Reviewerly/exHarmony huggingface dataset:
Efficient Share Generator for Slicing and Securely Retrieving the Cloud-Hosted Heterogeneous Multimedia Data

Recently, the security of heterogeneous multimedia data becomes a very critical issue, substantially with the proliferation of multimedia data and applications. Cloud computing is the hidden back-end for storing heterogeneous multimedia data. Notwithstanding that using cloud storage is indispensable, but the remote storage servers are untrusted. Therefore, one of the most critical challenges is securing multimedia data storage and retrieval from the untrusted cloud servers. This paper applies a Shamir Secrete-Sharing scheme and integrates with cloud computing to guarantee efficiency and security for sensitive multimedia data storage and retrieval. The proposed scheme can fully support the comprehensive and multilevel security control requirements for the cloud-hosted multimedia data and applications. In addition, our scheme is also based on a source transformation that provides powerful mutual interdependence in its encrypted representation—the Share Generator slices and encrypts the multimedia data before sending it to the cloud storage. The extensive experimental evaluation on various configurations confirmed the effectiveness and efficiency of our scheme, which showed excellent performance and compatibility with several implementation strategies.
Firefly algorithm based on intelligent single particle learning

The particles in the population of the firefly algorithm learn from each other using an all-attractive model, and the algorithm has a strong ability of social learning and global detection. However, the algorithm ignores the role of the global optimal particle, resulting in weak self-learning and local development ability of the algorithm. Therefore, this paper proposes an intelligent single-particle learning firefly algorithm. The algorithm divides the iterative process into two stages, the first stage adopts the standard firefly algorithm to evolve; in the second stage, the intelligent single particle optimisation algorithm is used to optimise the global optimal particle. The iterative process in the first stage ensures the sociality and global detection ability of the particle, and the second stage enhances the ability of self-learning and local development of the algorithm. The experimental results show that the algorithm in this paper has better performance.
Comparison of Hidden Markov Model and Recurrent Neural Network in Automatic Speech Recognition

Understanding human speech precisely by a machine has been a major challenge for many years.With Automatic Speech Recognition (ASR) being decades old and considering the advancement of the technology, where it is not at the point where machines understand all speech, it is used on a regular basis in many applications and services. Hence, to advance research it is important to identify significant research directions, specifically to those that have not been pursued or funded in the past. The performance of such ASR systems, traditionally build upon an Hidden Markov Model (HMM), has improved due tothe application of Deep Neural Networks (DNNs). Despite this progress, building an ASR system remained a challenging task requiring multiple resources and training stages. The idea of using DNNs for Automatic Speech Recognition has gone further from being a single component in a pipeline to building a system mainly based on such a network.This paper provides a literature survey on state of the art researches on two major models, namely Deep Neural Network - Hidden Markov Model (DNN-HMM) and Recurrent Neural Networks trained with Connectionist Temporal Classification (RNN-CTC). It also provides the differences between these two models at the architectural level.
Publisher Correction: Determining eigenstates and thermal states on a quantum computer using quantum imaginary time evolution

An amendment to this paper has been published and can be accessed via a link at the top of the paper.
A new parallel DSP hardware compatible algorithm for noise reduction and contrast enhancement in video sequence using Zynq-7020

Various video processing applications such as liquid crystal display processing, high quality video photography, terrestrial video recording and medical imaging systems requires robust noise reduction and contrast enhancement technique which provides visually pleasing for real time implementation novel hardware architecture has been designed using look-up-table (LUT) acceleration approach which helps achieve high speed processing. Until now, a lot of researchers have worked on noise removal and contrast enhancement of digital videos, but the developed algorithms works with only some verity of noises and failed to produce desirable results for various types of distortions and real time implementation is still remaining a challenge. Hence, appropriate filter needs to be designed which address both kinds of errors. In this paper, adaptive trilateral filter has been designed for noise reduction the results are measured using qualitative and quantitative analysis which has aided in better utilisation of hardware for real time implementation. The experimental results show that the proposed algorithm provides a frame rate of 40 fps on an average and has a resolution of 720 × 576. The proposed algorithm was implemented on ZedBoard Znyq-7020 development kit by Xilinx.
An Integral Characterization of the Dirichlet Process

We give a new integral characterization of the Dirichlet process on a general phase space. To do so, we first prove a characterization of the nonsymmetric Beta distribution via size-biased sampling. Two applications are a new characterization of the Dirichlet distribution and a marked version of a classical characterization of the Poisson–Dirichlet distribution via invariance and independence properties.
Using LARA for language learning: a pilot study for Icelandic

