Reviewerly/exHarmony huggingface dataset:
Six principles for a digital anthropology

This chapter proposes six basic principles as the foundation for a new subdiscipline: digital anthropology. The term digital will be defined as all that which can be ultimately reduced to binary code but which produces a further proliferation of particularity and difference. The dialectic refers to the relationship between this growth in universality and particularity and the intrinsic connections between their positive and negative effects. The commitment to holism, the foundation of anthropological perspectives on humanity, represents a third principle. The alternative argument is that most people are not particularly involved in or concerned with issues involved in coding or digital production. Australian aboriginal tribes may not have much material culture, but instead they use their landscape to create extraordinary and complex cosmologies that then become the order of society and the structures guiding social engagement.
Evaluation of Transformer model performance on a set of language pairs by varying standard parameters

In this paper, we have performed machine translation using modified Transformer model on TED Talk Dataset and from this we have selected four language pairs (English-Portuguese, Russian-English, French-Portuguese, Turkish-English) for testing. We have evaluated and analyzed the machine translation model on critical criteria's such as dataset size, batch size and training time over all four language pairs and have used BLEU scoring to generalize the results. We have searched for general trends and have analyzed the impacts of these variations on the scoring of the translation upon variation of different parameters, this analysis has been performed on each language pairs as well as have been compared with the results of other language pairs. From the results we found that with increase in dataset size, batch size and training time the BLEU score increases. The English - Portuguese pair achieves highest BLEU score of 23.2 from all language pairs and we get lowest BLEU score of 19.1 from the French - Portuguese pair.
Fields generated by characters of finite linear groups

In previous work (Dawsey et al. in J Algebra 533:339–343, 2019), the authors confirmed the speculation of J.G. Thompson that certain multiquadratic fields are generated by specified character values of sufficiently large alternating groups $$A_n$$ . Here we address the natural generalization of this speculation to the finite general linear groups $$\mathrm {GL}_m\left( {{\mathbb {F}}}_q\right) $$ and $$\mathrm {SL}_2\left( {{\mathbb {F}}}_q\right) $$ .
Text sentiment classification based on BP neural network

In a consumer-oriented market, people's opinions or comments will, directly or indirectly, have influence on other people's decisions. For example, as choosing goods or online services, positive opinions or comments can promote consumers' purchase. At the same time, negative comments can also reduce consumers' enthusiasm for buying. The BP neural network will be trained with a supervised learning method, and finally obtain the ability that can classify positive or negative text reviews. In this paper, the supervised training method is adopted to improve the operation speed of the whole model by 8-9 times after optimizing the data and structure. The accuracy rate, recall rate, and F1 value are around 87.5%, 86.7%, and 87.35% respectively.
Historical Corpora Correlation based on RNN and DCNN

Abstract Correcting historical corpora in digital version is a crucial task for the historical research, however, scan quality, book layout, visual character similarity can affect the quality of the recognizing. OCR is at the forefront of digitization projects for cultural heritage preservation. The main task is to identify characters from their visual form into their textual representation. In this paper, we propose a model combining recurrent neutral network(RNN) and deep convolutional network(DCNN) to correct OCR transcription errors. The experiment on a historical book corpus in German language shows that the model is very robust in capturing diverse OCR transcription errors greatly.
Sentiment Analysis Using Semi Supervised Machine Learning Technique

The sentiments of the users are expressed in the form of views or comments, in favor or against of any item, a product or a movie, etc. These reviews may be labeled or unlabeled. Labeled reviews are easier to process in compare to that of unlabeled once. Using Semi supervised machine-learning technique; the unlabeled reviews can be labeled. In this approach, with the help of small amount of labeled reviews, a large volume of unlabeled review can be labeled. In this paper, a step-by-step approach is adopted to label the unlabeled dataset. In order to perform this task, Support Vector Machine (SVM) technique is used. In order to access the results in each steps, the performance of the used technique is evaluated using different parameters like precision, recall and accuracy and thus, overall process can move forward.
Context-Adaptive Document-Level Neural Machine Translation

Document-level translation models are still far from perfect. Most existing document-level neural machine translation (NMT) models leverage a fixed number of the previous or all global sentences to handle the context-independent problem in standard NMT. However, the translating of each source sentence benefits from various sizes of context. And study shows that inappropriate redundant context will increase model burden but not improve the translation performance. This work introduces a data-adaptive method that enables the model to adopt the necessary and helpful context. Specifically, we introduce a light predictor into two document-level translation models to select the explicit context. Experiments demonstrate the proposed approach can significantly improve the performance over the previous methods with a gain up to 1.99 BLEU points.
Acceptance and Practicality of Voice Assistance Systems in the Everyday Life of Seniors: A Study Design

