Reviewerly/exHarmony huggingface dataset:
HEVC improved performance with end-to-end super resolution

High Efficiency Video Coding (HEVC), also known as H.265 and MPEG-H part2 is the state of art video encoder standard developed by the Joint Collaborative Team on Video Coding (JCT-VC). It achieves up to 50% better data compression at the same level of video quality than its predecessor (H.264-AVC). One of the main challenges of the HEVC coding is its overall performance as the improved quality and bit rate of the standard coding comes with significant performance overhead. This motivates significant research to reduce the overall complexity. In this paper, we explore the effect of applying down sampling and up sampling of coded video on the performance of the HEVC. The down sampling is applied to all video frames before the encoder process begins and the up sampling is applied to all the video frames after the decoding process is completed. We use an average filter in down sampling, and machine learning based network (SRCNN) for the up sampling. In contrast to other methods, the down sampling and up sampling are applied on frame level outside the encoding/decoding processes and not in block level. Our experiments show the down sampling and up sampling can improve the HEVC encoding/decoding performance by up to 50% in some sequences with limited impact on the output encoded bit rate and decoded video quality. The performance comparison is done using different quantization values.
Bidirectional Octonion Long-Short Term Memory Recurrent Neural Networks for Speech Recognition

Over the few decades onwards all the researchers got marvelous attention on machine learning due to a lot of applications in different fields like image processing, speech processing, etc. Automatic Speech Recognition (ASR) is an application of speech processing that achieved tremendous results due to the usage of recurrent neural networks (RNN). A unique type of recurrent neural network is long short-term memory (LSTM), to keeps away from the problem of long term dependencies. In this paper, multidimensional octonion algebra is used to process the input entities with multidimensions efficiently, when compared to real-valued models octonion numbers and octonion neural networks solve many tasks with less learning parameters. We propose a new octonion value long-short term memory (OVLSTM) to efficiently represent long-term dependencies among features in speech sequences prediction. The TIMIT dataset taken for experiments for speech recognition and results are compared with QLSTMS and LSTMs, OVLSTM with less learning parameters reaches better results.
The Challenges of Creating Design Requirements for Products for People with Dementia

With an ever-growing number of people with dementia, no cure, treatment or effective prevention effort is available to bring medical relief to deal with this condition. Therefore, efforts should be made to support people to live well with dementia and design can contribute to this by the creation of better care services, products and environments. A European research and implementation project was set up to tackle this, with the aim to promote and support the development and quality of products designed to enhance the daily lives and care of people with dementia, including the creation of a certification quality mark. In order to set up that certification quality mark, a series of requirements for ‘good’ design for people with dementia need to be developed. This has presented several challenges due to the diversity and complexity inherent to dementia, as well as to the wide range of products on offer. This paper presents the initial phase of this ongoing research project and discusses the uncertainties and critique regarding the creation of design requirements and how the consortium overcame them.
Hierarchically adaptive image block matching under complicated illumination conditions

Image block matching is one of active research fields in image processing, which has been widely used in security monitoring and motion estimation. Due to great disparities in images of the same scene under various illumination, block matching has been a challenging task. To this end, we propose a hierarchical block matching method which is adaptive to the computational complexity and suited to complicated illumination environments. The approach is divided into two parts. First, in order to reduce searching time, the whole algorithm is established in the framework of pyramid structures. Second, the correlation coefficient and structural functions are adopted to evaluate the similarity between two images so as to get better matching results. Simulation results show that, compared with the classical three-step, four-step searching algorithms and OHBM algorithm, this algorithm can reduce the time complexity efficiently. Moreover, since the algorithm makes use of the structure details of images, it is robust to complicated scenes.
Refusing to unmake the lemonade, or how not to go back to normal

research-article Share on Refusing to unmake the lemonade, or how not to go back to normal Author: Michelle Trim University of Massachusetts Amherst, Amherst, Massachusetts University of Massachusetts Amherst, Amherst, MassachusettsView Profile Authors Info & Claims ACM SIGCAS Computers and SocietyVolume 50Issue 1April 2021 pp 15–18https://doi.org/10.1145/3470834.3470839Online:14 June 2021Publication History 0citation40DownloadsMetricsTotal Citations0Total Downloads40Last 12 Months20Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Neuromorphic Silicon Photonics for Artificial Intelligence

