Reviewerly/exHarmony huggingface dataset:
Environment-Aware Multiscene Image Enhancement for Internet of Things Enabled Edge Cameras

With the development of the Internet of Things, it is feasible to leverage as much computing resources on an IoT-enabled camera to realize edge intelligence (hereafter referred to as an edge camera) for providing various human-centered services. However, environmental interference can significantly degrade the visual quality of an image into edge cameras, thereby affecting the overall image and service quality. In order to maintain image stability, image enhancement using complex deep networks has been widely utilized. As edge cameras often have restricted computing power and cannot run complex neural networks efficiently, we design a lightweight residual enhancement network, which incorporates highly efficient convolution operations as basic building blocks. With this modular design on a generative adversarial network training framework, the resultant network can be easily optimized according to hardware specifications of a given edge camera. To accommodate various weather conditions for an edge camera, we further design an environment-aware model deployer that can detect the weather conditions and deploy the most suitable image-enhancement model. The experimental results show that the lightweight residual enhancement network can produce image quality close to what past research demonstrated while increasing running speeds by more than 30%. In a streaming video to simulate changing weather, the results also demonstrate the effectiveness of the proposed environment-aware model deployer to maintain the quality of the images under a dynamic environment.
Image Restoration via Deep Memory-Based Latent Attention Network

Deep convolutional neural network (CNN) has made impressive achievements in the field of image restoration. However, most of deep CNN-based models have limited capability of utilizing the hierarchical features and these features are often treated equally, thus restricting the restoration performance. To address this issue, the present work proposes a novel memory-based latent attention network (MLANet) aiming to effectively restore a high-quality image from a corresponding low-quality one. The key idea of this work is to employ a memory-based latent attention block (MLAB), which is stacked in MLANet and makes better use of global and local features through the network. Specifically, the MLAB contains a main branch and a latent branch. The former is used to extract local multi-level features, and the latter preserves global information by the structure within a latent design. Furthermore, a multi-kernel attention module is incorporated into the latent branch to adaptively learn more effective features with mixed attention. To validate the effectiveness and generalization ability, MLANet is evaluated on three representative image restoration tasks: image super-resolution, image denoising, and image compression artifact reduction. Experimental results show that MLANet performs better than the state-of-the-art methods on all the tasks.
A Case Study of Blockchain-Induced Digital Transformation in the Public Sector

Public administration has long faced the challenge of addressing a steadily growing workload with limited resources. Blockchain and related technologies promise manifold applications which might alleviate this tension, but adoption may be hampered by the dearth of academic literature documenting existing use cases and the lessons learned from them. This case study examines a public administration use case which was initiated by the state government of South Tyrol in Northern Italy in cooperation with the firm SAP. Many important lessons have been learned in pursuit of the main project goal of streamlining the complex administrative processes surrounding the business of building and modifying cell towers. Exploiting the full potential of blockchain necessitates a complete rethinking of public management, yet holds the potential for a leaner and more service-oriented administration that reestablishes citizen trust in public institutions. However, several disadvantages also emerged, which highlight specific limitations of blockchain technologies. In this paper we apply a single case study approach and derive several best practices based on the analysis of qualitative interviews and rich project documentation.
Enhanced Interactive 360° Viewing via Automatic Guidance

We present a new interactive playback method to enhance 360° viewing experiences. Our method automatically rotates the virtual camera of a 360° panoramic video (360° video) player during interactive viewing to guide the viewer through the most important regions of the video. With this method, the viewer can watch a 360° video with minimum efforts to find important events in a scene both in interactive (e.g., HMD) and less-interactive (e.g., PC and TV) viewing environments. To estimate the importance of each viewing direction, we combine spatial and temporal saliency with cluster-based weighting. A maximum backward cumulative importance volume (MBCIV) is then constructed by accumulating this importance in the video space. During playback, which uses a forward tracing scheme through the MBCIV, the initial optimal path is found based on the viewer’s viewing direction. A smooth path is then derived using penalized curve fitting. Finally, the virtual camera is rotated to follow the path. The experiments and user studies demonstrate that our method allows the viewer to effectively enjoy 360° videos with minimum interaction efforts, or even through a non-interactive display.
Infusing Principles and Practices for Secure Computing Throughout an Undergraduate Computer Science Curriculum

