Reviewerly/exHarmony huggingface dataset:
Analyzing Transferability of Happiness Detection via Gaze Tracking in Multimedia Applications

How are strong positive affective states related to eye-tracking features and how can they be used to appropriately enhance well-being in multimedia consumption? In this paper, we propose a robust classification algorithm for predicting strong happy emotions from a large set of features acquired from wearable eye-tracking glasses. We evaluate the potential transferability across subjects and provide a model-agnostic interpretable feature importance metric. Our proposed algorithm achieves a true-positive-rate of 70% while keeping a low false-positive-rate of 10% with extracted features of the pupil diameter as most important features.
Adaptively Secure and Fast Processing of Conjunctive Queries over Encrypted Data

This paper concerns the fundamental problem of processing conjunctive queries that contain both keyword and range conditions on public clouds in a privacy preserving manner. No prior Searchable Symmetric Encryption (SSE) based privacy preserving conjunctive query processing scheme satisfies the three requirements of adaptive security, efficient query processing, and scalable index size. In this paper, we propose the first privacy preserving conjunctive query processing scheme that satisfies all the above three requirements. To achieve adaptive security, we propose an Indistinguishable Bloom Filter (IBF) data structure for indexing. To achieve efficient query processing and structural indistinguishability, we propose a highly balanced binary tree data structure called Indistinguishable Binary Tree (IBtree). To achieve scalable and compact index size, we propose an IBtree space compression algorithm to remove redundant information in IBFs. To optimize search efficiency, we propose a traversal minimization algorithm. To make our scheme dynamic, we propose update algorithms. We prove that our scheme is adaptive secure under the IND-CKA secure model. The key contribution of this paper is on achieving conjunctive query processing with both strong privacy guarantee and practical efficiency in terms of both speed and space. We implemented our scheme in C++, evaluated and compared its performance with the prior KRB scheme for keyword queries and the prior PBtree scheme for range queries on two real-world data sets. Experimental results show that our scheme is both fast and scalable. For example, processing a query only takes a few milliseconds for millions of records.
Hardware and Software Support of Technological Processes Virtualization

This paper proposes is development of is to develop hardware virtualization technology for complex systems. Within the framework of the study to developed the concept of hardware virtualization of the IT infrastructure of complex systems; is developed a scenario for virtualization solutions for complex systems; is developed a methodology for the formation of hardware virtualization infrastructure.
DONE: Enhancing network embedding via greedy vertex domination

In this paper, we present DONE, a novel framework for learning networks representations, which fundamentally supports numerous network analytic tasks such as node classification, clustering, and visualization. Most existing network embedding methods are unable to efficiently scale for large networks and usually suffer from performance issues. In addition, these methods cannot efficiently leverage the vertex labels that are usually very scarce in real-world networks. Our framework provides a powerful way to generate representations for network vertices without relying on user-defined heuristics for manual feature extraction. We present a deep autoencoder model to generate low-dimensional feature representations by learning network reconstruction and semi-supervised classification tasks. We propose a novel greedy algorithm based on vertex domination and centrality concepts to simplify networks while preserving network topology and community structure, namely GreedyNet. In addition, we propose a novel sampling approach to estimate the labels of unlabeled vertices and adopt it to learn superior embedding. Using network dominating vertices, our approach enhances the generalization and scalability of network embedding through simplifying the underlying network. To the best of our knowledge, we are the first to propose the idea of using network dominating vertices to enhance network embedding. The experimental results show that our method outperforms the state-of-the-art methods.
An Analysis of Imperceptibility and Robustness Performance in CRT Image Watermarking based on Color Space Theory

Abstract Color images have more information and more storage space than gray images. The process of computing color images is also more complex compared to gray images. Nevertheless, human vision is more sensitive to color images compared to gray images, so color image processing is so important. Image watermarking is a method of copyright protection by embedding copyrights in the image. The embedding process can cause changes in the pixel value of the image, so it needs to be analyzed and measured the quality of watermarked images both visually or imperceptibility and watermark durability. In general, color images use the RGB color space model and color space conversion is not done when embedding watermarking. But several studies have suggested converting RGB color space to YCbCr color space for a specific purpose. This research proposes to analyze the robustness and imperceptibility performance in the R, G, B and Y color spaces in the CRT method. CRT is one of the methods of image watermarking in the spatial domain, where this method was previously also used as a cryptographic algorithm. So that the CRT method excels in durability and security watermarks. Based on the results of testing with the CRT method, the color space R has an advantage in the aspect of imperceptibility compared to other color spaces, the difference is very thin with the G and B color spaces, but when compared with the Y color space the difference reaches 3dB based on the PSNR calculation. But for testing the robustness of the color space Y is far superior compared to other color spaces even though extraction without attack, not all watermarks can be extracted perfectly.
Are They Actually Looking? Identifying Smartphones Shoulder Surfing Through Gaze Estimation

