Reviewerly/exHarmony huggingface dataset:
Towards Public Integrity Audition for Cloud-IoT Data Based on Blockchain

With the rapidly developing of Internet of Things (IoT), the volume of data generated by IoT systems is increasing quickly.To release the pressure of data management and storage, more and more enterprises and individuals prefer to integrate cloud service with IoT systems, in which the IoT data can be outsourced to cloud server.Since cloud service provider (CSP) is not fully trusted, a variety of methods have been proposed to deal with the problem of data integrity checking.In traditional data integrity audition schemes, the task of data auditing is usually performed by Third Party Auditor (TPA) which is assumed to be trustful.However, in real-life TPA is not trusted as people thought.Therefore, these schemes suffer from the underlying problem of single-point failure.Moreover, most of the traditional schemes are designed by RSA or bilinear map techniques which consume heavy computation and communication cost.To overcome these shortcomings, we propose a novel data integrity checking scheme for cloud-IoT data based on blockchain technique and homomorphic hash.In our scheme, the tags of all data blocks are computed by a homomorphic hash function and stored in blockchain.Moreover, each step within the process of data integrity checking is signed by the performer, and the signatures are stored in blockchain through smart contracts.As a result, each behavior for data integrity checking in our scheme can be traced and audited which improves the security of the scheme greatly.Furthermore, batch-audition for multiple data challenges is also supported in our scheme.We formalize the system model of our scheme and give the concrete construction.Detailed performance analyses demonstrate that our proposed scheme is efficient and practical without the trust-assumption of TPA.
The future of DLL

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Dissipation-enabled resonant adiabatic quantum state transfer: Entanglement generation and quantum cloning

Resonant dissipation-enabled adiabatic quantum state transfer processes between the polarization degrees of freedom of a single-photon wave packet and quantum emitters are discussed. These investigations generalize previous work [Trautmann and Alber, Phys. Rev. A 93, 053807 (2016)] by taking into account the properties of the spontaneously emitted photon wave packet and of nonadiabatic corrections. It is demonstrated that the photonic degrees of freedoms of these adiabatic one-photon quantum state transfer processes can be used for the passive, heralded, and deterministic preparation of Bell states of two material quantum emitters and for realizing a large family of symmetric and asymmetric quantum cloning processes. Although these theoretical investigations concentrate on waveguide scenarios they are expected to be relevant also for other scenarios as long as the processes involved are adiabatic so that the Fourier-limited bandwidth of the single-photon wave packet involved is small in comparison with the relevant dissipative rates.
On the Problem of Synchronization of Virtual and Real Movements for Virtual Reality Systems

Abstract An immersive experience in modern virtual reality systems requires adjusting of various parameters. This report focuses on the problem of virtual representation of real movements of objects in space. Separately, a special class of problems is highlighted: synchronization of visual and dynamic imitation for virtual reality systems and motion platforms. Modern mass-produced virtual reality headsets do not allow effective tracking of human movements on a moving base using standard software. The element base and algorithms necessary for the successful solution of this problem are presented.
Online hotel rating prediction through a dynamic weighted ordered probit model

Hotel reputation might volatile through time due to improvement or deterioration of accommodation facilities, management strategies changes, etc. A recommender system should take this information into consideration in order to provide updated recommendations. The vast majority of recommender systems rely on matrix factorization techniques without explicitly modeling hotel reputation evolution through time. In real life, recommender systems face another challenge, namely data sparsity, which affects strongly prediction accuracy. In this paper, a dynamic weighted ordered probit model is integrated with collaborative Kalman filtering for online hotel rating prediction. The model learns a dynamically evolving optimal standard deviation via a Newton-Raphson type update, modeling the hotel reputation evolution. An accept-reject sampling scheme is applied to compute on the fly efficiently the expected value of an auxiliary truncated normal random variable utilized to predict hotel ratings based on past ratings, which fill in a short window. Such a window can always be filled in, confronting thus data sparsity and scalability issues. Experiments have demonstrated that the proposed model outperforms state-of-the-art techniques. Performance gains are attested to be statistically significant.
Research on Chinese Text Summarization Based on Core Word Attention Mechanism

