Reviewerly/exHarmony huggingface dataset:
Decentralized Cloud and File System for Blockchain Environment

Blockchain is considered as an advanced technology that is always having issue related to data security and privacy and is widely used in cloud as well as fog-based environment for high-performance applications. Blockchain technology is now extensively employed in business and social sectors so that overall integrity can be maintained with high security standards. These segments include the Internet in general, social networking, e-commerce, transportation, logistics, professional communications, and e-governance. Blockchain is a high-performance security-aware technology used in this segment with the secured file distribution in decentralized environment with secure digital ledger. The digital ledger is very transparent, and there is no scope of manipulation by agent. The digital records of all the transactions are time-stamped in the blockchain. Ledger and transactions are irreversible once completed. This research manuscript is having the focus on the integration of InterPlanetary File System (IPFS) for the high-performance security and anonymity-based transactions with privacy. The IPFS protocol is the key technology and file system that is used in blockchain technology for the transmission of data via secured channels in which hacking or sniffing attempts can be avoided. The work integrates IPFS implementation with blockchain environment with demonstration toward the secured transmission.
Advanced Authentication Method by Geometric Data Analysis Based on User Behavior and Biometrics for IoT Device with Touchscreen

The Internet of Things (IoT) technology is rapidly being applied to real life, but the application of a corresponding secure and convenient authentication method is still in significant challenge. So far, pattern, password and fingerprint authentication are the most used methods, but it is important to address various security vulnerabilities and limitations of these approaches. In the case of fingerprint recognition, additional hardware such as a fingerprint scanner is required, which causes cost issues and could be vulnerable to fingerprint theft. To solve this problem, this paper proposes a model that uses both biometric and behavioral authentication at the same time. This method exploits the biometric authentication that measures the length of the contact region that occurs when three fingers are placed side by side on the touch screen or pad. In addition, it utilizes the behavioral authentication itself using three-finger L-shape touch, as well as secure geometric information generated by smart watch such as acceleration sensors. Therefore, this proposed model will be useful to implement more secure, rapid and user-friendly way of authentication in many practical busy and buzzling field where deal with sensitive private information.
Hardware performance analysis of RSA cryptosystems on FPGA for wireless sensor nodes

In order to enhance the speed with good flexibility and physical security of design it is required to implement public key cryptographic algorithm on reconfigurable devices. The dedicated accelerators or coprocessors are combined with hardware solutions to support high-speed data cryptographic techniques in wireless sensor nodes. Two architectures are proposed (a) RSA-CIPHER128 and (b) mMMM42 to check the appropriateness for implementation in Wireless Sensor Nodes. Synthesis and simulation of VHDL code is carried out using Xilinx-ISE for the architectures. Results are obtained on different FPGA devices to compare the performance with speed and area. RSACIPHER128 multiplier gives the good execution speed. Where area is not a constraint, RSA cryptosystem with mMMM42 is appropriate for Wireless Sensor Network(WSN) nodes. Cryptosystem with mMMM42 consumes adoptable hardware in FPGA for WSN nodes. On Atrix-7 device the cryptosystem with RSA-CIPHER128 gives less area utilization and mMMM42 delivers good throughput. This trade-off between the design metrics leads to the designer to select the architecture depending on the applications. Throughput achievement with utilization of Slices(TPS) and LUTs(TPL) is analyzed to evaluate the performance of architectures.
The Ai Music Generation Challenge 2020: Double Jigs in the Style of O'Neill's ``1001''

The Ai Music Generation Challenge 2020 had three objectives: 1) to promote meaningful approaches to evaluating artificial intelligence (Ai) applied to music;2) to see how music Ai research can benefit from considering traditional music, and how traditional music might benefit from music Ai research; and 3)to facilitate discussions about the ethics of music Ai research applied to traditional music practices.There were six participants and a benchmark in the challenge, each competing to build an artificial system that generates the most plausible double jigs, as judged against the 365 published in solved'', but that the evaluation of such systems can be done in meaningful ways.The article ends by reflecting on the challenge and considering the coming 2021 challenge.
Analysing Human Strategies of Information Transmission as a Function of Discourse Context