Voice assistance systems (VAs) are becoming more popular. For Digital Natives, these systems are almost part of everyday life. Does this technology also have the potential to facilitate access to digital services for persons aged 55+? Using the example of Amazon’s “Echo” devices, our research intends to provide a survey of the extent to which VAs are known to the target group, how many elderly people own these systems and whether there are any reservations. In addition, test persons will be investigated, which form of interaction seems easiest for the solution of the tasks and how the operation of VAs differs from the operation of mobile devices, to be learned and applied. The evaluation will include statements on socio-demographic and ethno-graphic aspects. Various Amazon Echo devices are installed in 20 senior households (and additionally in their family households) in order to test user acceptance and its benefits under real-world conditions.
Comparative Analysis of Approaches to Prediction of Quantitative Parameters During a Pandemic

The purpose of this paper is to study and compare several approaches to predict quantitative parameters of an epidemiological situation. These parameters change in time is not stochastic and chaotic. For instance, the number of total infection cases increases exponentially in the beginning but tends to have a linear trend later. Such processes can be modeled in a variety of ways, for example, with the SEIR model or its modifications. This paper also compares time series models, like exponential smoothing, autoregressive models, and a neural network in application to the target task. This article describes a result of a comparison of these algorithms, and an explanation of obtained results, for instance how some characteristics of target features describe a more accurate prediction of future values by the modified SEIR model, rather than an exponential smoothing process or Holt-Winters method.
High-Performance Image Visual Feature Detection and Matching

The fundamental task in high-speed and high-accuracy CCTV video surveillance system is attention-based Image visual point detection and matching. The image viewpoints are different at different instants of time, so that the image recognition and matching systems are having large computational complexity. The designed work is to present an ROI-based SIFT Image detection and BRIEF matching algorithms with a processing speed of 44 ns, and it meets the real-time requirement. The performance of the designed system is improved with a hybrid sorting-based median filter. These algorithms are able to establish accurate feature extraction and matching at 30fps for 640 Pixel video. This can be implemented using MATLAB and Xilinx tools..
Course Question Answering System Based on Artificial Intelligence

With the rapid update of artificial intelligence technology and network technology, modern distance education has developed rapidly and has gradually become an important part of the modern education field, which has attracted great attention from the education and computer circles. The course question answering system based on artificial intelligence is an indispensable part of the modern distance education teaching process and plays an important role in assisting teaching. The purpose of this article is to research the course answering system based on artificial intelligence. This paper introduces the concept of artificial intelligence Agent into the research of question answering system. Under the guidance of constructivist learning theory, using natural language processing technology and personalized recommendation technology to design and develop an artificial intelligence network intelligent answering system based on multi-agent. This paper studies the three key technologies to realize the artificial intelligence answering system, namely natural language processing technology, personalized recommendation technology and the structure and organization of the knowledge base. This paper proposes a multi-agent-based artificial intelligence network answering system model, implements the prototype system, and analyzes the system structure and workflow in detail. This article aims to improve the recall and accuracy of problem text clustering. Applying association rules to each type of problem after clustering can obtain a more accurate association table, and at the same time promote the accuracy of similarity to be improved. This loop operation can improve the answering speed and accuracy. Experimental research shows that when the number of transactions is 300, a takes 0.878, and B takes 0.037, the highest accuracy of answering questions can be obtained.
Inside-out Vision for Procedure Recognition in Dental Environment

Smart homes and offices are becoming more and more common with the advances in computer vision research and technology. Identifying the human activities and scenarios are basic components of such systems. This is important not only for the eco-system to work independently, but also to allow robots to be able to assist humans. This is specially true in the more complicated medical setups, e.g. dentistry, where we need subtle cues e.g. eye motion to identify scenarios. We present a hierarchical model in this paper for robustly recognizing scenarios and procedures in a dental setup by using the objects seen in eye gaze trajectories like material and equipment used by the dentist, and symptoms of the patient. We utilize the fact that by identifying the objects viewed during an activity and linking them over time to create more complicated scenarios, the problem of scenario recognition can be hierarchically solved. We performed experiments on a dental dataset and showed that combining multiple parameters results in a better precision and accuracy compared to any of them individually. Our experiments show that the accuracy increased from 45.18% to 94.42% when we used a combination of parameters vs. a single one.
Defense Against Frequency Analysis In Elliptic Curve Cryptography Using K-Means Clustering

