Reviewerly/exHarmony huggingface dataset:
A Generic Multimodels-Based Approach for the Analysis of Usability and Security of Authentication Mechanisms

Authentication is a security function, added on top of an interactive system, whose role is to reduce organizations and users’ risks to grant access to sensitive data or critical resources to unauthorized users. Such a security function interfere with users’ goals and tasks by adding articulatory activities, which affect each dimension of usability. In order to mitigate their negative effect on usability, security functions must be designed following a User Centered Approach. In order to ensure their efficiency in terms of security, security processes have to be followed. With this respect, this paper focuses on the representation of user tasks (using task modelling techniques) to be performed during authentication. For security aspects, we propose the use of an approach called “attack trees” which represents threats and their effect. To integrate both aspects in a single framework, we propose an extended task modelling technique that is able to represent explicitly security threats and their potential effect together with users’ tasks performed during authentication. We show how such models can be used to compare the usability and the security of different authentication mechanisms and to make explicit conflicts between these properties. We exemplify the use of the approach on two sophisticated authentication mechanisms demonstrating its applicability and its usefulness for representing and assessing in a single framework, usability and security of these security mechanism.
CP-ABE for Circuits (and More) in the Symmetric Key Setting

The celebrated work of Gorbunov, Vaikuntanathan and Wee [GVW13] provided the first key policy attribute based encryption scheme (ABE) for circuits from the Learning With Errors (LWE) assumption. However, the arguably more natural ciphertext policy variant has remained elusive, and is a central primitive not yet known from LWE. In this work, we construct the first symmetric key ciphertext policy attribute based encryption scheme (CP-ABE) for all polynomial sized circuits from the learning with errors (LWE) assumption. In more detail, the ciphertext for a message m is labelled with an access control policy f, secret keys are labelled with public attributes $$\mathbf {x}$$ from the domain of f and decryption succeeds to yield the hidden message m if and only if $$f(\mathbf {x})=1$$ . The size of our public and secret key do not depend on the size of the circuits supported by the scheme – this enables our construction to support circuits of unbounded size (but bounded depth). Our construction is secure against collusions of unbounded size. We note that current best CP-ABE schemes [BSW07, Wat11, LOS+10, OT10, LW12, RW13, Att14, Wee14, AHY15, CGW15, AC17, KW19] rely on pairings and only support circuits in the class $${\mathsf {NC}}_1$$ (albeit in the public key setting). We adapt our construction to the public key setting for the case of bounded size circuits. The size of the ciphertext and secret key as well as running time of encryption, key generation and decryption satisfy the efficiency properties desired from CP-ABE, assuming that all algorithms have RAM access to the public key. However, the running time of the setup algorithm and size of the public key depends on the circuit size bound, restricting the construction to support circuits of a-priori bounded size. We remark that the inefficiency of setup is somewhat mitigated by the fact that setup must only be run once. We generalize our construction to consider attribute and function hiding. The compiler of lockable obfuscation upgrades any attribute based encryption scheme to predicate encryption, i.e. with attribute hiding [GKW17, WZ17]. Since lockable obfuscation can be constructed from LWE, we achieve ciphertext policy predicate encryption immediately. For function privacy, we show that the most natural notion of function hiding ABE for circuits, even in the symmetric key setting, is sufficient to imply indistinguishability obfuscation. We define a suitable weakening of function hiding to sidestep the implication and provide a construction to achieve this notion for both the key policy and ciphertext policy case. Previously, the largest function class for which function private predicate encryption (supporting unbounded keys) could be achieved was inner product zero testing, by Shen, Shi and Waters [SSW09].
Knowledge Graphs for Public Service Description: Τhe Case of Getting a Passport in Greece

An important part of electronic Government is the provision of high quality Public Services (PS) to citizens. Towards this goal, the European Commission has proposed the Core Public Service Vocabulary (CPSV), as a PS data model to be used across the public sector. CPSV is adequate for use in the case of simple PS however its effectiveness is questionable in the case of complex PS. A complex PS is one having many (often complicated) rules interrelating its concepts, e.g. dictating citizens have to submit different documents to invoke a PS based on their profiles and circumstances. The aim of this paper is to investigate the use of Knowledge Graphs (KG) for providing personalized information on PS modeled using CPSV. For this purpose, we develop and evaluate a KG for PS “Get a passport” in Greece as a proof-of-concept to study mapping of CPSV to KG. For simplicity, we limit our scope to developing a KG that can provide the input documents and the relevant cost required for obtaining a passport by citizens based on their profile and circumstances. Free software GRAKN.AI was employed for the development of the KG.
Investigating Web3D topics on StackOverflow: a preliminary study of WebGL and Three.js

