Reviewerly/exHarmony huggingface dataset:
Distribution of density matrices at fixed purity for arbitrary dimensions

Uniformly sampling quantum density matrices has been demonstrated for a long time [Mezzadri, Notices AMS 54, 592 (2007), \ifmmode \dot{Z}\else \.{Z}\fi{}yczkowski, J. Math. Phys. 52, 062201 (2011)] and is a valuable tool in the analysis and testing of quantum relations (e.g., the tightness of an uncertainty relation); in ranking the performance of various tools to witness entanglement, nonlocality, discord, etc.; and in probing the geometry of fundamental quantum properties (e.g, the boundary between separable and entangled states). While uniformly sampling quantum states has been shown to be quite valuable, there is to our knowledge no such method to sample these states uniformly at a constant amount of mixedness. This challenge becomes increasingly acute as post-selecting a subensemble of states within a narrow range of, say, purities from this fully uniform sampling becomes overwhelmingly inefficient at high dimension. In this work, we demonstrate how to analytically construct the distribution of density matrices at constant purity (the trace of the square of a density matrix) as our mixedness measure. With this technique at our disposal: we provide analytic formulas for the cumulative radial distribution functions for the uniform distribution of density matrices at constant purity for arbitrary total dimension $N$, with closed form expressions up to $N=4$, and perform numerical integration to analyze the statistics of these uniform-purity distributions at higher dimensions (up to $N=100$). Moreover, we use our new capabilities to: compare different measures of entanglement and nonclassicality, including Wootter's concurrence, the logarithmic negativity and quantum discord, as a function of purity at dimension $N=4$ (two qubits); to find the probability distribution of purities of marginal density matrices from a uniform distribution of joint pure states (and thus the entanglement statistics of this uniform ensemble); and to numerically test a conjectured uncertainty relation between quantum mutual information and the sum of classical mutual informations in complementary bases.
Modular orders on defaults in formal argumentation

Abstract Default logic and formal argumentation are paradigmatic methods in the study of nonmonotonic inference. Defeasible information often comes in different strengths stemming from different degrees of reliability in epistemic applications or from varying strengths of authorities issuing norms in deontic applications. In both paradigms, methods have been developed to deal with prioritized knowledge bases and normative systems. Questions of comparability of these methods therefore naturally arise. Argumentation theory has been developed with a strong emphasis on unification. It is therefore a desideratum to obtain natural representations of various approaches to (prioritized) default logic within frameworks of structured argumentation, such as ASPIC. Important steps in this direction have been presented in Liao et al. (2016, 2018). In this work, we identify and address some problems in earlier translations, we broaden the focus from total to modular orderings of defaults and we consider non-normal defaults.
D2D-assisted user-centric adaptive video transmission in next generation cellular networks

Next-generation cellular networks need to provide seamless connectivity with higher data rates, increased capacity, and enhanced network coverage. As multimedia service demands in various heterogeneous devices grow rapidly compared to the underlying network’s capacity and bandwidth, the adaptation in multimedia streaming services is essential for providing satisfactory Quality of Experience (QoE). This paper develops a Device-to-Device (D2D)-assisted Utility-based Adaptive Multimedia (video) Streaming scheme (UAMS) using D2D communication in a 5th Generation (5G) cellular network where low-battery users may extend their streaming duration by spending lower reception energy with the help of D2D-assisted communication. The adaptation algorithm considers four utility functions: quality, power consumption, packet error ratio, and remaining battery of the user devices to adapt the bitrate dynamically and augment viewers’ experience. We formulate an optimization problem to maximize the joint utility function to provide the best adaptive multimedia content selected for transmission to the end-users either directly or via D2D Relay Nodes (DRNs) in every scheduling interval. We use a graph theoretic approach for choosing the best DRNs. Extensive simulations show the efficacy of the proposed scheme in terms of saved battery energy, churn rate, and QoE metrics compared to a few well-known existing schemes in the literature that do not use D2D communication.
An exploration of instructional behaviors of a teacher in a mobile learning context

Mobile learning has been frequently adopted, but it is necessary to explore how teachers face the challenges. In this study, an experienced teacher's teaching behaviors were collected and analyzed through sequential pattern analysis. Also, an interview with the teacher was conducted. The result showed that the teacher put a great deal of effort into creating a positive learning atmosphere. His teaching behavioral pattern in mobile learning was more scattered than that in conventional teaching mode. This might be the biggest challenge for novice teachers. Finally, this study found that the students' basic digital skills were also a vital element.
Eigen-Entropy: A metric for multivariate sampling decisions