The neural sequence-to-sequence model provides a feasible new method for abstract text summarization.Because the traditional Seq2Seq neural network model only judges the importance of each vocabulary based on the word meaning information of the vocabulary itself and the current input of the decoder, they can easily reproduce the factual details inaccurately and often repeat themselves. However, we know that the more relevant a certain vocabulary is to the core words of the original text, the higher its importance will be. Therefore, this article proposes a summary generation that integrates the core word attention mechanism based on the Seq2Seq model based on the attention and coverage mechanism. The model (Fusion Core Word Attention Mechanism Model, FCWAM) integrates the core word information of the original vocabulary, that is, the probability information of the word being assigned to each core word of the original text, as a kind of prior information into the traditional attention mechanism, thereby Improve the decoder’s attention to the important information of the original text, and finally use the pointer generation network to alleviate the problem of unregistered words that may appear in the model. The experimental results based on the LCSTS data set show that the addition of core word information improves the accuracy of the generated abstract
Book genre and author’s gender recognition based on titles

The subject of this chapter is the application of automatic taxonomy methods to the corpus of microtexts, consisting of book titles. We test two hypotheses. The first one claims that simply on the basis of a book title one can automatically recognize its genre (writing species). The second assumes the possibility of recognizing the author’s gender on the basis of the book’s title. FastText and word2vec methods were applied. The analyses give a positive (and rather astonishing) result: with properly chosen n-grams more than 70% of titles could be correctly assigned a writing species, while the accuracy of the gender recognition of the author was almost 80%. Both values significantly exceed the levels of random recognition. The research was conducted on the corpus of titles derived from the Polish national bibliography.
A New Paradigm for the Enjoyment and Exploitation of Cultural Heritage Based on Spatial Augmented Reality: The Case of the Ducal Palace of Urbino

Abstract In the last years, museums have begun to apply new technological solutions to manage their exhibits in a more open, inclusive, and creative way, to improve the visitors’ experience to respond to the need to expand the audience. The main goal is to face the increasing competition in an economy referred to as the “Experience Economy”. To this end, Augmented Reality technology seems to represent a good solution for museum guide systems, to improve visitors’ learning and enjoyment. In this context, the present paper proposes a museum guide system based on Spatial Augmented Reality powered by dynamic projection. The paper describes the overall HW and SW system architecture and reports in detail the developed process adopted to design and implement a museum guide and entertainment application, in the context of the “Studiolo of Federico da Montefeltro” in the Ducal Palace of Urbino. A preliminary survey has been carried out, which involved a total of 79 subjects, aimed at investigating the quality of visitor’s experience, aroused by the proposed application, in terms of the “Four Experience Realms” defined by Pine &amp; Gilmore (1998). Results suggest that the proposed application can be used to stage experiences that satisfy the visitors and may help to enable museums into the Experience Economy.
Delay-on-Squash: Stopping Microarchitectural Replay Attacks in Their Tracks

MicroScope and other similar microarchitectural replay attacks take advantage of the characteristics of speculative execution to trap the execution of the victim application in a loop, enabling the attacker to amplify a side-channel attack by executing it indefinitely. Due to the nature of the replay, it can be used to effectively attack software that are shielded against replay, even under conditions where a side-channel attack would not be possible (e.g., in secure enclaves). At the same time, unlike speculative side-channel attacks, microarchitectural replay attacks can be used to amplify the correct path of execution, rendering many existing speculative side-channel defenses ineffective. In this work, we generalize microarchitectural replay attacks beyond MicroScope and present an efficient defense against them. We make the observation that such attacks rely on repeated squashes of so-called “replay handles” and that the instructions causing the side-channel must reside in the same reorder buffer window as the handles. We propose Delay-on-Squash, a hardware-only technique for tracking squashed instructions and preventing them from being replayed by speculative replay handles. Our evaluation shows that it is possible to achieve full security against microarchitectural replay attacks with very modest hardware requirements while still maintaining 97% of the insecure baseline performance.
Blockchain for IoT: A Critical Analysis Concerning Performance and Scalability

The world has been experiencing a mind-blowing expansion of blockchain technology since it was first introduced as an emerging means of cryptocurrency called bitcoin. Currently, it has been regarded as a pervasive frame of reference across almost all research domains, ranging from virtual cash to agriculture or even supply-chain to the Internet of Things. The ability to have a self-administering register with legitimate immutability makes blockchain appealing for the Internet of Things (IoT). As billions of IoT devices are now online in distributed fashion, the huge challenges and questions require to addressed in pursuit of urgently needed solutions. The present paper has been motivated by the aim of facilitating such efforts. The contribution of this work is to figure out those trade-offs the IoT ecosystem usually encounters because of the wrong choice of blockchain technology. Unlike a survey or review, the critical findings of this paper target sorting out specific security challenges of blockchain-IoT Infrastructure. The contribution includes how to direct developers and researchers in this domain to pick out the unblemished combinations of Blockchain enabled IoT applications. In addition, the paper promises to bring a deep insight on Ethereum, Hyperledger blockchain and IOTA technology to show their limitations and prospects in terms of performance and scalability.
Correlation Between Code Smells for Open Source Java Projects

