assumptions Checked

Users of voice assistants often report that they fall into patterns of using their device for a limited set of interactions, like checking the weather and setting alarms. However, it's not clear if limited use is, in part, due to lack of learning about the device's functionality. We recruited 10 diverse families to participate in a one-month deployment study of the Echo Dot, enabling us to investigate: 1) which features families are aware of and engage with, and 2) how families explore, discover, and learn to use the Echo Dot. Through audio recordings of families' interactions with the device and pre- and post-deployment interviews, we find that families' breadth of use decreases steadily over time and that families learn about functionality through trial and error, asking the Echo Dot about itself, and through outside influencers such as friends and family. Formal outside learning influencers, such as manufacturer emails, are less influential. Drawing from diffusion of innovation theory, we describe how a home-based voice interface might be positioned as a near-peer to the user, and that by describing its own functionality using just-in-time learning, the home-based voice interface becomes a trustworthy learning influencer from which users can discover new functionalities.