Sampling is a technique to help identify a representative data subset that captures the characteristics of the whole dataset. Most existing sampling algorithms require distribution assumptions of the multivariate data, which may not be available beforehand. This study proposes a new metric called Eigen-Entropy (EE), which is based on information entropy for the multivariate dataset. EE is a model-free metric because it is derived based on eigenvalues extracted from the correlation coefficient matrix without any assumptions on data distributions. We prove that EE measures the composition of the dataset, such as its heterogeneity or homogeneity. As a result, EE can be used to support sampling decisions, such as which samples and how many samples to consider with respect to the application of interest. To demonstrate the utility of the EE metric, two sets of use cases are considered. The first use case focuses on classification problems with an imbalanced dataset, and EE is used to guide the rendering of homogeneous samples from minority classes. Using 10 public datasets, it is demonstrated that two oversampling techniques using the proposed EE method outperform reported methods from the literature in terms of precision, recall, F-measure, and G-mean. In the second experiment, building fault detection is investigated where EE is used to sample heterogeneous data to support fault detection. Historical normal datasets collected from real building systems are used to construct the baselines by EE for 14 test cases, and experimental results indicate that the EE method outperforms benchmark methods in terms of recall. We conclude that EE is a viable metric to support sampling decisions.
Neural referential form selection: Generalisability and interpretability

In recent years, a range of Neural Referring Expression Generation (REG) systems have been built and they have often achieved encouraging results. However, these models are often thought to lack transparency and generality. Firstly, it is hard to understand what these neural REG models can learn and to compare their performance with existing linguistic theories. Secondly, it is unclear whether they can generalise to data in different text genres and different languages. To answer these questions, we propose to focus on a sub-task of REG: Referential Form Selection (RFS). We introduce the task of RFS and a series of neural RFS models built on state-of-the-art neural REG models. To address the issue of interpretability, we probe these RFS models using probing classifiers that consider information known to impact the human choice of Referential Forms. To address the issue of generalisability, we assess the performance of RFS models on multiple datasets in multiple genres and two different languages, namely, English and Chinese.
Visual dubbing pipeline with localized lip-sync and two-pass identity transfer

Visual dubbing uses visual computing and deep learning to alter the lip and mouth articulations of the actor to sync with the dubbed speech. It has the potential to greatly improve the content generated from the dubbing industry. Quality of the dubbed result is primary for the industry. An important requirement is that visual lip sync changes be localized to the mouth region and not affect the rest of the actor’s face or the rest of the video frame. Current methods can create realistic looking fake faces with expressions. However, many fail to localize lip sync and have quality problems such as identity loss, low-res, blurs, face skin feature or colour loss, and temporal jitter. These problems mainly arise because end-to-end training of networks to correctly disentangle these different visual dubbing parameters (pose, skin colour, identity, lip movements, etc.) is very difficult to achieve. Our main contribution is a new visual dubbing pipeline, in which, instead of end-to-end training we apply incrementally different disentangling techniques for each parameter. Our pipeline is composed of three main steps: pose alignment, identity transfer and video reassembly. Expert models in each step are fine-tuned for the actor. We propose an identity transfer network with an added style block, which with pre-training is able to decouple face components, specifically identity and expression, and also works with short video clips like TV ads. Our pipeline also includes novel stages related to temporal smoothing of the reenacted face, actor specific super resolution to retain fine facial details, and a second pass through the identity transfer network for preserving actor identity. Localization of lip-sync is achieved by restricting changes in the original video frame to just the actor’s mouth region. The results are convincing, and a user survey also confirms their quality. Relevant quantitative metrics are included.
Governing digital crisis responses: platform standards and the dilemma of COVID-19 contact tracing

In response to the impact of the SARS-CoV-2 (COVID-19) pandemic, various developers turned to smartphone-based contact tracing to address the challenges of manual tracing. Due to the presence of network effects, i.e., the effectiveness of contact tracing applications increases with the number of users, information technology standards were critical to the technology's success. The standardization efforts in Europe led to a variety of trade-offs concerning the choice of an appropriate technological architecture due to the contradictory tensions resulting from the dualism between the need for contact tracing data to contain the pandemic and the need for data minimization to preserve user privacy. Drawing predominantly on the software platform and standards literature, we conduct an interpretive case study to examine the emergence and consequences of this multi-layered decision situation. Our findings reveal how Google and Apple were able to limit the individual leeway of external developers, thereby effectively resolving the European standards war. Furthermore, we identify and discuss the various short-term and long-term trade-offs associated with the standardization of contact tracing applications and translate our findings into recommendations for policy makers with respect to future crisis situations. Specifically, we propose a strategy grounded in our data that enables responsible actors to make goal-oriented and rapid decisions under time constraints.
Regulation of data breach publication: the case of US healthcare and the HITECH act

