Reviewerly/exHarmony huggingface dataset:
Strategies for highlighting items within visual scene displays to support augmentative and alternative communication access for those with physical impairments

In contrast to the traditional grid-based display, visual scene displays (VSDs) offer a new paradigm for aided communication. For individuals who cannot select items from an AAC display by direct selection due to physical impairments, AAC access can be supported via methods such as item scanning. Item scanning sequentially highlights items on a display until the individual signals for selection. How items are highlighted or scanned for AAC access can impact performance outcomes. Further, the effectiveness of a VSD interface may be enhanced through consultation with experts in visual communication. Therefore, to support AAC access for those with physical impairments, the aim of this study was to evaluate the perspectives of experts in visual communication regarding effective methods for highlighting VSD elements.Thirteen participants with expertise related to visual communication (e.g., photographers, artists) completed semi-structured interviews regarding techniques for item highlighting.Study findings identified four main themes to inform how AAC items may be highlighted or scanned, including (1) use of contrast related to light and dark, (2) use of contrast as it relates to colour, (3) outline highlighting, and (4) use of scale and motion.By identifying how compositional techniques can be utilized to highlight VSD elements, study findings may inform current practice for scanning-based AAC access, along with other selection techniques where feedback or highlighting is used (e.g., eye-gaze, brain-computer interface). Further, avenues for just-in-time programming are discussed to support effective implementation for those with physical impairments.IMPLICATIONS FOR REHABILITATIONFindings identify multiple potential techniques to improve scanning through items in a photograph for individuals with severe motor impairments using alternative access strategies.Study findings inform current practice for scanning-based AAC access, along with other selection techniques where feedback or highlighting is used (e.g., eye-gaze, brain-computer interface).Avenues for just in time programming of AAC displays are discussed to decrease programming demands and support effective implementation of study findings.
Design and Implementation of a Charging Station Transaction System Based on Blockchain

With the rapid development of electric vehicles in recent years, many sharing and trading platforms for charging station have emerged. However, most of these platforms adopt a centralized management structure, and there are many problems. Users and transaction information are kept by the platform unilaterally, so there are risks of information leakage and transaction data tampering. The transaction process is not regulated, and some irregular institutions use the rights of the center to harm the interests of users. In order to deal with these problems, a new type of trustless charging station transaction platform is needed. Blockchain has the characteristics of distributed storage, consistent algorithm and asymmetric encryption. And it uses smart contracts to complete the transfer of funds and can effectively solve the trust and security issues in charging transactions. In view of the above research content, first we briefly introduce the operation mode of the decentralized charging station transaction system. After demand analysis, the blockchain-based charging station transaction system is designed. Finally, by building a Fabric network, designing data model and coding the smart contracts, the system of charging station transaction based on block chain is realized.
Integrating Digital Literacy Into EFL Classroom: A Study of Theory of Planned Behavior

Beliefs take a vital role in influencing students' decision about integrating digital literacy in EFL classroom. This case study investigated students' behavioral, control, and normative beliefs toward integrating digital literacy into the EFL classroom. This research used the Theory of Planned Behavior initiated by Ajzen to collect and analyze the data. The results of interviews and observations highlighted students' integration of digital literacy into EFL classroom referred to several beliefs. The behavioral beliefs (attitude toward advantages of a behavior) about the value of digital literacy for its effectiveness and efficiency potential to find information, to promote students cognitive skills and ethical awareness, and to improve English proficiency; normative beliefs (about social support) about the meeting of the expectation of school, teachers, parents, and friends; and control beliefs (about perceive behavioral control) about the opportunity and the availability to interact with the technology. This study highlighted a substantive theory related to the findings: Students' positive beliefs about integrating digital literacy into EFL classroom influence students' learning strategy. By having positive beliefs about the outcomes that students will get (behavioral beliefs), support from family, teachers, and the school environment (normative beliefs), as well as beliefs about the presence of factors that may facilitate or hinder the performance (control beliefs), students demonstrate the tendency to always use digital technology in learning English.
Japanese Zero Anaphora Resolution Can Benefit from Parallel Texts Through Neural Transfer Learning