Elliptic Curve Cryptography (ECC) is a revolution in asymmetric key cryptography which is based on the hardness of discrete logarithms. ECC offers lightweight encryption as it presents equal security for smaller keys, and reduces processing overhead. But asymmetric schemes are vulnerable to several cryptographic attacks such as plaintext attacks, known cipher text attacks etc. Frequency analysis is a type of cipher text attack which is a passive traffic analysis scenario, where an opponent studies the frequency or occurrence of single letter or groups of letters in a cipher text to predict the plain text part. Block cipher modes are not used in asymmetric key encryption because encrypting many blocks with an asymmetric scheme is literally slow and CBC propagates transmission errors. Therefore, in this research we present a new approach to defence against frequency analysis in ECC using K-Means clustering to defence against Frequency Analysis. In this proposed methodology, security of ECC against frequency analysis is achieved by clustering the points of the curve and selecting different cluster for encoding a text each time it is encrypted. This technique destroys the regularities in the cipher text and thereby guards against cipher text attacks.
Shot Segmentation Algorithm Based on HSV Model in Sport Video

With the development of multimedia technology and the appearance of information highway, the storage and transmission of digital video have become a less difficult issue. Various sport game brings the explosive expanding of sport video, therefore, efficient analysis of sport video was necessary. An algorithm of shot segmentation was proposed. Firstly, the data in each frame was quantized in HSV model; then based on histogram of each frame, the difference of adjacent frames, the difference of cumulate frames and the percentage of the frame difference in a defined window were computed; furthermore, the adaptive average in a window, the adaptive thresholds and sliding windows were obtained. Experiment results show that the new approach is superior over double threshold comparison method and singular value decomposition algorithm.
Human Body Measurement Extraction from 2D Images

In this fast-phased world, the fashion industry is changing and tries to give confidence to people who wear their clothes. The fit of the garment depends on accuracy of measurements. The traditional method of measuring may provide wrong information if the tools are inappropriate. Even though 3D body scanning can give accurate results, they cannot be afforded by small business setups. 3D imaging makes the process expensive. Not all can afford a stylish to measure and stitch 4–5 sets of outfits and select the best. The working community has no time to visit stores/tailoring shops regularly. This paper proposes inexpensive method for extracting human body measurements from 2D images which helps the society to reach out to the different styles and fitted garments of their taste. Human body measurements are extracted with the help of—Affine and Metric correction, Green Screen Segmentation, Heuristics for detection and pixel-to-real world distance. It is a 2D-image-based system which takes one front view, side view and front view with checkerboard. This method involves manual annotation technique.
Searching a Raw Video Database using Natural Language Queries

The number of videos being produced and consequently stored in databases for video streaming platforms has been increasing exponentially over time. This vast database should be easily index-able to find the requisite clip or video to match the given search specification, preferably in the form of a textual query. This work aims to provide an end-to-end pipeline to search a video database with a voice query from the end user. The pipeline makes use of Recurrent Neural Networks in combination with Convolutional Neural Networks to generate captions of the video clips present in the database.
Multiple-Image Super-Resolution Using Deep Learning and Statistical Features

Capturing, transferring, and storing high-resolution images has become a serious issue in a wide range of fields, in which these processes are costly, time-consuming, or even infeasible. As obtaining low-resolution images may be easier in practice, enhancing their spatial resolution is currently an active research area and encompasses both single- and multiple-image super-resolution techniques. In this paper, we propose a deep learning approach for multiple-image super-resolution that is independent from the number of available low-resolution images of the scene. It is in contrast to other deep networks which are crafted to deal with input stacks of a constant size, hence are not applicable once the number of low-resolution images varies. The experiments showed that our technique not only outperforms other single- and multiple-image super-resolution algorithms, but also it is lightweight and delivers instant operation, thus can be deployed in hardware-constrained environments.
An Analysis of Purchase Data in Drugstore by Poisson Latent Block Model

In this paper, POS data with IDs of drugstores is applied to the analysis method proposed by Govaert & Nadif (2010).By using the Poisson latent block model with snacks purchase history data at drugstores located in the Gifu region, we cluster both the customers and the snacks brands simultaneously, and grasp the snack brand group that the customer group with a high purchase frequency choose.Furthermore, based on the model estimation, we discuss effective marketing measures in drugstores.Although we analyze only snack category, it can be extended to other product categories and is an effective way to summarize Big Data.
Task Assignment of Multi-AUVs Based on Artificial Bee Colony Genetic Hybrid Algorithm