Abstract Data breaches continue to plague most industries but especially the healthcare sector. While previous studies have aimed at assessing the financial implications arising from data breaches, none of them focused on the healthcare sector. This study of market reactions following data breaches fills that gap. Federal disclosure requirements of data breaches within the healthcare sector allow for our comprehensive study. Using multivariate regression analysis, our findings show the high specificity with which investors react to the announcement of data breaches.
Privacy‐preserving enhanced dummy‐generation technique for location‐based services

Summary Location‐based services (LBS) has become an intrinsic part of our everyday life. However, the flexibility and convenience provided by LBS are at the cost of user privacy since untrusted LBS server can leak private information of users. To overcome the privacy issues observed in LBS, a novel dummy‐generation based privacy preservation technique is proposed in this article. The proposed dummy‐generation technique is a circle‐based technique which generates dummy locations in the circle area and is effective against center‐of‐anonymized spatial region attack, map‐matching attack, and location‐homogeneity attack. Additionally, an edge computing enabled framework is proposed, which can be used in the IoT environment. The edge computing enabled framework helps in handling the resource poverty issues of the service requesting devices and provides the low latency solution. The security analysis of our proposed dummy‐generation technique reflects that the proposed technique is resilient to specific attacks for different adversary attack models. The results obtained through simulations suggest that our technique performs better than the pre‐existing techniques.
Implementation of Chaotic Encryption Architecture on FPGA for On-Chip Secure Communication

Chaos is an interesting phenomenon for nonlinear systems that emerges due to its complex and unpredictable behavior. With the escalated use of low-powered edge-compute devices, data security at the edge develops the need for security in communication. The characteristic that Chaos synchronizes over time for two different chaotic systems with their own unique initial conditions, is the base for chaos implementation in communication. This paper proposes an encryption architecture suitable for communication of on-chip sensors to provide a POC (proof of concept) with security encrypted on the same chip using different chaotic equations. In communication, encryption is achieved with the help of microcontrollers or software implementations that use more power and have complex hardware implementation. The small IoT devices are expected to be operated on low power and constrained with size. At the same time, these devices are highly vulnerable to security threats, which elevates the need to have low power/size hardware-based security. Since the discovery of chaotic equations, they have been used in various encryption applications. The goal of this research is to take the chaotic implementation to the CMOS level with the sensors on the same chip. The hardware co-simulation is demonstrated on an FPGA board for Chua encryption/decryption architecture. The hardware utilization for Lorenz, SprottD, and Chua on FPGA is achieved with Xilinx System Generation (XSG) toolbox which reveals that Lorenz’s utilization is ~9% lesser than Chua’s.
Privacy Protected Modified Double Ratchet Algorithm for Secure Chatbot Application

Our world has been revolutionized as a result of digitalization. Large amounts of data are regularly generated as a result of the well-known World Wide Web, various social networking sites, and an infinite number of apps. Personal assistants are proof of the growing popularity of chatbots. Conversational AI developers need to be aware of any potential threats or flaws in existing designs, as well as how hackers may exploit those flaws, if they want their creations to be secure. The concept of “chatbot” refers to a piece of software that is executed on a computer and has the ability to engage in conversation with human users while also processing the content of such talks. Users are also able to connect and communicate with digital gadgets via the use of chatbots (such as smartphones and tablets). According to the authors, it is also referred to as “one of the most sophisticated and promising representations of interactions between people and robots.” Its primary function is to assist students by responding to their inquiries. The chatbots that are currently available are not entirely secure, and it is possible that they could provide opportunities for information flowing through the chatbot interface to be accessed by cybercriminals or hackers. As a direct consequence of this, the chatbot will be made secure by utilizing authentication (session) timeouts in combination with encryption mechanisms. During the transport of data from one end system to another, encryption is used to prevent outsiders from gaining access to the information being sent. The data is encrypted as soon as it leaves the machine of the sender, and the only person who will be able to decrypt it is the recipient to whom it is intended to be sent. It is probable to set a time limit on how long an authorized user may remain “logged in,” which prevents cyber attackers from being able to guess their way into someone else’s protected account by using authentication (session) timeout. “In this research paper, the Double Ratchet algorithm is modified with Paillier Cryptosystems. Our Privacy Protected Modified model is secure in key as well as message data through network attacks.
A design of an autonomous mobile robot base with omnidirectional wheels and plane-based navigation with Lidar sensor