Web3D developers often have to decide on which technologies are best for their projects. We explore that question through the perspective of community attention and support on Stack Overflow (SO). We focused on i) WebGL, a key low-level JavaScript (JS) API used to render 3D graphics in browsers without plugins, and ii) Three.js, a higher level JS library that reuses WebGL and is reputed easier and more intuitive. We considered questions from SO tagged with WebGL or Three.js and extracted all tags used on these questions. Using these, we were able to compare the relative attention (considering the number of questions and views) and support (considering satisfactory answers and how long they take) received by concerns and technologies associated to WebGL and Three.js. Our results suggest that Three.js gets significantly more community attention but less community support than WebGL on SO.
Deep Deterministic Policy Gradient for Navigation of Mobile Robots

This article describes the use of the Deep Deterministic Policy Gradient network, a deep reinforcement learning algorithm, for mobile robot navigation. The neural network structure has as inputs laser range findings, angular and linear velocities of the robot, and position and orientation of the mobile robot with respect to a goal position. The outputs of the network will be the angular and linear velocities used as control signals for the robot. The experiments demonstrated that deep reinforcement learning’s techniques that uses continuous actions, are efficient for decision-making in a mobile robot. Nevertheless, the design of the reward functions constitutes an important issue in the performance of deep reinforcement learning algorithms. In order to show the performance of the Deep Reinforcement Learning algorithm, we have applied successfully the proposed architecture in simulated environments and in experiments with a real robot.
Scalable Schema Discovery for RDF Data

The semantic web provides access to an increasing number of linked datasets expressed in RDF. One feature of these datasets is that they are not constrained by a schema. Such schema could be very useful as it helps users understand the structure of the entities and can ease the exploitation of the dataset. Several works have proposed clustering-based schema discovery approaches which provide good quality schema, but their ability to process very large RDF datasets is still a challenge. In this work, we address the problem of automatic schema discovery, focusing on scalability issues. We introduce an approach, relying on a scalable density-based clustering algorithm, which provides the classes composing the schema of a large dataset. We propose a novel distribution method which splits the initial dataset into subsets, and we provide a scalable design of our algorithm to process these subsets efficiently in parallel. We present a thorough experimental evaluation showing the effectiveness of our proposal.
Describing Textures Using Natural Language

Textures in natural images can be characterized by color, shape, periodicity of elements within them, and other attributes that can be described using natural language. In this paper, we study the problem of describing visual attributes of texture on a novel dataset containing rich descriptions of textures, and conduct a systematic study of current generative and discriminative models for grounding language to images on this dataset. We find that while these models capture some properties of texture, they fail to capture several compositional properties, such as the colors of dots. We provide critical analysis of existing models by generating synthetic but realistic textures with different descriptions. Our dataset also allows us to train interpretable models and generate language-based explanations of what discriminative features are learned by deep networks for fine-grained categorization where texture plays a key role. We present visualizations of several fine-grained domains and show that texture attributes learned on our dataset offer improvements over expert-designed attributes on the Caltech-UCSD Birds dataset.
Some problems of humanitarian training of future specialists in pedagogical specialties using interactive technologies

Abstract The article examines a number of issues related to the social and humanitarian training of future teachers using interactive technologies. In our opinion, social and humanitarian training should be considered in close connection with educational, methodological, educational and research processes. It is associated with all areas of vocational education and contributes to their successful functioning. In our opinion, more attention should be paid to teaching students of social and humanitarian disciplines using interactive technologies. Their use contains great educational, creative and developmental potential. Social and humanitarian disciplines have an impact on the spiritual and moral behavior and actions of a person, however, traditional approaches to their study, as a rule, do not allow achieving the desired result. Focusing on the degree of importance of studying social and humanitarian disciplines, we substantiate that a person lives in a social and humanitarian environment, and his life is a social process. In the process of studying social and humanitarian academic disciplines using interactive technologies, it increases the flexibility of a person’s thinking, these disciplines have a significant impact on the emotional-motivational sphere of the individual, and also contribute to the development of skills related to introspection and analysis of human relations.
App Competition Matters: How to Identify Your Competitor Apps?