This paper presents a brief overview of LARA (Learning And Reading Assistant), an open source online tool that has been under development since summer 2018. LARA currently contains a corpus of about 25 texts in ten languages and a crowdsourcing model used to expand the corpus. The central goal is to provide support for improving second language (L2) reading comprehension. The focus here is on the development of Icelandic content and its use during pilot testing amongst adult L2 learners of Icelandic. Preliminary feedback from users, while mostly positive, contained suggestions on how the tool might be improved.
Classification of Ancient Roman Coins by Denomination Using Colour, a Forgotten Feature in Automatic Ancient Coin Analysis

Ancient numismatics, that is, the study of ancient currencies (predominantly coins), is an interesting domain for the application of computer vision and machine learning, and has been receiving an increasing amount of attention in recent years. Notwithstanding the number of articles published on the topic, the variety of different methodological approaches described, and the mounting realisation that the relevant problems in the field are most challenging indeed, all research to date has entirely ignored one specific, readily accessible modality: colour. Invariably, colour is discarded and images of coins treated as being greyscale. The present article is the first one to question this decision (and indeed, it is a decision). We discuss the reasons behind the said choice, present a case why it ought to be reexamined, and in turn investigate the issue for the first time in the published literature. Specifically, we propose two new colour-based representations specifically designed with the aim of being applied to ancient coin analysis, and argue why it is sensible to employ them in the first stages of the classification process as a means of drastically reducing the initially enormous number of classes involved in type matching ancient coins (tens of thousands, just for Ancient Roman Imperial coins). Furthermore, we introduce a new data set collected with the specific aim of denomination-based categorisation of ancient coins, where we hypothesised colour could be of potential use, and evaluate the proposed representations. Lastly, we report surprisingly successful performances which goes further than confirming our hypothesis—rather, they convincingly demonstrate a much higher relevant information content carried by colour than even we expected. Thus we trust that our findings will be noted by others in the field and that more attention and further research will be devoted to the use of colour in automatic ancient coin analysis.
MPEG Video Coding: MPEG-1, 2, 4, and 7

In this chapter, we examine the ideas behind the MPEG standards, starting with MPEG-1, -2, and then MPEG-4, and 7. In MPEG-1 and -2, bidirectional search for motion vectors is introduced. Interlaced video and high-definition TV (HDTV) are supported in MPEG-2. Moreover, it supports various scalable coding methods such as SNR, spatial, temporal, and their combinations. MPEG-4 studied the issue of video coding based on video objects, and MPEG-7 emphasized video representation and retrieval. Although the visual object-based video representation and compression approach developed in MPEG-4 and 7 have not been commonly used in current popular standards such as H.264, H.265, and H.266, it has great potentials to be adopted in the future when the necessary Computer Vision technology for automatic object detection and recognition becomes more readily available.
From the web of bibliographic data to the web of bibliographic meaning: structuring, interlinking and validating ontologies on the semantic web

Bibliographic data sets have revealed good levels of technical interoperability observing the principles and good practices of linked data. However, they have a low level of quality from the semantic point of view, due to many factors: lack of a common conceptual framework for a diversity of standards often used together, reduced number of links between the ontologies underlying data sets, proliferation of heterogeneous vocabularies, underuse of semantic mechanisms in data structures, "ontology hijacking" (Feeney et al., 2018), point-to-point mappings, as well as limitations of semantic web languages for the requirements of bibliographic data interoperability. After reviewing such issues, a research direction is proposed to overcome the misalignments found by means of a reference model and a superontology, using Shapes Constraint Language (SHACL) to solve current limitations of RDF languages.
The Human Factor

This article presents a cross spectrum of issues where the cyber domain is impacted by human decision making. Technical efforts and solutions are not enough. Therefore, until personal awareness of the cyber domain improves, we will be no closer to solving this great challenge of our time.
ReCPE: A PE for Reconfigurable Lightweight Cryptography

The Internet-of-Things has given rise to an over-whelming number of resource-constrained devices which must communicate securely with a core server. Due to the variability in processing power among these devices, one lightweight crypto-graphic (LWC) algorithm cannot be standardized. This creates a problem for fog and cloud architectures, where a central server, is subsequently required to support many ciphers. Compounding this problem, LWC ciphers tend to require large numbers of rounds to achieve high security levels, thus occupying the server for unacceptable lengths of time. To minimize LWC overhead, we propose a novel parallel mapping of LWC ciphers and ReCPE, a reconfigurable, lightweight processing element (PE) for use in hardware security modules (HSM) and array processors such as smartNICs. The proposed design was synthesized for both FPGA and ASIC implementations. We validate the ReCPE architecture by comparing it with a baseline array processor and custom field programmable gate array (FPGA) LWC accelerators that use dynamic reconfiguration. The ReCPE architecture is shown to accelerate cryptographic processing by 2× when compared to a baseline PE, with 30% and 60% increases in logic and registers, respectively. Furthermore, we achieve a 50× improvement in dynamic reconfiguration environments when compared to custom FPGA accelerators.
Survey Paper: Image Quality Assessment