In recent years, all computing disciplinary communities and curricular guidelines have increased their expectations of and requirements for incorporating cybersecurity into their discipline. For computer science, this has been a daunting task for a number of reasons, including the fast-paced evolution and expansion of the discipline, the perceived challenge of finding space in the curriculum, and the difficulty of selecting the best content. This paper takes the position that infusing security concepts pervasively into an undergraduate Computer Science program is a crucial and attainable best practice. A five-step methodology is presented to incorporate cybersecurity into a traditional computer science curriculum in a way that maintains disciplinary integrity without adding significant new curricular content. This methodology is consistent with the philosophy and recommendations of the latest computer science and cybersecurity curricular guidelines. The paper also illustrates the application of these techniques to a typical Computer Science program.
Information Hiding for Text Files by Adopting the Genetic Algorithm and DNA Coding

Hiding information is a process to hide data or include it in different digital media such as image, audio, video, and text. However, there are many techniques to achieve the process of hiding information in the image processing, in this paper, a new method has been proposed for hidden data mechanism (which is a text file), then a transposition cipher method has been employed for encryption completed. It can be used to build an encrypted text and also to increase security against possible attacks while sending it over the World Wide Web. A genetic algorithm has been affected in the adjustment of the encoded text and DNA in the creation of an encrypted text that is difficult to detect and then include in the image and that affected the image visual quality. The proposed method outperforms the state of arts in terms of efficiently retrieving the embedded messages. Performance evaluation has been recorded high visual quality scores for the (SNR (single to noise ratio), PSNR (peak single to noise ratio) and MSE (mean square error).
Satisfaction of Students with the Quality of Educational Services at Technical University

Improving the quality of educational services is impossible without a detailed analysis of student satisfaction. Identifying weaknesses in the educational process and socio-cultural environment, as well as evaluating the dynamics of satisfaction in recent years will help to formulate a roadmap for managing the quality of education. The research was conducted at Perm Polytechnic University which is typical, therefore the findings of the research can be considered as representative. The article analyzes the results of a survey of 1126 full-time students, which was about 12% of the contingent (full-time). The sample is presented for all structural divisions: faculties and branches, as well as the content of questionnaires on blocks of questions. The results of the level of satisfaction with the quality of educational activities are analyzed, in particular: the content of educational programs, the implementation of educational programs, the conditions for ensuring the educational process, the quality of teaching, knowledge control and training. Positive assessment (satisfaction is more than 50%) deserve: the organization and conditions of the educational process, the content of academic disciplines in all cycles, the quality of teaching, the volume and quality of the library fund. However, some comments revealed dissatisfaction with the technical means, the quality of the content audiences, the organization of pedagogic assistance and students’ self-directed learning, schedule of classes, quality of teaching materials, organization of traineeships, quality control test, availability of laboratories and computer classes outside the classroom. The assessment of the socio-cultural environment considered aspects of the organization of students’ extracurricular work, the socio-psychological climate; the conditions of life support of the educational process (sanitary and social); socio-cultural activities related to the satisfaction of spiritual and physical needs of an individual; socio-cultural fostering. The main comments are related to the quality of social and living conditions (catering, living conditions in a hostel). The results of the survey showed the need for further research in order to form a generalized (complex) model for assessing the quality of the University activities.
Formal Analysis of Mobile Multi-Factor Authentication with Single Sign-On Login

Over the last few years, there has been an almost exponential increase in the number of mobile applications that deal with sensitive data, such as applications for e-commerce or health. When dealing with sensitive data, classical authentication solutions based on username-password pairs are not enough, and multi-factor authentication solutions that combine two or more authentication factors of different categories are required instead. Even if several solutions are currently used, their security analyses have been performed informally or semiformally at best, and without a reference model and a precise definition of the multi-factor authentication property. This makes a comparison among the different solutions both complex and potentially misleading. In this article, we first present the design of two reference models for native applications based on the requirements of two real-world use-case scenarios. Common features between them are the use of one-time password approaches and the support of a single sign-on experience. Then, we provide a formal specification of our threat model and the security goals, and discuss the automated security analysis that we performed. Our formal analysis validates the security goals of the two reference models we propose and provides an important building block for the formal analysis of different multi-factor authentication solutions.
Out-of-plane motion and non-perpendicular alignment compensation for 2D-DIC based on cross-shaped structured light