Mobile devices have evolved to be a crucial part of our everyday lives. However, they are subject to different types of user-centered attacks such as shoulder surfing attacks. Previous work focused on notifying the user with a potential shoulder surfer if an extra face is detected. Although it is a successful approach, it eliminated the possibility that the alleged attacker is just standing and not looking at the user's device. In this work, we investigate estimating the gaze of potential attackers in order to verify if they are indeed looking at the user's phone.
A Modified Method of an Inverse Neural and Adaptive Correction Factor for Speed Control of DC Motor

This Article Proposes a modified method of adaptive controlling for the DC motor and it is deriver circuit based on Inverse Neural Controller. The proposed controller composed of two parts: the first is the inverse neural control that will control the speed of the direct current motor and, the second is adaptive correction factor that will act as an adaptive compensator to overcome the error (offset) in the steady state response of the motor (eliminate the steady state error). The obtained results prove that the new method successfully controlled the DC motor speed by eliminating the error in the steady state response, and letting the output correctly tracking the input set-point with minimum rising time and minimum peak overshoot. Simulation done using MATLAB SIMULINK. Results compared with the conventional Inverse Neural controller to demonstrate the impact of the proposed method also a load test had been done.
Meal Chat: Promoting Mealtime Social Interaction for College Students

Mealtimes serve important social functions in our everyday lives. Public dining spaces on college campuses are positioned to be social and engaging spaces to make new connections. With the prevalence of digital devices, technology usage introduces new dynamics into students' mealtimes. In this study, we explored the current mealtime technology usage patterns of college students and rethought the role of technology in eating. We proposed Meal Chat - a technology probe to explore the alternative role of technology during mealtimes by encouraging social interaction for students eating at on-campus public dining areas. Meal Chat aims to provide an opportunity for college students to socialize and reduce the barrier of starting mealtime socialization with a stranger. Rethinking the role of technology in mealtimes, Meal Chat seeks to prompt rather than to replace social interaction during mealtimes.
Matrix Factorization with Interval-Valued Data

With many applications relying on multi-dimensional datasets for decision making, matrix factorization (or decomposition) is becoming the basis for many knowledge discovery and machine learning tasks, from clustering, trend detection, anomaly detection, to correlation analysis. Unfortunately, a major shortcoming of matrix analysis operations is that, despite their effectiveness when the data is scalar, these operations become difficult to apply in the presence of non-scalar data, as they are not designed for data that include non-scalar observations, such as intervals. In this paper, we propose matrix decomposition techniques that consider the existence of interval-valued data. We show that naive ways to deal with such imperfect data may introduce errors in analysis and present factorization techniques that are especially effective when the amount of imprecise information is large.
Digital Liminalities: Understanding Isolated Communities on the Edge

This paper brings together three distinct case studies to explore how social isolation and notions of liminality shape ontological security within communities on "the edge" of society. Each case study exemplifies the differing nature of liminality in everyday contexts and the extent to which increased digitalisation perturbs it in multiple ways. Taking an ethnographic approach, the research engaged with seafarers onboard container ships in European waters, communities in Greenland and welfare claimants in the North East of England. It posits that technological innovation must attend to the routinisation of everyday life through which people establish ontological security if such innovation is to be supportive. The paper thus moves beyond existing HCI scholarship by foregrounding the contextual and relational aspects of social isolation rather than the technological. It does so by advocating a ground-up design process that considers ontological security in relation to notions of liminality among communities on the edge.
Morphic: Auto-Personalization on a Global Scale

Users frequently have trouble finding and changing technology settings, even when adjusting those settings to personalize their technology devices may significantly improve their user experience. This paper describes the Morphic project, a cloud-based auto-personalization tool. Primarily designed for improving accessibility, the auto-personalization in Morphic may also help a broader audience. This paper describes: 1) the technical infrastructure needed to support cloud-based auto-personalization, 2) the interaction design approaches necessary to support users during the personalization of their settings, and 3) which settings and modifications have been most frequently used. This paper presents the evolution of the Morphic project, the building of the infrastructure, the formative evaluation of various interaction design approaches (NFC cards, physical keypads, on-screen buttons, and menus), and implications for researchers and developers. It also includes preliminary findings from six technology probes.
LTE Network Quality Analysis Method Based on MR Data and XGBoost Algorithm

