Reviewerly/exHarmony huggingface dataset:
Stochastic emulation of quantum algorithms

Abstract Quantum algorithms profit from the interference of quantum states in an exponentially large Hilbert space and the fact that unitary transformations on that Hilbert space can be broken down to universal gates that act only on one or two qubits at the same time. The former aspect renders the direct classical simulation of quantum algorithms difficult. Here we introduce higher-order partial derivatives of a probability distribution of particle positions as a new object that shares these basic properties of quantum mechanical states needed for a quantum algorithm. Discretization of the positions allows one to represent the quantum mechanical state of n bit qubits by 2( n bit + 1) classical stochastic bits. Based on this, we demonstrate many-particle interference and representation of pure entangled quantum states via derivatives of probability distributions and find the universal set of stochastic maps that correspond to the quantum gates in a universal gate set. We prove that the propagation via the stochastic map built from those universal stochastic maps reproduces up to a prefactor exactly the evolution of the quantum mechanical state with the corresponding quantum algorithm, leading to an automated translation of a quantum algorithm to a stochastic classical algorithm. We implement several well-known quantum algorithms, analyse the scaling of the needed number of realizations with the number of qubits, and highlight the role of destructive interference for the cost of the emulation.
Early Perceptions of COVID-19 Contact Tracing Apps in German-Speaking Countries: Comparative Mixed Methods Study (Preprint)

<sec> <title>BACKGROUND</title> The main German-speaking countries (Germany, Austria, and Switzerland) have implemented digital contact tracing apps to assist the authorities with COVID-19 containment strategies. Low user rates for these apps can affect contact tracing and, thus, its usefulness in controlling the spread of the novel coronavirus. </sec> <sec> <title>OBJECTIVE</title> This study aimed to assess the early perceptions of people living in the German-speaking countries and compare them with the frames portrayed in the newspapers during the first wave of the COVID-19 pandemic. </sec> <sec> <title>METHODS</title> We conducted qualitative interviews with 159 participants of the SolPan project. Of those, 110 participants discussed contact tracing apps and were included in this study. We analyzed articles regarding contact tracing apps from 12 newspapers in the German-speaking countries. </sec> <sec> <title>RESULTS</title> Study participants perceived and newspaper coverage in all German-speaking countries framed contact tracing apps as governmental surveillance tools and embedded them in a broader context of technological surveillance. Participants identified trust in authorities, respect of individual privacy, voluntariness, and temporary use of contact tracing apps as prerequisites for democratic compatibility. Newspapers commonly referenced the use of such apps in Asian countries, emphasizing the differences in privacy regulation among these countries. </sec> <sec> <title>CONCLUSIONS</title> The uptake of digital contact tracing apps in German-speaking countries may be undermined due to privacy risks that are not compensated by potential benefits and are rooted in a deeper skepticism towards digital tools. When authorities plan to implement new digital tools and practices in the future, they should be very transparent and proactive in communicating their objectives and the role of the technology—and how it differs from other, possibly similar, tools. It is also important to publicly address ethical, legal, and social issues related to such technologies prior to their launch. </sec>
Bio-Inspired Multimodal Imaging in Reduced Visibility

The visual systems found in nature rely on capturing light under different modalities, in terms of spectral sensitivities and polarization sensitivities. Numerous imaging techniques are inspired by this variety, among which, the most famous is color imaging inspired by the trichromacy theory of the human visual system. We investigate the spectral and polarimetric properties of biological imaging systems that will lead to the best performance on scene imaging through haze, i.e., dehazing. We design a benchmark experiment based on modalities inspired by several visual systems, and adapt state-of-the-art image reconstruction algorithms to those modalities. We show the difference in performance of each studied systems and discuss it in front of our methodology and the statistical relevance of our data.
Effect Evaluation of Network Protection Based on Fuzzy-AHP

In order to accurately evaluate the effect of network protection, this paper presents an effect evaluation model based on Fuzzy-AHP. The model presents the evaluation index system of network protection effect, and uses the method of fuzzy analytic hierarchy process to calculate the index weight, and finally uses the weighted average method to calculate the result of the evaluation. The elementary test and calculation results show that the model can be in all reason utilized to measure the actual network protection effect.
Probability distributions of COVID-19 tweet posted trends use a nonhomogeneous Poisson process