The increasing demands and excessive use of computers and applications worldwide makes it challenging for the application and software developers to deliver the product on time and without faults. Looking at applications built for bugs, and thus minimizing the faults becomes tedious. Different researchers have extensively studied the maintenance of software and prediction of vulnerabilities. Code smells, and bug prediction is one way to find and locate the bugs through smell. These smells are beneficial in bug prediction if the smells are known beforehand. This paper studies the correlation for six code smells compiled for three open source java projects. The study results show that three smells are strongly correlated with code smells, namely God class, Brain method, and Dispersed Coupling.
Enhanced gradient learning for deep neural networks

Deep neural networks have achieved great success in both computer vision and natural language processing tasks. How to improve the gradient flows is crucial in training very deep neural networks. To address this challenge, a gradient enhancement approach is proposed through constructing the short circuit neural connections. The proposed short circuit is a unidirectional neural connection that back propagates the sensitivities rather than gradients in neural networks from the deep layers to the shallow layers. Moreover, the short circuit is further formulated as a gradient truncation operation in its connecting layers, which can be plugged into the backbone models without introducing extra training parameters. Extensive experiments demonstrate that the deep neural networks, with the help of short circuit connection, gain a large margin of improvement over the baselines on both computer vision and natural language processing tasks. The work provides the promising solution to the low-resource scenarios, such as, intelligence transport systems of computer vision, question answering of natural language processing.
Traffic Sign Recognition and Distance Estimation with YOLOv3 model

Due to the expeditious increase in the number of vehicles, there is an increase in the number of road casualties even in a highly sophisticated roadway. This depicts the natural limitation of a human in maintaining Traffic rules. To avoid any lethal circumstance assistive driving vehicles are introduced which consists of systems that guide drivers in different Traffic situations. Traffic sign recognition systems play a crucial role in assistive driving vehicles these systems have been based on characteristics of the sign and two-state detectors due to accuracy and real-time factors systems on these bases are not used for real-time application. In this paper, we present a system that can recognize Traffic signs and their distance from the vehicle in non-ideal lighting as well as in varying climatic conditions. Our work proceeds with the implementation of YOLOv3(deep convolutional network based on end-to-end detection algorithm) used for Traffic sign recognition and segmentation. Training of the model is done with GTSRB dataset and achieves an accuracy of about 98.5% for the recognition task in different real-time scenarios. Furthermore, an efficient Heuristic-based approach has been deployed for estimating the distance between the Traffic sign and the monocular camera(placed in the vehicle) at every instance.
Agile Systems Engineering in Building Complex AI Systems

The process of building a complex Artificial Intelligence (AI) system must address two issues to guarantee its correct functioning: 1) the software engineering errors that are inadvertently introduced during the building of the system; and 2) the inherent uncertainty in finding a solution that generates actionable insights due to the application of AI techniques in building complex and interdependent modules. The first issue is addressed with the help of both a declarative approach to programming and the modularization of many complex algorithms, all in the form of libraries. However, the second issue calls for an incremental, agile-system engineering approach without fully committing to its development path, which is uncertain.In this chapter, we detail a scrum-based agile approach that we pursue at AdventHealth to build intelligent data products in the consumer analytics space to better serve our patients. We make use of natural language processing (NLP) in conjunction with cutting-edge machine learning (ML) and deep learning (DL) techniques to build various data products, including an understanding of consumer needs, their grievances, and how well we serve our consumers. Each sprint of our agile approach typically incorporates formulation of the problem or the revision of a previous formulation, a time-consuming data preparation involving large, noisy, and incomplete data, and analysts’ evaluation of the system to provide feedback. Our overall experience with the agile approach is its quick turn-around time for incremental deliverables, where meeting the requirements of our stakeholders.
Analysis of the Fluctuation of Bank Interest Rate Based on Computer Statistical Model and Machine Learning

In order to improve the effect of bank interest rate volatility analysis, this article combines actual conditions and machine learning algorithms to construct a fluctuation analysis model of bank interest rate based on computer statistical model and machine learning. For the data with system transformation, the data contains stationary and nonstationary processes; so, the power of the standard unit root test is low. This paper therefore proposes a new unit root test method. From demand analysis, system design to system implementation, and testing, advanced software engineering-related ideas are adopted, and the bank’s interest rate management system is designed and implemented in strict accordance with software development-related processes. This paper adopts the modular design idea, classifies the functions to be realized according to their content, and conducts structural verification and performance analysis of the functional modules. Through experimental analysis, we can see that the system model constructed in this paper has certain effects in the analysis of interest rate fluctuations.
Unsupervised Learning for Acoustic Shadowing Artifact Removal in Ultrasound Imaging