Recent investigations in neuromorphic photonics, i.e. neuromorphic architectures on photonics platforms, have garnered much interest to enable high-bandwidth, low-latency, low-energy applications of neural networks in machine learning and neuromorphic computing. Although electronics can match biological time scales and exceed them, they eventually reach bandwidth limitations. Neuromorphic photonics exploits the advantages of optical electronics, including the ease of analog processing, and fully parallelism achieved by busing multiple signals on a single waveguide at the speed of light. In this chapter, we summarize silicon photonic on-chip neural network architectures that have been widely investigated from different approaches that can be grouped into three categories: (1) reservoir computing; reconfigurable architectures based on (2) Mach-Zehnder interferometers, and (3) ring-resonators. Our scope is limited to their forward propagation, and includes potential on-chip machine learning tasks and efficiency analyses of the proposed architectures.
Feature Selection of 3D-Texture from MRI Data towards Localization of Prostate Tumor

Prostate cancer is the most common cancer in men in the EU. The TRUS-guided prostate biopsy e.g. is painful and lack accuracy. A noninvasive, reproducible and precise localization of prostate cancer without causing pain is still required. Multispectral MRI data of various modalities are currently promising technique for prostate cancer localization. However, it is obvious that these images are difficult to read. With the help of a computerized segmentation the time required to read the images could be reduced and the accuracy of eventual biopsies could be increased by focusing on a specific region. For this purpose, three major 3D textural features are exploited, i.e. GLCM, LBP, and three dimensional wavelet transform, together with T1-weighted, T2-weighted and contrast enhanced data will be used to generate a multispectral basis for the features. In this paper, all mentioned enhanced MRI features are investigated, at the end the best promising 3D features are presented.
The system of multimedia technologies implementation in high school

Recent research highlights the effective use of multimedia as an auxiliary strategy; it helps to easily deliver educational material to students and plays a positive role in strengthening the overall trend towards computer use in education. It is impossible to overestimate the importance of multimedia technologies in education. Multimedia technologies help simplify abstract content, allow you to differ from individual people, and allow you to coordinate diverse views from different points of view. Of course, multimedia technologies improve teaching and learning process, but this technology has a number of drawbacks that make it difficult to implement it into the higher school program. The authors of the study developed the system of MMT implementation is the educational process in high school. The system was developed according to the content of MM phenomenon based on advantages as well as eliminated disadvantages of new technologies in learning process. The definite level of financing and technical support, provided according to the State Program on Digital Economy of the Russian Federation, allows high school to organize the learning process via MMT providing a big volume of different opportunities for specific purposes. Follow the developed system allows educating digital personnel ready to work for digital economy of the region.
The Concept of Spontaneity and its Relationship with the Individual Characteristics of Personality

In this article, the authors consider the concept of spontaneity and the relationship of this characteristic with such individual personality characteristics as locus of control, autonomy, tolerance to uncertainty, creativity, self-regulation, flexibility in communication and spontaneity itself. The authors conduct an experiment that allows to trace this relationship. The research involved 205 people between the ages of 18 and 40, including 125 people living in Ukraine and 80 people living and studying abroad. The Ukrainian study group consisted of 82 women and 43 men. The group of foreign researchers consisted of 52 women and 28 men. To study the impact on the spontaneity of the sociocultural experience of the individual, representatives of other communities aged 18–39 were involved. The sample of foreign respondents was divided into 4 groups: countries in Europe, countries in Asia and Africa, CIS countries and other countries in the world 80 people in total. In the study, the authors conclude that spontaneity does not exist separately from other personality characteristics, is not an independent personal property that develops according to its own laws, but is closely connected with many important personality formations and structures, and in order to understand how these connections function, it is not enough to investigate spontaneity separately from other personality traits.
Research on Relation Extraction Method Based on Multi-channel Convolution and BiLSTM Model

Deep learning methods have achieved good results in relation extraction research and have received widespread attention. However, the existing deep learning methods use a single word vector model, which cannot fully utilize the rich semantic information and syntactic structure in the corpus. The high parameter dimension causes information overload and cannot make full use of context information. Aiming at the problems of the current method, this paper proposes a multichannel relation extraction framework that uses multiple word vector models to map the corpus to form a multi-channel. Feature extraction is performed through the neural network model fused with convolutional neural network, BiLSTM and attention mechanism, and finally completes the relationship extraction task through the classifier. The experimental results on the SemEval 2010 task 8 data set show that this method can not only acquire rich semantic information in the corpus, but also better learn local features and use contextual information. Compared with other methods, the method in this paper achieves competitive performance.
Cyber Security Education and Future Provision