Parallel texts of Japanese and a non-pro-drop language have the potential of improving the performance of Japanese zero anaphora resolution (ZAR) because pronouns dropped in the former are usually mentioned explicitly in the latter.However, rule-based cross-lingual transfer is hampered by error propagation in an NLP pipeline and the frequent lack of transparency in translation correspondences.In this paper, we propose implicit transfer by injecting machine translation (MT) as an intermediate task between pretraining and ZAR.We employ a pretrained BERT model to initialize the encoder part of the encoder-decoder model for MT, and eject the encoder part for finetuning on ZAR.The proposed framework empirically demonstrates that ZAR performance can be improved by transfer learning from MT.In addition, we find that the incorporation of the masked language model training into MT leads to further gains.
Correction to: On the Use of X-ray Computed Tomography in Assessment of 3D-Printed Components

A Correction to this paper has been published: 10.1007/s10921-020-00721-1
The Obstacles and Opportunities of the Mineral Resources in the Kurdistan Region, Iraq

The Kurdistan Region in Iraq is located in the extreme northeastern part of the Arabian Plate which is in a collision since the Late Cretaceous with the Iranian Plate. Therefore, large ophiolite bodies have been thrust along the northeastern margins of Kurdistan Region; accordingly, different metallic mineral can be associated with igneous and metamorphic rocks at Penjween, Qalat Diza and Rawandouz vicinities, besides, radioactive minerals like uranium and thorium. Moreover, large and long thrust fault has developed along the northern and northeastern parts of the Kurdistan Region. Along the plane of this huge thrust fault, hydrothermal liquids have deposited different metallic minerals as showings, especially between Zakho and Amadiyah towns. We have presented and discussed the discipline of mineral investment in Kurdistan Region, the announced minerals’ blocks for investment by the Ministry of Natural Resources in the Kurdistan Regional Government, the encouraging factors and obstacles of investments. To fulfill the scope of this work, we have used the best available and updated data as acquired from different sources. The main obstacles which contributed to the backwardness and non-development of the mining industry in the Kurdistan Region can be summarized in the nonexistence of a valid and promising mineral investment law which can attract the big international mining companies to invest in the region, adding to the nonexistence of comprehensive, detailed and mineral exploration studies which can give confident figures of the mineral and ore reserves in the region. The non-availability of a specialized mining education institution which prepares mining expertise and mining engineers who can lead the progress in this regard could count as another hurdle.
Implementation of Web Scraping on News Sites Using the Supervised Learning Method

Indonesia is one of the highest internet users in the world, including in the penetration of information on the internet, online news media.But in general news sites not only display news information, but most sites also display other information such as advertisements and also forms of navigation that interfere with news site readers and interfere with reader's comfort, from these problems this study aims to implement web scraping techniques with supervised learning methods and analyzing the form of DOM tree and XPath news sites.The supervised learning approach method is the method used in this study, which is one of the methods of machine learning.By combining these web scraping techniques with supervised learning, the aim is to be able to implement and optimize web scraping techniques to gather news information from various sites.To do basic web scraping namely knowing DOM patterns, XPath structure as a data model or selector at each site.The results of research in the form of a web scrap application that can retrieve news site content without copy paste and the data is stored in a database and displayed to the user application form for the reader without any ads and navigation that disturb the reader.
Knowledge Distillation with BERT for Image Tag-Based Privacy Prediction

Text in the form of tags associated with online images is often informative for predicting private or sensitive content from images.When using privacy prediction systems running on social networking sites that decide whether each uploaded image should get posted or be protected, users may be reluctant to share real images that may reveal their identity, but may share image tags.In such cases, privacy-aware tags become good indicators of image privacy and can be utilized to generate privacy decisions.In this paper, our aim is to learn tag representations for images to improve tagbased image privacy prediction.To achieve this, we explore self-distillation with BERT, in which we utilize knowledge in the form of soft probability distributions (soft labels) from the teacher model to help with the training of the student model.Our approach effectively learns better tag representations with improved performance on private image identification and outperforms state-of-the-art models for this task.Moreover, we utilize the idea of knowledge distillation to improve tag representations in a semi-supervised learning task.Our semi-supervised approach with only 20% of annotated data achieves similar performance compared with its supervised learning counterpart.Last, we provide a comprehensive analysis to get a better understanding of our approach.
Forensic examination of electronic signatures: A comparative study

The writing of names or signatures has great importance due to its use in authentication, validation, and authorization of documents. Moreover, handwritten signatures present an aura of personality and make an impression on many people. But now, with the emergence of new technologies, a variety of electronic writing media such as digital tablets and pens are being used to produce writings and signatures; and the conventional way to produce the writing using pen and paper is waning. With the changing ways of writing and signature production, the means of producing forged writing or signature are also bound to change. This has brought new challenges for handwriting examiners. In the present study, a comparative analysis of electronically captured signatures with pen-paper signatures has been performed to study the effect of changes in writing media. Signature samples were taken from the same subjects on paper and electronic pads. The similarities and differences with respect to class and line quality features between both signatures were analysed. It was observed that despite differences between the signatures produced by the same author, it is still possible to establish the authorship of signatures in the case of electronic signatures.
NewsBERT: Distilling Pre-trained Language Model for Intelligent News Application