One of the most prominent artifacts in the ultrasound (US) imaging is the acoustic shadowing artifact. Acoustic shadowing artifacts appear when the transmitted signal encounters with the structure of high attenuation property results in a drop of transmitted energy for deep region and produce dark area in the US image. To overcome this problem, a time gain compensation (TGC) tool has been used, but it cannot automatically produce an improved result. That is to say, the human intervention is necessary. To alleviate this problem, herein a deep learning based algorithm is proposed for the removal of shadowing artifacts. Since, obtaining a paired dataset for this task is difficult, therefore cycle consistency-based generative adversarial network (CycleGAN) is utilized to train the proposed deep learning model in an unsupervised fashion. The model is trained to process delay-and-sum (DAS) beamformer images as the input to generate shadowing-free patches as the target. The method is evaluated on real in vivo samples and shown to produce noticeably improved quality images.
Text-based CAPTCHA Vulnerability Assessment using a Deep Learning-based Solver

The focus of this work is to test the security offered by Text-based CAPTCHAs. We present different types of CAPTCHAs and a preprocessing and segmentation process to clean noise in CAPTCHA images and crop digits or characters in single images. We present a convolutional neural network architecture trained under several hyperparameters, comparing multiple models with different batch sizes, epochs, and optimizers. We confirmed that using Text-based CAPTCHAs is no longer a secure mechanism for protection because, with simple computer vision techniques and current machine learning algorithms, they can be broken. We achieved a 90.49% accuracy with our model trained with a mix of four datasets and up to 97.10% with one dataset, which is enough to consider these schemes insecure in practice.
Waypoint-Based Global Optimal Path Planning Algorithm for Mobile Robots

Dynamic programming has long been used for optimal path generation. Different from the most research works in this area which discretize the workspace and use cells for path planning, we propose a global optimal path planning method using waypoints. Although the waypoints of a simple environment with a few rectangular obstacles can be pre-defined manually, it is almost impossible to manually define the global optimal waypoints for a complex environment with many obstacles of different shapes. In this paper, we propose a global optimal path planning algorithm using automatically generated waypoints based on dynamic programming. We have shown that the proposed algorithm can find an exact optimal solution or the shortest path to the goal position from any starting point in environments with multiple convex polygon obstacles.
A Course Recommendation System for Moocs Based On Online Learning

Systems that make recommendations One of the most intriguing systems, which allows students to browse or search a big number of possibilities for specific goods related to their interests. Using varied methodologies such as the content-based approach and the collaborative approach, these systems have assisted learners in e-learning in filtering options and selecting information. However, learners have not been totally happy with the offer as a result of these tactics. In this paper, to compensate for the shortcomings of previous approaches, propose a novel clustering-based strategy. The main purpose of this proposed technique is to generate homogeneous groups of learners in order to cover and absorb all of the essential information.
Construction of Circular Convolution Group for Block Data Transmission with High Diversity Order

Perfect sequences (PSs) have long being applied to various communication systems and related areas. This is the first study in literature on constructing a cyclic group <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$G$</tex> defined on the basis of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N$</tex> -point circular convolution operation over a set of normalized PSs, which is called circular convolution group (CCG). The related properties, theorems, and construction of CCG are explored and addressed in this article. We demonstrate an example of CCG application on block data transmission which can achieve high diversity order with implementation efficiency.
Case study of face detection libraries on Raspberry Pi

With the fast development in recent years of micro PC, applications that require of using computer vision techniques have become more and more popular. This paper describes a thoroughly research on four different face detectors using the two most popular computer vision libraries (i.e., OpenCV and Dlib), and compares the detecting speed of those detectors. The face detectors, which are examined in all experiments, include two classic detectors (Haar and HOG based face detector) and two newer, deep neural networks based detectors. Also, four different Raspberry Pi models including Raspberry Pi Zero W, Pi 2, Pi 3, and Pi 4 are put into the experiments of face detecting speed. The results show that the Raspberry Pi 2, Pi 3, and Pi 4 are capable of doing real-time face detection tasks using Haar or HOG based face detector, and all four Raspberry Pi models can be used in practical applications where face detection is required. For other detectors based on deep neural networks, although are not suitable for face related real-time tasks, they are still effectively used in many other face related computer vision tasks.