In this paper, the Artificial Bee Colony(ABC) Algorithm is used to study the multi-autonomous underwater vehicle(AUV) missions allocation scheme of a heterogeneous net. An improved bee colony-niched genetic algorithm (IABC-NGA) is proposed to solve the multi-AUV task allocation scheme in the late evolutionary stage to optimize the global search performance, improving the system cooperation ability and improving the lack of single AUV capability. Finally, simulation experiments indicate the evaluate of IABC-NGA algorithm. They show that IABC-NGA can obtain more effective assignment results under multiple constraints.
Using the Markov chain to predict the outcome of an election in a given situation

Abstract This paper used Markov Chain, a mathematical model that is often utilized in predicting, to forecast the result of the election process of a person in a specific circumstance. In the way of modeling, starting from the tree diagram to the model of Markov Chain and related transition matrix, this paper aimed at the election result, which relates the object of study, a specific candidate. The research data are collected from the prompt of the research question and the calculation of coding based on the transition matrix. The result is, when it is the 64 th year of that specific candidate running for the reelection campaign, he will be likely to retire from politics.
Recognition of the Basketball Players Position Using Live Cameras

This article discusses the research aimed at creating a program that will track the players of the attacking basketball team in various matches and transfer them to a minimap in the form of dots, showing the trajectory of each player's movement. The article also reviews previous research on this topic, which partially solve the tasks set, and describes the technologies used in this study. Models of previous works have been changed for this research. An experiment was presented that shows the trainability of the neural network that determines the players on the field. The article also describes a ball tracking experiment based on the trainability of neural networks. A study on transferring tracked data (players, ball) to a 2D diagram of a basketball field was conducted.
Short Definite Descriptions and Referent Activation

We test the claim that the use of short definite descriptions (SDDs) for referent mentions in discourse (e.g. the country) corresponds to a specific range of referent activation in the speaker’s working memory, lower than the activation level necessary for pronoun use and higher than the level corresponding to proper names (PNs). Data from the WSJ MoRA 2020 corpus of English written texts suggest that the use of SDDs and PNs corresponds to one and the same activation range. Referential distance is highly correlated with activation, and we compare the distribution of two distance metrics, linear distance and rhetorical distance, across SDDs and PNs. We have found the differences to be statistically insignificant. Furthermore, we demonstrate on the corpus data that at least four factors operate that affect the choice between SDDs and PNs, while not being directly related to activation, namely: referent animacy, referent number, relative length of the SDD and the PN for the given referent, and prior mentions of the referent by means of an SDD within the current text.
Designing the Curriculum for a Minor in Cyber Criminology

We are living in an age of growing cyber crime and the costs associated with it. With more and more people and devices being connected through the Internet, there are plenty of opportunities for new kinds of criminal activity as the Internet provides cyber criminals with anonymity and global reach. According to the FBI, in 2018 the Internet Crime Compliant Center (IC3) [1] received 351,936 complaints with total losses exceeding $2.7 billion. The threat of cyber crime is real and pertinent when the Internet is intertwined with our everyday lives. We must prepare today's undergraduate students, tomorrow's future workforce, to fight this growing threat of cyber crime. If we do not prepare today, we will be vulnerable tomorrow. This paper details our experiences in developing and implementing an interdisciplinary minor in Cyber Criminology. The minor is designed for students who are interested in learning about cyber crime from the dual perspectives of computer science and criminal justice.
Impact of Data Augmentation on Age Estimation Algorithms

This paper presents and analyzes algorithms for age estimation of the person in an image. Theoretical analysis and practical implementation of the experimental system were performed. The emphasis was on neural network-based algorithms. The impact of increasing the training set on the accuracy of the network was analyzed and different techniques of data augmentation in real-time and out of real-time were presented. Some simpler augmentation techniques were used to enlarge the training set in real-time, while augmentation out of real-time was made possible by merging several different age estimation datasets. In addition to merging existing datasets, the possibilities of increasing the training set with artificially generated images were also examined. For that purpose, a generative adversarial network was used, which can generate artificial images that belong to the appropriate age groups. At the end of the paper, the results on the test set were evaluated. It has been shown that increasing the training set achieves about 6% higher accuracy compared to the case without augmentation and about 3% higher accuracy compared to the results from the published papers.