App stores, such as Google Play and Apple Store, contain abundance of information, including descriptions and reviews of various apps. They provide valuable information for app developers to learn about their own apps as well as similar apps for improving their apps, e.g., adding popular features or removing unpopular features. A place to start this learning process is to identify the competitor apps of a given target app in terms of their features, popularity as well as other relevant aspects. Given the large number of apps and the large amount of information available about these apps, it is a very challenging task for app developers to effectively and efficiently identify competitor apps. In this paper, we introduce a novel approach for identifying the competitor apps of a given target app, which includes three major components. Firstly, we identify the factors that characterise the competition between apps. Then, based on the identified competition factors, we extract and process the relevant information from the app store about the target app and the apps in the same app category. Finally, we cluster the apps based on their similarity across all the competition factors and identify those apps that are in the same cluster as the target app as its competitor apps. We evaluate our approach by comparing its results with corresponding search results in (1) Google Trends and (2) Google Search. The results show that our approach is effective in identifying competitor apps.
Modern educational technologies in the context of distance learning

Abstract Due to the difficult situation in the world caused by a new coronavirus infection, all higher education institutions in the Russian Federation were necessarily transferred to remote mode. This was the impetus for the transition of education to the era of digitalization, not partially, but completely and without exceptions. This article describes the positive effects of this transition, which many scientists have noticed. The article discusses the methods of distance learning and describes in more detail the most effective methods, such as the business game. The importance of focusing on the personalization of learning, which, in turn, is possible in the digital educational environment, is indicated.
DETR for Pedestrian Detection.

Pedestrian detection in crowd scenes poses a challenging problem due to theheuristic defined mapping from anchors to pedestrians and the conflict betweenNMS and highly overlapped pedestrians. The recently proposed end-to-enddetectors(ED), DETR and deformable DETR, replace hand designed components suchas NMS and anchors using the transformer architecture, which gets rid ofduplicate predictions by computing all pairwise interactions between queries.Inspired by these works, we explore their performance on crowd pedestriandetection. Surprisingly, compared to Faster-RCNN with FPN, the results areopposite to those obtained on COCO. Furthermore, the bipartite match of EDharms the training efficiency due to the large ground truth number in crowdscenes. In this work, we identify the underlying motives driving ED's poorperformance and propose a new decoder to address them. Moreover, we design amechanism to leverage the less occluded visible parts of pedestrianspecifically for ED, and achieve further improvements. A faster bipartite matchalgorithm is also introduced to make ED training on crowd dataset morepractical. The proposed detector PED(Pedestrian End-to-end Detector)outperforms both previous EDs and the baseline Faster-RCNN on CityPersons andCrowdHuman. It also achieves comparable performance with state-of-the-artpedestrian detection methods. Code will be released soon.
Implicit Regularization in ReLU Networks with the Square Loss

Understanding the implicit regularization (or implicit bias) of gradient descent has recently been a very active research area. However, the implicit regularization in nonlinear neural networks is still poorly understood, especially for regression losses such as the square loss. Perhaps surprisingly, we prove that even for a single ReLU neuron, it is impossible to characterize the implicit regularization with the square loss by any explicit function of the model parameters (although on the positive side, we show it can be characterized approximately). For one hidden-layer networks, we prove a similar result, where in general it is impossible to characterize implicit regularization properties in this manner, except for the balancedness property identified in Du et al. [2018]. Our results suggest that a more general framework than the one considered so far may be needed to understand implicit regularization for nonlinear predictors, and provides some clues on what this framework should be.
Organizational Cyber Data Breach Analysis of Facebook, Equifax, and Uber Cases

Data breaches are events that have concluded in the compromise of personally identifiable information (PII) for millions of people globally. The consequences of such events can only result in certain serious outcomes, including identity theft. Such perilous outcomes highlight the importance of organizational entities accurately safeguarding and preserving the PII gathered from stakeholders or consumers. The user data breaches of Facebook, Equifax, and Uber concluded in the compromise of PII data for millions of consumers and employees, which are the most critical aspects that comprise any organizational infrastructure. This paper will examine the events leading to and transpiring after the data breaches of Facebook, Equifax, and Uber. In addition, the collective impact on every organization and its various incident management procedures will be addressed.
Identity-Based Outsider Anonymous Broadcast Encryption with Simultaneous Individual Messaging

Broadcast encryption (BE) is an effective method to broadcast encrypted confidential content, although it does not support the transmission of personalized messages to individuals. Broadcast encryption with personalized messages (BEPM) simultaneously transmits not only the common encrypted message to a group of users but also encrypted personalized messages to individual users. Currently available BEPM schemes fail to provide the recipient's anonymity that means the information of the subscriber set is available to the enemies. This paper first introduces a new BEPM paradigm, called identity-based outsider anonymous broadcast encryption with personalized messages (IB-OAnoBEPM), by tweaking the identity-based framework over the most advanced and secure asymmetric Type-3 variant of the bilinear maps. In addition to being adaptively secure, our construction withstands indistinguishable chosen-plaintext attack under the standard asymmetric decisional bilinear Diffie-Hellman exponent assumption without using the random oracle model. More positively, the proposed scheme is the first BEPM system that achieves security without any non-standard q-type assumptions. In particular, our design is very efficient both in terms of communication and computation costs, as the ciphertext size is constant, and the decryption algorithm requires only three asymmetric bilinear pairings to recover the correct message and the personalized messages, which are highly desirable for light-weight devices.
Arbitrary Viewpoint Visualization for Teleoperated Hydraulic Excavators