The influence of social media in disseminating information, especially during the COVID-19 pandemic, can be observed with time interval, so that the probability of number of tweets discussed by netizens on social media can be observed. The nonhomogeneous Poisson process (NHPP) is a Poisson process dependent on time parameters and the exponential distribution having unequal parameter values and, independently of each other. The probability of no occurrence an event in the initial state is one and the probability of an event in initial state is zero. Using of non-homogeneous Poisson in this paper aims to predict and count the number of tweet posts with the keyword coronavirus, COVID-19 with set time intervals every day. Posting of tweets from one time each day to the next do not affect each other and the number of tweets is not the same. The dataset used in this study is crawling of COVID-19 tweets three times a day with duration of 20 minutes each crawled for 13 days or 39 time intervals. The result of this study obtained predictions and calculated for the probability of the number of tweets for the tendency of netizens to post on the situation of the COVID-19 pandemic.
Morphological Tagging and Lemmatization in the Albanian Language

Abstract An important element of Natural Language Processing is parts of speech tagging. With fine-grained word-class annotations, the word forms in a text can be enhanced and can also be used in downstream processes, such as dependency parsing. The improved search options that tagged data offers also greatly benefit linguists and lexicographers. Natural language processing research is becoming increasingly popular and important as unsupervised learning methods are developed. There are some aspects of the Albanian language that make the creation of a part-of-speech tag set challenging. This research provides a discussion of those issues linguistic phenomena and presents a proposal for a part-of-speech tag set that can adequately represent them. The corpus contains more than 250,000 tokens, each annotated with a medium-sized tag set. The Albanian language’s syntagmatic aspects are adequately represented. Additionally, in this paper are morphologically and part-of-speech tagged corpora for the Albanian language, as well as lemmatize and neural morphological tagger trained on these corpora. Based on the held-out evaluation set, the model achieves 93.65% accuracy on part-of-speech tagging, The morphological tagging rate was 85.31 % and the lemmatization rate was 88.95%. Furthermore, the TF-IDF technique weighs terms and with the scores are highlighted words that have additional information for the Albanian corpus.
Blind Spot Warning System based on Vehicle Analysis in Stream Images by a Real-Time Self-Supervised Deep Learning Model

&lt;p&gt;With the advent of intelligent systems, we are still facing a high number of fatal traffic accidents. Driver assistance systems can significantly reduce this rate. For example, when a driver uses a turn signal, driver assistance systems alert the object's presence in blind spot areas. Camera-based driver assistance systems for blind spots usually alert by detecting objects, including vehicles, in image frames. Based on a more dynamic dangerous situation classification for lane changing and turning to the sides, we propose an efficient blind-spot warning system that works with a single camera sensor for each side. Our contribution consists of two sections. First, we take a deeper look at classifying dangerous and safe situations in a dynamic environment with moving objects. Second, to distinguish dangerous situations from safe conditions, we install a pre-trained SOTA object detector to track vehicles in consecutive frames and then estimate the distances of tracked cars by a 6% mean percentage error rate. In addition, to detect objects in blind spots, the proposed system uses cars' relative velocity to warn dangerous situations. This classification process is not real-time. So, in the second section, we propose a tiny model as a driver assistance system for the blind spot that works in real-time. This tiny model feeds optical flow into CNN layers. This vision-based system uses self-supervised learning without the necessity of the labeled data. It shows 97% accuracy and can detect dangerous situations as a real-time system.&lt;/p&gt;
Cloud Computing for English Teaching Assistant Platform

With the in-depth development of Internet technology, cloud computing applications and services have begun to emerge. At the same time, cloud computing has brought new ideas, platforms and resources for English teachers to easily build English teaching auxiliary websites. Starting from the definition of cloud computing, this paper discusses that the construction of English teaching auxiliary website should follow the six principles of pertinence, subjectivity, regularity, interactivity, simplicity and co construction, and elaborates the construction process of English teaching auxiliary website based on cloud computing.
Mammogram Pre Processing and Data Manipulation using Novel Threshold Based Pre Reduction Technique

Mammography is the best existing assessment for the discovery of early indications of breast cancer, for example, masses, calcifications, respective asymmetry and building twisting. Furthermore, mammograms are hard to interpret, particularly of cancer at their beginning times. The presence of relics and commotions can exasperate the location of breast cancer and decrease the pace of accuracy in the computer aided analysis (CAD). For this reason, pre-processing of mammogram images is significant during the time spent breast cancer analysis since it could decrease the quantity of false positive. Then again, it could assist radiologists with making an examination between mammograms. This paper proposed to Novel Threshold Based Pre Reduction Technique and Pre Processing research strategy of mammography breast cancer image processing.
The CRISP-ML Approach to Handling Causality and Interpretability Issues in Machine Learning