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Horizontal Feature Pyramid Network for Object Detection in UAV Images

Feature extraction for object detection in UAV images is of great importance. In this context, a method named Horizontal Feature Pyramid Network (HFPN) is proposed, aiming at generating abundant features from the original Feature Pyramid Network (FPN). Specifically, we enhance the entire feature hierarchy with multiple stages of simple convolution and channel-wise addition operations, which enrich the classification and location information, and reduce the amount of computation. The detection accuracy on UAV image dataset VisDrone is improved with the proposed method.
On the Impact of COVID-19 on Subjective Digital Media Quality Assessment

The COVID-19 pandemic has induced dramatic effects in all areas of society worldwide. It has led to drastic restrictions on academic exchanges and caused research projects to be put on hold. The need to maintain social distancing has severely affected the research communities that rely on in-person studies. In particular, Quality of Experience (QoE) research relies heavily on user studies and in-person subjective tests as means of obtaining ground truths for system designs. In this position paper, we focus on the impact of COVID-19 on conducting subjective tests for digital media quality assessment. An overview of the related discussions that take place in associated research communities is provided. A number of challenges are posed in terms of hygiene, ethics, standardization, and research quality issues. Opportunities for consolidating QoE research under COVID-19 and beyond are put forward, including alternative experimental designs and adaptation of research methodologies. Numerous enablers are suggested and discussed, revealing various avenues that can be followed to assist in conducting subjective tests under pandemic conditions. We believe that this position paper will be helpful for researchers and practitioners relying on in-person studies to keep abreast about the potential measures that support coming out of the COVID-19 pandemic strong and being prepared for future endeavors.
Classification: a Tour of the Classics

The goal of this chapter is to present some of the more classical techniques for classification. These methods are a must for any newcomer in the field. Bayesian decision theory is first reviewed and the concepts of discriminant functions and decision surfaces are introduced. Then, minimum distance classifiers are presented as a special instance of the Bayesian classification. The naive Bayes classifier is discussed and the design of linear models for classification are presented, including logistic regression and Fisher's linear discriminant method. Then, decision trees are introduced. The technique of combining classifiers is discussed, and the AdaBoost algorithm and the method of boosted trees are reviewed.
Digital signatures

A digital signature algorithm refers to a standard for digital signatures, which is based on the algebraic properties of discrete logarithm problem and modular exponentiations based on public-key cryptosystems principal. Digital signatures are work on the principle of two mutually authenticating cryptographic keys, i.e., a public key and a private key. In this chapter, we have discussed the digital signature algorithm, elliptic curve digital signature algorithm (ECDSA), and Edward curve digital signature algorithm (EdDSA). With this, the detailed working and cryptographic functionality of these algorithms are described.
Introduction

The introduction gives some context about what quantum machine learning is, how it got established as a sub-discipline of quantum computing and which higher level approaches have been adopted. We then give a first taste of what it means to learn from data with quantum computers by working through a toy example based on a very small interference circuit.
Recognition of parasite eggs in microscopic medical images based on YOLOv5

Parasitosis is a disease caused by parasites invading the human body. Because of the different species and parasitic sites, it causes different pathological changes and clinical manifestations, and also causes other complications, which is harmful to human health. In clinical medicine, the diagnosis of parasitic diseases is mostly through etiological diagnosis, that is, through the detection of whether there are parasitic eggs in human feces. The diagnosis and treatment of parasitic diseases is a very important part of clinical medicine. At present, the recognition and classification of parasite eggs in human fecal microscopic images are mainly based on manual processing and machine learning, which are inefficient and easily affected by subjective factors, while machine learning can not deal with complex and changeable fecal environment. Here, an automatic recognition algorithm based on YOLOv5 for parasite eggs in fecal microscopic medical images is proposed. Experimental results show that the average accuracy of the model is 0.994 in our test set. In addition, the calculation time of each human fecal microscopic image under GPU is less than 25 ms, and the algorithm has higher accuracy and faster speed than the traditional machine learning algorithm. As such, it will help advance the etiological diagnosis of parasitic diseases and the development of therapeutic drugs.
Confidentiality