This paper proposes the design of a base for an autonomous mobile robot that plans optimal routes for its movement in a known environment. The proposal describes both the hardware and software of the robot.In the case of hardware, sensors are used to facilitate obtaining data from the environment and actuators that allow the movement of the robot. Furthermore, the Raspberry PI 4B+ is used as a computer, this overseeing verifying the values of the sensors and sending data to the actuators.On the other hand, for navigation an algorithm is used that generates possible routes, compares them, and chooses the most efficient one to allow optimal movement. This was demonstrated in the tests carried out, where a high efficiency was obtained. This uses as base the map of the chosen space. The purpose of this project is to design a base of a mobile robot, with an efficient autonomous navigation that avoids static obstacles from a base point to different points established by the user, which will be saved for future uses. The design is modeled in Autodesk Inventor 2020 and Python programming language is used, through which validation tests were carried out, where the proposed routes are compared and the one with the least number of steps is chosen.
OrderlessFile

Cloud storage has gained popularity as an affordable and available cloud-based file storage. However, despite its apparent advantages, clients must rely on cloud providers to safely and securely store the files. A few blockchain-based file storage systems have been introduced as trusted alternatives. However, their limited scalability and use of off-chain storage restrict their applicability. To address these issues, we introduce OrderlessFile, a CRDT-based on-chain permissioned blockchain for file storage.
Lightweight helmet detection algorithm based on improved YOLOv5

In the construction process, wearing safety helmet can effectively reduce the injury caused by falling objects and hard objects. In order to detect the wearing of safety helmet during the construction process, a lightweight safety helmet wearing detection algorithm YOLOv5-CS is proposed and applied to the embedded equipment at the construction site. The algorithm is based on the improvement of YOLOv5 target detection algorithm. The improved CAShuffle module is used to form the backbone feature extraction network, which reduces the size of the original algorithm and ensures the detection accuracy of the algorithm; DIoU as the loss function, which makes the algorithm more conducive to detecting small targets such as hard hats; modify dataset, add the dataset on the basis of the original dataset and mark the safety helmet and the anchor frame of the person separately, and judge whether the worker wears the safety helmet by analyzing positional relationship of the identified anchor frame, use the algorithm combining K-means and genetic algorithm to predict the anchor frame of the dataset, and use Mosaic algorithm to enhance the data. The results show that the parameter quantity of YOLOv5-CS algorithm is 2.4M, less than one third of YOLOv5; The average precision decreased by only 4.2%; 640x640 pictures can be recognized at 31 frames per second on Jetson Nano, a embedded platform.
Deep <scp>auto‐encoder</scp> based clustering algorithm for <scp>graph‐based</scp> web page recommendation system

Summary Web‐page recommendation system (RS) plays a major role in smart web systems. The core technology is the RS, which helps users find their interests or discover item preferences. With the rapid proliferation of information technology, the present era witnessed a remarkable growth in the collection and generation of web data. Every day, it is difficult to project the appropriate information to the user and adds severe complexity to the decision making procedure. Moreover, the existing RS techniques have limitations like huge computation time, high computational cost, over specialization, and sparsity problems. This work presents an effective graph‐based web page recommendation model to overcome these difficulties. Initially, the input data is preprocessed utilizing Natural Language Tool Kit executed in Python. The snapshot‐based dynamic graph model obtains the user‐web page relationship. Weight calculation of user‐item and the user‐user edge is also processed with this graph model. The user‐user edge similarity is used to obtain the user preference vector. Then, unsupervised‐deep auto encoder based density‐based clustering algorithm is proposed to cluster the user preference vector, which groups the visited web page details into k clusters. The web pages are recommended based on the highest score calculated by the page ranking method. Finally, the topmost visited web pages are recommended to web users based on the ranking score. “All the news”, MSNBC, and Weblog datasets are taken for evaluation. The proposed method is evaluated with respect to standard measures like F1 measure, accuracy, recall, precision, and root mean square error. Compared with other existing methods, the proposed technique achieved the highest accuracy, 97.99% for All the news datasets, 93.03% for the MSNBC database, and 90.07% for the Weblog database.