At present, LTE network quality analysis is mainly based on Drive Test (DT) and Call Quality Test (CQT). Both methods require a large number of professional network optimization engineers and testing equipment. The testing methods cost a lot of time and money, and they cannot diagnose the causes of LTE network quality difference in time, which seriously affects customer perception. In this paper, a method of LTE network quality analysis based on Measurement Report (MR) data and XGBoost classification algorithm is proposed to quickly diagnose the cause of poor quality network. This method can effectively and comprehensively reflect customer perception. It can provide useful information for network optimization. It greatly improves the efficiency of network optimization engineers, and reduces a large number of labor costs.
Design and Implementation of Internet of Things Information Security Transmission Based On PBFT Algorithm

Aiming at the security problems such as data theft, fraud and tampering in the information transmission process of the Internet of things, this paper studies the decentralized and trustless technology of Internet of things information transmission based on the blockchain technology through the super ledger Fabric blockchain platform, and proposes a technology for Internet of things transmission information security that uses the PBFT algorithm. Simulation results show that the algorithm effectively utilizes the redundant computing power in the network and improves the security of information transmission and fault tolerance of the system.
Distributed and Context Aware Application of Deep Neural Networks in Mobile 3D-Multi-sensor Systems Based on Cloud-, Edge- and FPGA-Computing

The use of deep neural networks (DNN) for 3Dimage processing significantly enhances visual cognition of mobile systems by considering spatial information. However, training and execution require high computing power. This is crucial in applications with real-time constraints since mobile systems have limited resources. Current approaches do not consider the usage of 3D-sensing. Furthermore, suggested system architectures solely focus on cloud- and edge-computing in combination with load balancing and parallelization for a distributed execution of DNNs. In contrast, we propose a novel system architecture for the distributed and context aware usage of DNNs for image processing tasks in mobile 3D-multi-sensor systems. Thereby, the scalable cloud- and edge-infrastructure is complemented by realtime capable and energy-efficient FPGA-computing. The publishsubscriber pattern facilitates the distributed execution of DNNs as well as their dynamic deployment. Moreover, context information is considered. Thus, a rule-based context model dynamically loads specialized DNNs and selects appropriate devices for execution. Finally, a case-study on a mobile 3D-multi-sensor system for wheeled walkers demonstrates applicability and benefits of the proposed approach.
Ambiguous reconstructions of hv-convex polyominoes

The core problem of discrete tomography, i.e., the faithful reconstruction of an unknown discrete object from projections along a given set of directions, is ill-posed in general. When further constraints are imposed on the object or on the employed directions, uniqueness of the reconstruction can be obtained. It is the case, for example, of convex lattice sets in Z2, for which a theorem by Gardner and Gritzmann assures the faithful reconstruction when suitable sets of directions are considered. It was conjectured that a similar result holds for the class of hv-convex polyominoes. In this paper we are concerned with this conjecture, providing new 4-tuples of discrete directions that do not lead to a unique reconstruction of hv-convex polyominoes, underlining the relevant structural difference with the class of convex sets. Our result is based on the recursive definition of new hv-convex switching components on discrete sets along four directions.
Bearing fault diagnosis from raw vibration signals using multi-layer extreme learning machine

In recent years, machine learning technology is widely used in the field of fault diagnosis for bearings. Although these methods usually work well, the following defects still exist when they are dealing with large amount of fault data: (1) feature extraction methods need to rely on expertise or signal processing technologies. Therefore, there is a lack of a feature extraction method that is common to different diagnostic problems; (2) shallow models can't learn more complex mapping relationships well; (3) traditional intelligent diagnostic methods are usually computationally intensive and slow in convergence. Inspired by the Auto-encoder's (AE) feature extraction capability and fast training speed of the Extreme Learning Machine (ELM), a new fault diagnosis method for bearings based on Extreme Learning Machine-Autoencoder (ELM-AE) is proposed in this paper. With its automatic feature extraction capability and very efficient learning strategy, the raw vibration signals of bearings are directly sent to the model without any manual feature extraction for fault diagnosis, which overcomes the above drawbacks. The experimental results on CWRU bearing dataset show that the proposed method takes into account both diagnostic accuracy and time efficiency. Compared with existing literatures, our proposed method obtains superior accuracy.
In-water handwriting in multi-medium using electrical impedance imaging