In this paper, we propose a visualization system for the teleoperation of excavation works using a hydraulic excavator. An arbitrary viewpoint visualization system is a visualization system that enables teleoperators to observe the environment around a machine by combining multiple camera images. However, when applied to machines with arms (such as hydraulic excavators), a part of the field of view is shielded by the image of the excavator’s arm; hence, an occlusion occurs behind the arm. Furthermore, it is difficult for teleoperators to understand the three-dimensional (3D) condition of the excavating point because the current system approximates the surrounding environment with a predetermined shape. To solve these problems, we propose two methods: (1) a method to reduce the occluded region and expand the field of view, and (2) a method to measure and integrate the 3D information of the excavating point to the image. In addition, we conduct experiments using a real hydraulic excavator, and we demonstrate that an image with sufficient accuracy can be presented in real-time.
A Hybrid Approach to Human Motion Enhancement under Kinematic and Anthropometric Constraints

This paper proposes a novel approach to improve the quality of human motion data captured by a depth sensor. Depth-based motion capture (D-Mocap) data suffers significant errors due to noise, self-occlusion, interference, and other algorithmic limitations. We aim to smooth and correct 3D joint trajectories to be more kinematically admissible and anthropometrically stable. Our research synergistically integrates advanced nonlinear Kalman filters (KF) with Differential Evolution (DE) algorithms into one computational flow to take advantage of the kinematic and arthrometric constraints respectively. Specifically, we compare three nonlinear KFs in terms of their effectiveness and accuracy, including the Extended Kalman filter (EKF), the Unscented Kalman filter (UKF), and the Tobit Kalman filter (TKF). Two sets of motion capture datasets are used in this research to examine the proposed algorithms in both simulated and real-world data. Furthermore, we demonstrate significant improvements in six joint angles that are often used for clinical gait assessment.
Eye Tracking in Human Interaction

There is a long history of interest in looking behavior during human interaction. With the advance of (wearable) video-based eye trackers, it has become possible to measure gaze during many different interactions, even in challenging situations, such as during interactions between young children and their caregivers. We outline the different types of eye-tracking setups that currently exist to investigate gaze during interaction. The setups differ mainly with regard to the nature of the eye-tracking signal (head- or world-centered) and the freedom of movement allowed for the participants (see Figure 1). These crucial, yet often overlooked features place constraints on the research questions that can be answered about human interaction. Furthermore, recent developments in machine learning have made available the measurement of gaze directly from video recordings, without the need for specialized eye-tracking hardware, widening the spectrum of possible eye-tracking setups. We discuss the link between type of eye-tracking setup and the research question being investigated, and end with a decision tree to help researchers judge the appropriateness of specific setups (see Figure 2).
Asymmetric Convolution-Based Neural Network for SAR Ship Detection from Scratch

In recent years, an increasing number of synthetic aperture radar (SAR) researchers have applied convolutional neural network (CNN)-based optical image object detection methods to the field of SAR ship detection via transfer learning technology, and achieve well performance. However, such approaches need to load a pre-trained model for initialization, which results in the structure of CNN is fixed and it is hardly for improvement and optimization; besides, there is domain mismatch for SAR ship detection, which restricts the detection performance to some extent. In the paper, we designed two asymmetric convolution blocks that can be easily embedded in any object detection methods, namely asymmetric and square convolution feature aggregation block (A-S AB) and asymmetric and square convolution feature fusion block (A-S FB), and embedded them in the classic DSOD by replacing all of the 3 × 3 convolution layers with A-S AB and A-S FB, respectively. Experiments on the RDISD_SAR dataset demonstrate that the A-S AB contribute up to 2.86% gain of average precision (AP) while significantly reducing the number of parameter and the amount of computation; the A-S FB contribute about 1.00% AP gain at channel decay factors of 0.2, 0.3, 0.5, and 0.6, and the number of parameter and the amount of computation are also reduced to varying degrees. Compared with the original DSOD, the well performance of the two structures designed in this paper is obvious.