Cybersecurity education is a crucial element to provide a workforce for the future to have an awareness together with the skills and knowledge enabling them to adapt and diversify in the field. Cybercrime is covered by the need for distinct aspects of security and measures of control on subsequent systems and devices. As the need for Cybersecurity specialists has increased in recent years and during the exceptional circumstances of the Covid-19 pandemic, the provision of education in secondary, post-16 and higher education sectors needs to be met. Utilisation of strategies and innovations to meet industry and educational expectations is key for future provision. Drive for different strategies and innovations in place by governments and organisations throughout the world, as provision in education is not balanced enough to cope with the increasing demand for a cybersecurity workforce. This study will inform recommendations for effective provision of future cybersecurity education.
Correction of curved screen in deflectometry

Deflectometry has been widely used in topography measurement of specular surface due to the merits of full-field data acquisition, automatic data processing, high accuracy, and large dynamic range. Existing deflectometry-based methods, such as model phase measuring deflectometry, direct phase measuring deflectometry, stereo deflectometry, employ a plane screen. Due to the size and shape of the used screen, reconstruction of specular surface with large curvature can’t be carried out in single measurement. The characteristics of curved screens effectively expand the ranges of the gradient and height field of mirror. Ideally, curved screens are a part of normal cylinders. However, initial experiment shows that the commercialized curved screen changes in radius, instead of a constant value. In order to obtain the model of the curved screen, two CCD cameras capture fringes displayed on the curved screen. Stereo technique is applied to compute screen’s point cloud containing curved screen’s location and phase. The stereo technique is rectifying pairs of fringe map based on epipolar constraint to the same rotating matrix. The epipolar line of fringe map pair lies in the same row. Then disparity is computed through block match in column coordinate and refined through curve fitting. Some experiments have been carried out and results show that rectifying fringe map helps get more accurate and stable point cloud of curved screen to be suitably employed in deflectometry by a curved screen.
Professor SM Chitre - A Tribute

I was pained to hear from my former student Dr. Sujata Deshpande, of the passing away of Professor Shashikumar Madhusudan Chitre, on 11th January 2021. I have been privileged to have had a long association with Prof. Chitre. My first vivid recollection of Prof. Chitre is from 1994 when he chaired a committee that interviewed me for the prestigious Homi Bhabha Fellowship. I was deeply moved by Prof. Chitre’s scholarship in and outside the natural sciences, his humility, his grace and his smile. I never since lost an opportunity to chat with him and always came away with optimism. I also had frequent interactions with him while we both served on the council of the Indian Academy of Sciences, Bangalore. Prof. Chitre had a remarkable ability to communicate the essence, and the excitement of little pieces of scientific research carried out by him and his students and even more often, by people who were being considered for a prize or a fellowship. I often remember these expositions by Prof. Chitre with nostalgic regret, when we nowadays spend so much time discussing the numbers of citations and other indices of performance of the candidates and learn nothing about their science. Prof. Chitre epitomised a culture of science that recognised no disciplinary boundaries, a culture in which science and the arts were organically connected, a culture imbued with humanism. I will miss Prof. Chitre. The passing away of individuals is the law of nature, but there is no reason that we should let the cultural and human values embodied by people like Prof. Chitre also to pass on. We can pay no greater tribute to Prof. Chitre than to cherish and perpetuate the qualities in him that made us all so fond of him.
Optimizing the Distributed Learning System with Accuracy Driven Dynamic Communication Frequency

The parameter server system (Parameter Server, PS) is the most widely used distributed machine learning system. Its core module is the communication module, which mainly includes three parts: communication content, communication frequency, and communication pace. The communication scheme of the existing PS system will have problems such as waste of computing power resources and a large proportion of communication overhead. This paper hopes to design and implement a distributed set that saves computing power and has a smaller communication overhead based on the communication module of the original PS system. This article mainly innovates in following aspects: First, through the analysis and experiment of Bulk Synchronous Parallel (BSP) communication pace, based on the superiority of BSP algorithm level, it overcomes the waste of computing power of BSP at the system level, and designs and implements a new set of Communication pace, the new communication pace under the same training task can save more computing power and achieve higher model accuracy. Secondly, this article combines the quantization compression technology with the parameter server system, and uses the quantization technology to compress and decompress the communication content in the parameter server system, so that the volume of the communication content during the operation of the parameter server system is significantly reduced, thereby reducing the communication overhead and improving Speedup ratio of the system. Finally, based on the analysis and experiment of the change trend of the model accuracy rate, this paper designs and implements a dynamic communication frequency adjustment scheme based on the change trend of the model accuracy rate, which reduces the overall communication frequency during the operation of the parameter server system and reduces the parameter server. Experiments show that the system designed in this paper is superior to the existing mainstream solutions in terms of communication pace, communication content, and communication frequency.
Trainee translators' and professional translators' source text comprehension and reproduction