Abstract A novel approach to human–computer interaction, in water handwriting is presented in this work. The novel handwriting method is based on tomographic imaging of electrical resistivity changes. The new method allows the users to write inside of a water ball in a natural, unconstrained way. In-water handwriting suffers no friction and allows handwriting or drawing in a more relaxed way. This work shows how an electrical impedance tomography (EIT) system and sensor/phantom can be used to create a robust handwriting in water. It is well known that EIT has low special resolution but offers a very good functional imaging with high temporal resolution. The work shows an example of a good quality functional imaging aspect of the EIT in a novel human computer interface (HCI) application. Although it is almost impossible to produce images of letters or complex shapes using EIT sensor, it is totally possible to produce the same using many EIT frames. The system used in this study has a frame rate of 15 frames per second so the actual handwriting of letters and shapes can be done without noticing any delays. Analysis of image similarity between actual handwriting and the one follow the EIT scheme shows a good correlation.
Embracing Uncertainty in HCI

Uncertainty is prevalent characteristic of contemporary life, and a central challenge of HCI. This one-day workshop will explore how HCI has and might continue to engage uncertainty as a generative feature in design, as opposed to a force to mitigate and control. We hope to convene researchers from broad ranging areas to explore the many ways in which uncertainty appears in our research and the different types of responses that HCI has to offer. There is an incredible variety of conceptual formulations of uncertainty and related ideas like risk, ambiguity, and suspense that raise both difficult challenges as well as significant opportunities for creative engagement with societal challenges. During the workshop, we won't seek to "solve" uncertainty but rather expand the ways in which we think about and navigate it. In doing so, we will experiment with and contribute to new practices, methods, and concepts for embracing uncertainty. Outcomes of the workshop will include documentation of exercises designed to evoke uncertainty in participants, concept mappings, and a collection of short essays written and refined by participants.
User Experience Analysis on Class I LPKA Palembang Filial E-School

Abstract The Class I LPKA Palembang management wishes to present academic services through the provision of systems that can better support education. Therefore, Filial e-school was built and implemented as a learning system in filial schools implemented in LPKA. The e-school system with the link address http://e-schoolfiliallpka.com/, consists of several modules: a.Video-based learning module, b.Audio-based learning module, c.Text-based learning module, d.Distance learning media, e.Giving and submission assignment media, f.Discussion media. This system facilitates the learning process of LPKA students to be able to learn independently with existing media and make it easier for teachers to deliver learning material. The system development method used in this study was the Rapid Application Development (RAD) method. Testing of user experience on the use of e-school was tested using the User Experience method with the following aspects:Perspicuity, Efficiency, Dependability, Stimulation, and Novelty. Based on the results of UEQ differences between parent schools’ teachers and LPKA students on the use of filial e-school, it was found that e-school filial gave slightly higher impressions to parent schools’ teachers compared to students. The average results of the UEQ questionnaire for all users of the e-school filial system showed that the stimulation and novelty aspects were felt to be higher than the other four aspects.
Method for identification, stability analysis and the dynamics monitoring of sociotechnical clusters

Abstract The problem arising in the study of sociotechnical clusters is revealed. This problem consists in the impossibility of using a standard approach for the identification and sociotechnical clusters analysis, which does not take into account the relationship of coordinated interaction between objects in such clusters, depending on their goals and objectives. The set of indicators for the identification and analysis of sociotechnical clusters is substantiated. These indicators are based on fuzzy relations of concerted interaction between objects in such clusters. The original method for identification, stability analysis and the dynamics monitoring of sociotechnical clusters is proposed. The proposed method, in contrast to the known ones, allows you to analyze: the changing of the sociotechnical clusters sizes; the sociotechnical cluster centre drift; the separation and merging of these clusters; the emergence and disappearance of sociotechnical clusters. Fuzzy cognitive approach is used for the first time: firstly, to justify and estimate a set of indicators for sociotechnical clusters; secondly, for the stability analysis of these sociotechnical clusters.
Enhanced Authentication Mechanism for Automated Teller Machine (ATM) through Implementation of Soft Two-Factor Authentication

The demand of Automated Teller Machine (ATM) is exponentially increase from 2003 to 2018 which makes the ATM as important component in modern banking. The easiness and flexibility to have a transaction at any time without the need to stand in a long queue and dealing with administration make ATM is preferable by a lot of bank customers. However, it is also a big attraction point for criminals and fraudsters. Skimming is one of the threat of ATM service which will allow fraudster to create a clone card through duplication and combination of eavesdropping techniques such as shoulder surfing or installation of miniature video camera, utilization of counterfeit hardware proximity, etc. Therefore, an enhanced method of authentication shall be implemented to maintain the security of ATM service. In this work, we are proposing a new mechanism of ATM authentication which will enhance its security through the use of Soft Two-Factor Authenticator. The analysis of our proposed mechanism is done using NIST SP800-63B to ensure its security against possible techniques for skimming. Henceforth, it provides an additional security against skimming.