Although the 3D-DIC method has matured both theoretically and technically, the 2D-DIC method still plays an important role in in-plane deformation measurements. However, the accuracy of 2D-DIC is affected by out-of-plane motion (including out-of-plane translation and out-of-plane rotation) and non-perpendicular alignment. To tackle this problem, we propose to directly measure these unfavorable error sources by cross-shaped structured light (CSSL) and the optical triangulation method. Subsequently, pseudo-strains are calculated and compensated using an integrated mathematical model developed in this paper. To avoid mutual interference between the structured light strips and the speckle image, color coding is also proposed to use different color information for 2D-DIC processing and error compensation. Experiments with controlled out-of-the-plane motions show that the mean error after compensation can be as small as 50με. Uniaxial tension tests were also conducted to verify the feasibility of the proposed method in the real material parameter estimation experiment.
Table-to-Dialog: Building Dialog Assistants to Chat With People on Behalf of You

Artificial Intelligence (AI) personal assistant has attracted much attention from both academia and industry.Almost all existing AI personal assistants serve as service terminals to chat with human users for certain tasks.We are instead interested in building AI personal assistants for a different yet important dialog scenario, where they chat with people to fulfill specific tasks on behalf of their human users.As the personal assistants are playing a requester role, instead of a service terminal role, the conversation goal becomes delivering or requesting information according to specific user requests precisely and efficiently.The challenge for the conversation policy is that all user requests must be delivered precisely, while the challenge for the response generation is that it's generally expected for machine generated responses to cover multiple information slots, either requesting or delivering, to make the conversation efficient.In this paper, we present Table-to-Dialogue, a novel approach to address the above challenges when building a requester role AI personal assistant.We employ an encoder-decoder network to learn explicit conversation policy, which generates the corresponding information slots based on the conversation context and the user request table.We further integrate a novel Multi-Slot Constrained Bi-directional Decoder (MS-CBD) into the above encoder-decoder network, to generate machine response according to the multiple slot values and their intermediate representations from the policy decoder.Different from the existing single direction text decoder approaches, MS-CBD leverage the bi-directional context of the response when generating it to enhance the semantic coherence.The experiments shows that our approach significantly outperform the state-of-the-art conversation approaches on automatic and human evaluation metrics.
Robust Cooperative Multi-Vehicle Tracking with Inaccurate Self-Localization Based on On-Board Sensors and Inter-Vehicle Communication

The fusion of on-board sensors and transmitted information via inter-vehicle communication has been proved to be an effective way to increase the perception accuracy and extend the perception range of connected intelligent vehicles. The current approaches rely heavily on the accurate self-localization of both host and cooperative vehicles. However, such information is not always available or accurate enough for effective cooperative sensing. In this paper, we propose a robust cooperative multi-vehicle tracking framework suitable for the situation where the self-localization information is inaccurate. Our framework consists of three stages. First, each vehicle perceives its surrounding environment based on the on-board sensors and exchanges the local tracks through inter-vehicle communication. Then, an algorithm based on Bayes inference is developed to match the tracks from host and cooperative vehicles and simultaneously optimize the relative pose. Finally, the tracks associated with the same target are fused by fast covariance intersection based on information theory. The simulation results based on both synthesized data and a high-quality physics-based platform show that our approach successfully implements cooperative tracking without the assistance of accurate self-localization.
Quantum process tomography with unsupervised learning and tensor networks

The impressive pace of advance of quantum technology calls for robust and scalable techniques for the characterization and validation of quantum hardware. Quantum process tomography, the reconstruction of an unknown quantum channel from measurement data, remains the quintessential primitive to completely characterize quantum devices. However, due to the exponential scaling of the required data and classical post-processing, its range of applicability is typically restricted to one- and two-qubit gates. Here, we present a technique for performing quantum process tomography that addresses these issues by combining a tensor network representation of the channel with a data-driven optimization inspired by unsupervised machine learning. We demonstrate our technique through synthetically generated data for ideal one- and two-dimensional random quantum circuits of up to 10 qubits, and a noisy 5-qubit circuit, reaching process fidelities above 0.99 using several orders of magnitude fewer (single-qubit) measurement shots than traditional tomographic techniques. Our results go far beyond state-of-the-art, providing a practical and timely tool for benchmarking quantum circuits in current and near-term quantum computers.
Adaptive Gaussian Mixture based orbit determination with combined atmospheric density uncertainty consideration