Interpretability in machine learning projects and one of its aspects - causal inference - have recently gained significant interest and focus. Due to the recent rapid appearance of frameworks, methods, algorithms and software most of which are in early stages of their development, it can be confusing for practitioners and researchers involved in a machine learning project to choose the best approach and set of techniques that would efficiently deliver valid insights while minimising the known risks of failure of data-related projects. CRISP-ML process methodology minimises this confusion by outlining a clear step-by-step process that explicitly treats of interpretability issues through every stage. The paper presents an update of CRISP-ML, which incorporates causality in a similar way and supports formalisation, design and implementation of specific instances of CRISP-ML process, subject to required levels of interpretability and causality of results. The approach is demonstrated on examples from the domains of credit risk, public health and healthcare.
Indonesian Language Characteristics of Deaf Children and Implications for Literacy Skills

This research aimed to reveal the characteristics of the Indonesian language in deaf children and the implications for their literacy skills. A qualitative approach was used on the subjects determined through purposive sampling. Data were collected by recording student performance portfolios, interviewing teachers, and observing video recordings. The data were analyzed descriptively and explanatively. The results showed that the Indonesian language for deaf children is limited, as seen through four ways. First, the vocabulary of deaf children is dominated by general words (hypernyms) and concrete references. Second, they do not use figures of speech or idioms, compound words, and terms. Third, their ability to form derivative words is still low. Fourth, they generally produce core and single sentences, and only a small part uses compound sentences. The main and single sentences have an inversion pattern, where the verb precedes the subject. Moreover, they fail to use linking between sentences, and the resulting discourse is a collection of separate sentences. The linguistic barriers resulted in a low effective reading speed of 33.04 – 68, 30 words per minute. This has implications for low literacy skills. Therefore, an intervention program is needed to improve the language skills of deaf children.
Peer review declaration

All papers published in this volume of Journal of Physics: Conference Series have been peer reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing. • Type of peer review: Double-blind • Conference submission management system: All the papers were submitted papers via the conference email: eame2021@sina.com • Number of submissions received: 226 • Number of submissions sent for review: 209 • Number of submissions accepted: 99 • Acceptance Rate (Number of Submissions Accepted/Number of Submissions Received X 100):43.8 • Average number of reviews per paper: 2 • Total number of reviewers involved:61 • Any additional info on review process: EAME 2021 uses a plagiarism software to detect instances of overlapping and similar text in submitted manuscripts. • Contact person for queries : Ms. Yolanda Xu, Advanced Science and Industry Research Center, Hong Kong , eame2021@sina.com
Learning analysis of obstacle avoidance to beginners at trial times using NASA-TLX for drive assist system of welfare vehicle using Mixed Reality

Recently, welfare vehicles are widely used among the aged. However, it is not easy for inexperienced beginners or the aged to control the welfare vehicle in the narrow place. For the problem, the driving assistance system was proposed in which a virtual vehicle is projected on head mounted display (HMD) and controlled by a third person viewpoint. However, as a user wears HMD to control the virtual vehicle, the driving situation of the virtual vehicle was not visible from outsiders and the appropriate guidance was not provided to beginners. In order to get the driving skill of the vehicle more quickly, it is important to utilized the mixed reality (MR) space using the virtual object and to provide timely guidance while checking the movements of the virtual vehicle. In this paper, we propose a drive assist system of welfare vehicle for beginners using Mixed Reality. A virtual course that automatically displays virtual objects using MR is constructed and the virtual space is shared with the instructor. To evaluate the practice of the driving skill for beginners, NASA task load index (NASA-TLX) is used for quantifying the mental work-load. As a result, proposed method suppresses the weighted workload (WWL) score, and it is clarified that the driving skill is improved in a smaller number of trials.
THE SPEECH FUNCTION IN ADVERTISEMENTS

he study aims to describe types of speech functions and reasons for using them in advertisements. Descriptive qualitative method is applied in this research with sentences found in the dialogue or conversation is the advertisement as the data of this research. The data collected is from the advertisement of electronic media. The research results in the finding of six types of speech function used in the advertisement which are the statement acknowledge statement, question, response statement to question, offer, and command with the statement as the dominant. These types are used in advertisements for descriptive and persuasive purposes.
On Semantic Spatiotemporal Space and Knowledge with the Concept of “Dark-Matter”