Pre-trained language models (PLMs) like BERT have made great progress in NLP. News articles usually contain rich textual information, and PLMs have the potentials to enhance news text modeling for various intelligent news applications like news recommendation and retrieval. However, most existing PLMs are in huge size with hundreds of millions of parameters. Many online news applications need to serve millions of users with low latency tolerance, which poses huge challenges to incorporating PLMs in these scenarios. Knowledge distillation techniques can compress a large PLM into a much smaller one and meanwhile keeps good performance. However, existing language models are pre-trained and distilled on general corpus like Wikipedia, which has some gaps with the news domain and may be suboptimal for news intelligence. In this paper, we propose NewsBERT, which can distill PLMs for efficient and effective news intelligence. In our approach, we design a teacher-student joint learning and distillation framework to collaboratively learn both teacher and student models, where the student model can learn from the learning experience of the teacher model. In addition, we propose a momentum distillation method by incorporating the gradients of teacher model into the update of student model to better transfer useful knowledge learned by the teacher model. Extensive experiments on two real-world datasets with three tasks show that NewsBERT can effectively improve the model performance in various intelligent news applications with much smaller models.
Research on Experience Design of Shu Embroidery Techniques Based on Virtual Reality (VR) Technology

With the continuous development of digitization, the public's understanding of traditional Shu embroidery techniques is increasingly ignored. Through the combination of VR technology and Shu embroidery techniques, it needs to be deeply excavated and inherited. Based on the inheritance and development of Shu embroidery, literature research, user research and case study are used to collect and sort out the current situation of Shu embroidery and its digital inheritance. Around the semiotic characteristics and process flow of Shu embroidery techniques, the interface design and human-computer interaction experience are studied, and the Shu embroidery skill experience area and game interactive experience area are mainly designed. Conclusion the innovative inheritance method of Shu embroidery is developed from the transformation of intangible cultural heritage modernization, and VR advanced science and technology is used to provide reference for the design and research of digital protection platform of Shu embroidery. It is expected that Shu embroidery will inherit well following the development of the times, and then carry forward the traditional technology to the world. Doi: 10.28991/HEF-2020-01-04-01 Full Text: PDF
VOVU: A Method for Predicting Generalization in Deep Neural Networks

During the development of deep neural networks (DNNs), it is difficult to trade off the performance of fitting ability and generalization ability in training set and unknown data (such as test set). The current solution is to reduce the complexity of the objective function, using regularization methods. In this paper, we propose a method called VOVU (Variance Of Variance of Units in the last hidden layer) to maximize the optimization of the balance between fitting power and generalization during monitoring the training process. The main idea is to give full play to the predictability of the variance of the hidden layer units in the complexity of the neural network model and use it as a generalization evaluation index. In particular, we take full advantage of the last layer of hidden layers since it has the greatest impact. The algorithm was tested on Fashion-MNIST and CIFAR-10. The experimental results demonstrate that VOVU and test loss are highly positively correlated. This implies that a smaller VOVU indicates that the network has better generalization. VOVU can serve as an alternative method for early stopping and a good predictor of the generalization performance in DNNs. Specially, when the sample size is limited, VOVU will be a better choice because it does not require dividing training data as validation set.
Multi-feature keyword extraction method based on TF-IDF and Chinese grammar analysis

In order to solve the problem of over-dependence on word frequency in traditional TF-IDF algorithm, a multi-feature keyword extraction method combined with grammar analysis is proposed. By integrating the features of grammatical information, part of speech and word position, the method constructs a grading and weighting process for candidate keywords that takes into account various influencing factors comprehensively, and makes use of the grammatical information in the sentence to perform keyword sorting optimization operation to form the formally extracted keywords. The experimental results show that the proposed method is better than the traditional method, and can improve the problem of over-dependence on word frequency and extract satisfactory keywords.
Museum In The Cultural Space Of The City