After Reading This Chapter, You Will: Know what confidentiality entails and why it is important in research Become familiar with techniques of securing confidentiality Understand the difference between anonymity and confidentiality Be able to identify breaches of confidentiality
Accurate single image depth detection using multiple rotating point spread functions

In this article we present the simulation and experimental implementation of a camera-based sensor with low object-space numerical aperture that is capable of measuring the distance of multiple object points with an accuracy of 8.51 µm over a range of 20 mm. The overall measurement volume is 70 mm × 50 mm × 20 mm. The lens of the camera is upgraded with a diffractive optical element (DOE) which fulfills two tasks: replication of the single object point to a predefined pattern of K spots in the image plane and adding a vortex point spread function (PSF), whose shape and rotation is sensitive to defocus. We analyze the parameters of the spiral phase mask and discuss the depth reconstruction approach. By applying the depth reconstruction to each of the K replications and averaging the results, we experimentally show that the accuracy of the reconstructed depth signal can be improved by a factor of up to 3 by the replication approach. This replication method (also called multipoint method) not only improves accuracy of depth reconstruction but also of lateral position measurement. Therefore, the presented concept can be used as a single camera 3D position sensor for multiple points with high lateral as well as depth resolution.
AI Recognition Method of Pronunciation Errors in Oral English Speech with the Help of Big Data for Personalized Learning

At present, there is a lack of careful consideration in the judgment process of pronunciation errors in many English speeches. These pronunciation errors will create a great impact on personalized learning. The process of creating a data set for errors is also not an easy work. On considering the above obstacle, an artificial intelligent recognition method of pronunciation errors in English speeches for personalized learning along with big data is proposed. This method takes the average pronunciation level of standard speech as the basis of pronunciation error judgment, and judges the pronunciation and application of words such as speed, pronunciation, semantics, etc. In the Hidden Markov Model (HMM) modelling method of speech recognition, Viterbi algorithm and improved posterior probability algorithm are implemented to recognize student’s vocalization instinctively. Through the segmentation and scoring of basic units, English learners are provided with reliable pronunciation information feedback, correct pronunciation errors and give corresponding feedback according to the judgment results. The innovation outcome establishes that the intelligent recognition method for personalized learning can efficiently diminish the error rate and enhance the accuracy of error detection. Let the artificial intelligence (AI) correct English learner’s pronunciation errors intelligently.
Sentiment Analysis using a Machine Learning Approach in Python

Every day, people all over the world use social networking sites to express their thoughts on a broad range of issues. amongst the most well-known social networking sites is “Facebook” for exchanging messages with friends and family. People express their ideas, beliefs and experiences about circumstance. This research intends to create a prototype that analyzes people's attitudes towards the social difficulties of women, a topic that is becoming more relevant in many nations. Using a Facebook scraper written in Python, we were able to collect a dataset of Facebook data, which we subsequently cleaned up using the nltk toolkit. Machine learning techniques and approaches are used to study the feelings of individuals. It is possible in Python to classify each Facebook post as either good, negative, or neutral, depending on the polarity of its thoughts. The hashtags #Women and #MeToo were used to collect data. According to the results of the research, women's experiences, views, and concerns are more often expressed using the hashtag than those of males. The model was built and tested using a variety of machine learning techniques. The accuracy, recall, and f1-score of these models were evaluated using a variety of testing criteria. Furthermore, the performance of each model is compared to each other.
Assessing individual contributions to software engineering projects: a replication study

Background and Context Assessing team members’ indivdiual contributions to software development projects poses a key problem for computing instructors. While instructors typically rely on subjective assessments, objective assessments could provide a more robust picture. To explore this possibility, In a 2020 paper, Buffardi presented a correlational analysis of objective metrics and subjective metrics in an advanced software engineering project course (n= 41 students and 10 teams), finding only two significant correlations.Objective To explore the robustness of Buffardi’s findings and gain further insight, we conducted a larger scale replication of the Buffardi study (n = 118 students and 25 teams) in three courses at three institutions.Method We collected the same data as in the Buffardi study and computed the same measures from those data. We replicated Buffardi’s exploratory, correlational and regression analyses of objective and subjective measures.Findings While replicating four of Buffardi’s five significant correlational findings and partially replicating the findings of Buffardi’s regression analyses, our results go beyond those of Buffardi by identifying eight additional significant correlations.Implications In contrast to Buffardi’s study, our larger scale study suggests that subjective and objective measures of individual performance in team software development projects can be fruitfully combined to provide consistent and complementary assessments of individual performance.