It is highlighted for machine learning models implementing functions based on data training without program coding. Artificial neural network is one of the efficient machine learning models. Different from the other machine learning models like artificial neural network, we have presented semantic computing models which represent “meaning” of machine learning results. In our model, semantic spaces are created based on training data sets. Data calculations are performed on the space. Data are mapped to semantic spaces and presented as points in semantic spaces. The mapped positions of data represent the “meaning” of data. In this paper, we first present our new discovery in the formation of semantic spaces. We use the word “matter” to represent features of semantic spaces which are related to the non-temporal data. As the same time, we use the word “dark-matter” to represent the features of semantic spaces which are temporally changed. We use the word “energy” to represent matrixes which are used in the semantic computations to generate output data. We reveal that the “dark-matter” is the spatiotemporal matrix and present a mechanism of “memory” for implementing the semantic computation. The most important contribution of this paper is that we developed a new mechanism for implementing machine learning with “knowledge” in the “memory.” In the paper, we use case studies to illustrate the concepts and the mechanism. At the beginning, we present an example on creating a semantic space from a “chaotic state” to an “ordered state.” After that, we use examples to illustrate the mechanism of the “memory” and the semantic computation. The space expansion and the space division are also illustrated by examples.
Application and Optimization of New Mapping Technology in Engineering Surveying

At present, the application of surveying and mapping technology in the construction industry plays an important advantage, the application of new surveying and mapping technology to promote the development of the construction industry, the article mainly analyzes the application and optimization of new surveying and mapping technology in engineering surveying.
Koreksi Bias Data Hujan Luaran GCM ECHAM5 Untuk Prediksi Curah Hujan Bulanan dan Musiman Pulau Lombok

This study aims to evaluate the ability of the ECHAM5 GCM model output data in estimating monthly rainfall on the island of Lombok. The data used in this study are ECHAM5 monthly rainfall data and automatic rainfall recorder (ARR) measurement rain data for 2000-2018 obtained from ARR Gunung Sari. Correction of bias is conducted by using the mean ratio method and the regression method. The method that produces the best approach is then used to obtain rain data projections and a simple regression method. Evaluation and validation used the Pearson correlation coefficient (r), Root Mean Square Error (RMSE) and Nash-Sutcliffe Efficiency (NSE) values. The results obtained are that the daily and monthly rainfall data from the ECHAM5 model cannot be directly used to replace the rain measurement data because of its very low accuracy. The downscaling technique performed on daily and monthly rainfall data using the average ratio method does not show satisfactory performance where the efficiency figures produced are still low even gave a slight increasing number. However, the ECHAM5 model data can be used to obtain rainfall projections on a monthly and seasonal scale with a good and satisfactory correlation. Key words: mean ratio method; global climate model; ECHAM5; monthly rainfall.
A proposal to establish a field hospital including isolation units during the Novel Coronavirus crisis

After the rapid spread that the world is witnessing in all its countries of novel Coronavirus (covid 19), thinking has become urgent to find alternatives or solutions to deal with this disease. One of these solutions is through building field hospitals consisting of a group of cargo containers equipped with all devices and supplies for the purpose of limiting this virus in addition to the possibility of using them as fast-setting field hospitals in remote places. The research has provided those containers (hospitals) with solar energy systems where hospital ceilings can be used by supplying it with solar panels equipped for electrical energy. One of the advantages that will be talked about is the possibility of installing these containers in different places to keep their influence from the centers in addition to the ability to serve in such areas with the necessary health services. This means increasing health awareness and increasing methods of prevention and controlling epidemics and various diseases not only this virus which is currently spreading, . In addition to other advantages such as speed of construction and good insulation, as well as low costs if compared with regular hospitals.
Cephalofacial Study to Determine Gender and Ethnicity using Facial Index among the Muslims and Baniyas of Jhansi District

The present study was done on the cephalo-facial parameters among the males and females of Muslims and Baniyas of Jhansi district signifies the ethnicity among the two different communities of the population. 400 subjects were considered aged between 18-45 years which ethnically from Jhansi and their great grandfathers also.The measurements were taking by using sliding caliper and was analysed in statistical software SPSS (Statistical Packages of Social Sciences). The Facial Index of the two communities was calculated using the Morphological Facial Height and Width of Face. Therefore it was seen that the Facial Index of Muslim males and females was Euryprosopic (82.60%) and Leptoprosopic (87.17%) respectively that means that the males of Muslim community were having broad face type whereas Muslim females were having long face type. On the other hand the Baniya community males and females were Leptoprosopic (90.97%) and Mesoprosopic (81.60%) respectively that means the males were having long face and the females were having round face. Thus the two communities gender is distinguishable on the basis of their facial phenotype. The significant correlation was seen between the Muslim males and females Morphological Facial Length, Physiognomic Facial Length and Width of Face at 0.01 level and Baniya males and females Physiognomic Facial Length, Morphological Facial Length and Width of face also showed significant correlation at 0.05 level. Hence the study showed significant correlation at 0.01 and 0.05 level. The study concluded that our data could act as a reference for the Muslim and Baniya community of Jhansi District.