Large initial uncertainties in the semi-major axis or force-model uncertainties, such as atmospheric density uncertainty are key drivers of the along-track uncertainty growth. Long propagation times may result in the need to use filtering algorithms for orbit determination that do not reside to the assumption of Gaussianity for state errors but estimate the entire probability density function. Adaptive Gaussian mixture based filters have shown promising results in the past. Previous research in the field of orbit determination using Gaussian mixture filters has restricted its attention to initial uncertainties in the semi-major axis direction. The present paper focuses on the consequences of including realistic, physics-based descriptions of atmospheric density uncertainty into the covariance propagation of the mixture kernels. It is shown that the neglect of process noise, as has been customary for many years, can lead to undesired characteristics of the probability density function (pdf) estimates and that the inclusion of atmospheric density uncertainty process noise, even in cases where it is not the dominant driver of along-track uncertainty growth, is able to correct these deficiencies. For low orbiting satellites with increased ballistic coefficients or small initial uncertainties in the semi-major axis direction, density uncertainty is the dominant driver of the along-track uncertainty increase. Due to its growth that evolves at least cubic in time, situations may arise which require the usage of Gaussian mixtures also for the process noise when working in Cartesian coordinates. The theoretical foundation for this case is elaborated and an algorithm capable of dynamically switching between a single Gaussian and a Gaussian mixture for the density uncertainty process noise is presented.
Parallel Simulation of Quantum Key Distribution Networks

With the significantly growing investment in quantum communi-cations, quantum key distribution (QKD), as a key application toshare a security key between two remote parties, has been deployedin urban areas and even at a continental scale. To meet the designrequirements of QKD on a quantum communication network, todayresearchers extensively conduct simulation-based evaluations in ad-dition to physical experiments for cost efficiency. A practical QKDsystem must be implemented on a large scale via a network, notjust between a few pairs of users. Existing discrete-event simulatorsoffer models for QKD hardware and protocols based on sequentialexecution. In this work, we investigate the parallel simulation ofQKD networks for scalability enhancement. Our contributions layin the exploration of QKD network characteristics to be leveragedfor parallel simulation. We also develop a parallel simulator forQKD networks with an optimized scheme for network partition.Experimental results show that to simulate a 64-node QKD net-work, our parallel simulator can complete the experiment 9 timesfaster than a sequential simulator running on the same machine.Our linear-regression-based network partition scheme can furtheraccelerate the simulation experiments up to two times than using arandomized network partition scheme.
Review of different platforms to perform rapid onsite evaluation via telecytology

Abstract There is increased utilisation of cytopathology to provide a rapid onsite evaluation (ROSE) of fine needle aspiration and touch preparations of small biopsies. A well‐executed ROSE procedure can significantly impact the diagnostic quality and appropriate specimen triage of procured biopsy materials. To accommodate the demand for ROSE, telecytology has been increasingly implemented to facilitate ROSE occurring remotely. Telecytology can be categorised based on camera systems including eyepiece system, camera port system and robotic microscope/whole slide image scanner system. Image sharing methods include static images, broadcast only live video streaming, teleconferencing and whole slide image management system. In this review, we will discuss the advantages and disadvantages of each of these systems and deployment considerations.
Restating The Meaning Of UCD And HCD For A New World Of Design Theories

Abstract User-centered design (UCD) or human-centered design (HCD) has become a recognized element of design theory that is gaining increasing prominence in many areas or disciplines related to design. The aim of this paper is to introduce the concept of HCD/UCD in the areas of design so that it contributes to the communication and development of theories in this area. This paper presents a content analysis of HCD/UCD academic literature and demonstrates a lack of agreement on the use of the term ‘user’ or ‘human’: a significant variation of meaning of the terms used, definitions with distinct levels of abstraction, high variation of elements and distinct preference by the acronym usage. A selection of definitions was analyzed and summarized into a framework that considers all levels of abstraction and proposes seven HCD/UCD elements. Finally, recommendations about the use of this framework to bring researchers close to a common understanding about HCD/UCD are presented. This work contributes to bring together knowledge about the theme under a single umbrella and to integrate design theories and methods based on the concept of HCD/UCD. This will facilitate the transdisciplinary and will build up all the knowledge acquired in a single body of knowledge.