In modern society, the problem of preserving cultural heritage and creating urban cultural space is of particular relevance.On a certain territory there are architectural monuments and cultural institutions that form the cultural environment of the city.The cultural space of historical cities has its own salient features.The creation and implementation of the town-planning concept, the construction of unique objects is the basis for the formation of urban space.Currently, among the criteria for evaluating the historical significance of cities, they note significant events associated with them, the names of famous natives, the presence of architectural monuments.At the same time, the preservation, study and use of the historical and cultural heritage of cities, its role in the formation and development of culture and education is of particular importance.An important role in the formation of the cultural space of both large and small cities is played by museums, which are collectors, keepers and distributors of historical knowledge and cultural traditions.The activities of museums as cultural centers are aimed at the general population.Despite the fact that most of the services of museums are paid, the museum remains one of the most accessible cultural institutions for citizens.Among the key functions of museums there is educational, pedagogical and advocacy function.
Pedestrian Reidentification Based on Feature Fusion and Metric Learning

The pedestrian recognition in public environment is influenced by the pedestrian environment and the dynamic characteristic boundary factors, so it is easy to produce the tracking error. In order to improve the ability of pedestrian re-identification in public environment, we need to carry out feature fusion and metric learning, and propose pedestrian re-identification based on feature fusion and metric learning. The geometric grid area model of pedestrian recognition in public environment is constructed, the method of fuzzy dynamic feature segmentation is used to reconstruct the dynamic boundary feature point of pedestrian recognition in public environment, the method of bottom-up modeling is used to design the dynamic area grid model of pedestrian recognition in public environment, the design of dynamic area grid model is three-dimensional grid area, the grayscale pixel set of pedestrian recognition dynamic constraint under public environment is extracted, the boundary feature fusion is carried out according to the distribution intensity of grayscale, the image fusion and enhancement information processing of pedestrian recognition under public environment, and the method of 3D dynamic constraint is used to realize the local motion planning of pedestrian recognition under public environment, and the recognition feature fusion and learning of pedestrian recognition under public environment is realized according to the result of contour segmentation. The simulation results show that the method is used for pedestrian recognition again in public environment, and the fuzzy judgment ability of pedestrian dynamic edge features is strong, which makes the error controlled below 10 mm, and the fluctuation of pedestrian recognition again is more stable, the recognition accuracy is higher and the robustness is better.
Upper bounds on the leakage of private data and an operational approach to Markovianity

We quantify the consequences of a private key leakage and private randomness generated during quantum key distribution. We provide simple lower bounds on the one-way distillable key after the leakage has been detected. We also show that the distributed private randomness does not drop by more than twice the number of qubits of the traced-out system. We further focus on irreducible private states, showing that their two-way distillable key is nonlockable. We then strengthen this result by referring to the idea of recovery maps. We further consider the action of a special case of side channels on some of the private states. Finally, we connect the topic of (non-)Markovian dynamics with that of hacking. In particular, we show that an invertible map is non-CP-divisible if and only if there exists a state whose the key witnessed by a particular privacy witness increases in time. This complements the recent result of J. Ko\l{}ody\ifmmode \acute{n}\else \'{n}\fi{}ski et al. [Phys. Rev. A 101, 020303(R) (2020)] where the log-negativity was connected with the (non-)Markovianity of the dynamics.
Text Multilevel Encryption Using New Key Exchange Protocol

The technological development in the field of information and communication has been accompanied by the emergence of security challenges related to the transmission of information. Encryption is a good solution. An encryption process is one of the traditional methods to protect the plain text, by converting it into inarticulate form. Encryption implemented can be occurred by using some substitute techniques, shifting techniques, or mathematical operations. This paper proposed a method with two branches to encrypt text. The first branch is a new mathematical model to create and exchange keys, the proposed key exchange method is the development of Diffie-Hellman. It is a new mathematical operations model to exchange keys based on prime numbers and the possibility of using integer numbers. While the second branch of the proposal is the multi-key encryption algorithm. The current algorithm provides the ability to use more than two keys. Keys can be any kind of integer number (at least the last key is a prime number), not necessarily to be of the same length. The Encryption process is based on converting the text characters to suggested integer numbers, and these numbers are converted to other numbers by using a multilevel mathematical model many times (a multilevel process depending on the number of keys used), while the decryption process is a one-level process using just one key as the main key, while the other keys used as secondary keys. The messages are encoded before encryption (coded by ASCII or any suggested system). The algorithm can use an unlimited number of keys with a very large size (more than 7500 bytes), at least one of them a prime number. Exponentiation is also used for keys to increase complexity. The experiments proved the robustness of the key exchange protocol and the encryption algorithm in addition to the security. Comparing the suggested method with other methods ensures that the suggested method is more secure and flexible and easy to implement.