This chapter aims to reaffirm the need to give more prominence to text-linguistic analysis for comprehension and reproduction in translator training programs of both source texts and target texts. At the interface between foreign language teaching and translator training is the issue of training students to analyze L2 source texts (i.e., English) whereby they learn to identify the semantic-pragmatic relations that underlie varied grammatical constructions, such as fronted participial clauses, parenthetical modificational elements positioned between the head noun and the rest of the sentence, appositive elements, and certain idiomatic phrases. The semantic-pragmatic relations that underlie the aforementioned grammatical constructions could be theme/rheme relations, statement-comment, problem-solution, etc. This chapter reports the results of a comparative study of a sample of undergraduate Arabic-speaking trainee translators’ renditions and a sample of professional translators’ renditions of a source text such as a nonfinite clause at the beginning of a sentence “Depending on where you stand,” or an idiomatic verbal phrase positioned between the head noun and the predicate as “strikes a budget deal” or an appositive element such as the “Presidency of the EU” as in “the British, as holders of the EU Presidency” (see Appendix A: Source Text titled: European Union Summit). Furthermore, it examines the processes the two groups use to identify or not identify the source text segments as problems and the solutions they offer. The study consisted of two phases: (1) a translation of the text from English into Arabic by 18 undergraduate trainee translators and by 18 professional translators and (2) a questionnaire that examined both groups’ identification or lack of identification of six source text segments as problems and the solutions to those problems. The first phase is a product-oriented approach to translation performance while the second phase is process-oriented (cf., Gile qtd. in Kelly 13). The analysis of the translations produced by the two groups, the undergraduate trainee translators and the professional translators, shows that the latter outperformed the trainees in translating the source text segments. However, both groups failed identically at the task of identifying the problematic source text segments and providing solutions. In using dictionaries, both groups disregarded the situational context of the problem segments. The findings of this study point to one pressing issue in translation teaching - the need to upgrade the level of trainee translators’ and the professional translators’ textual competence in L2 reading comprehension.
Mathematics, Computing, and Arithmetic

We have remarked earlier that actually doing cryptography requires combining mathematics and computing. In this chapter we describe several algorithms and computational tricks that make it possible to do the discrete mathematics that is cryptography on computers that have not necessarily been designed to provide robust support for discrete mathematics. This chapter covers a few of these tricks and algorithms necessary for understanding how one might actually do cryptography in the real world. The first set of tricks has been used extensively in testing integers for primality, using the bit patterns of the integers to eliminate the need for modular reduction. Multiprecise arithmetic is needed for much of modern cryptography, with modular reduction and multiplication dominating the cost of arithmetic. Multiplication itself is done with fast methods like the FFT, which we cover here, and reduction can be dealt with by Montgomery multiplication, which essentially extends the Mersenne prime trick to all integer moduli.
The integration of English words in online communication in China

This article investigates how Chinese Internet users integrate English words syntactically and semantically into their Chinese online communication. ‘English words’ – English lexicons that are fully spelled out in Latin alphabets – appear more often in Chinese speakers’ online interactions than before. However, there are not many studies revealing this ongoing situation. By examining netizens’ asynchronous communication, the article reveals a holistic picture of the integration of ‘English words’ and draws on previous literature to infer the possible reasons behind it. These findings show that the most common English words that are borrowed are content words. Meanwhile, these words are semantically and syntactically integrated into Chinese, demonstrating that English words are creatively localized. Moreover, this study inspires future studies to examine the concluded findings in a broader context and within a longer period.
Representing stand-alone automata by characteristic polynomials over a finite field

Abstract This paper proposes a method of representing a deterministic stand-alone output automaton by a minimal characteristic polynomial over a finite field. It is shown that defining various automaton transition and output functions allows using the algorithm developed to build the sets of minimal polynomials, different in their powers. Estimates of the relevant sets are given here. We have identified the relation between a characteristic minimal polynomial and an ergodic stochastic matrix defining the sequence developed by a stand-alone automaton. It is shown that the minimal degree of a characteristic polynomial depends linearly on the power of the automaton state set and on the accuracy of representing the elements of a given limiting vector of a stochastic matrix.