Speakers are thought to use rational information transmission strategies for efficient communication (Genzel and Charniak, 2002;Aylett and Turk, 2004;Jaeger and Levy, 2007).Previous work analysing these strategies in sentence production has failed to take into account how the information content of sentences varies as a function of the available discourse context.In this study, we estimate sentence information content within discourse context.We find that speakers transmit information at a stable rate-i.e., rationally-in English newspaper articles but that this rate decreases in spoken open domain and written task-oriented dialogues.We also observe that speakers' choices are not oriented towards local uniformity of information, which is another hypothesised rational strategy.We suggest that a more faithful model of communication should explicitly include production costs and goal-oriented rewards.
Fault Tolerant Control combining Reinforcement Learning and Model-based Control

Fault tolerant control (FTC) focuses on developing algorithms to accommodate the impact of system faults while allowing the system to continuously operate in a degraded manner. Additionally, data-driven methods like reinforcement learning (RL) have shown excellent performance for complex continuous control tasks. However, faults affect the system dynamics which disrupt optimal policy guarantees as the Markov Property cannot be satisfied. In this work, we propose a scheme based on a combination of parameter estimation, RL, and model-based control to handle faults in a continuous control environment. We empirically demonstrate our approach on a complex octocopter trajectory-tracking task subject to single and multi motor faults. We show improved performance compared with nominal hierarchical PID control for large magnitude faults. Lastly, we demonstrate our approach’s robustness against noisy parameter estimation.
Visualizing Model-Based Product Definitions in Augmented Reality

Abstract Augmented reality (AR) technologies present immense potential for the design and manufacturing communities. However, coordinating traditional engineering data representations into AR systems without loss of context and information remains a challenge. A major barrier is the lack of interoperability between manufacturing-specific data models and AR-capable data representations. In response, we present a pipeline for porting standards-based Product Manufacturing Information (PMI) with three-dimensional (3D) model data into an AR scene. We demonstrate our pipeline by interacting with annotated parts while continuously tracking their pose and orientation. Our work provides insight on how to address fundamental issues related to interoperability between domain-specific models and AR systems.
How Three Different Translators of The Holy Qur’an Render Anthroponyms from Arabic into English

The present paper examines anthroponyms in the Holy Qur'an in three different English translations to shed light on how procedures used by translators can help target-language (TL) readers understand the implied meaning of anthroponyms. In order to conduct the research, the anthroponyms in the Holy Qur'an were isolated and English equivalents were identified. Then Vermes’s (2003) model was applied to the collected data to find answers to the following research questions: (1) What strategies are used most frequently by the translators examined to render the Qur’anic anthroponyms into the target-language (TL)?; (2) How consistent are the translators in using particular strategies when translating the anthroponyms?; (3) Does the type of translator affect their choice of translation strategy?; (4) Does the model suggested by Vermes (2003) cover all of the strategies employed by the three translators?; and (5) Which procedures are source-language-oriented, TL-oriented, or deep-reader oriented? Overall, the findings indicated that the procedures most frequently used by the translators were “substitution” and “transference.” It was found that the native speaker of neither Arabic nor English foreignized 96.80% of the Qur’anic anthroponyms by using “transference,” while the native translators of either the target-language or the source-language domesticated 71.00% of the anthroponyms by using “substitution.” “Substitution” was used when an exact Biblical equivalent for the Qur’anic anthroponym existed. Otherwise, “transference” was used along with notes to transport the meaning and form while remaining faithful to the intended meaning of the sacred text.
A local density-based outlier detection method for high dimension data

The researchers faced challenges in the outlier detection process, mainly when deals with the high dimensional dataset; to handle this problem, we use The principal component analysis. Outlier detection or anomaly detection, with local density-based methods, compares the density of observation with the surrounding local density neighbors. We apply the outlier score as a measure of comparison. In this research, we choose different density estimation functions and calculated different distances. Weighted kernel density estimation with adaptive bandwidth for multivariate kernel density estimation(Gaussian) considered the $KNN$ and RNN. $KNN$ is considered too for the Epanenchnikov kernel density estimation. Lastly, we estimate the LOF as a base method in detecting outliers. Extensive experiments on a synthetic dataset have shown that RKDOS and EPA are more efficient than LOF using the precision evaluation criterion.
Look youse guys and gals, dat just ain’t right

When I invite authors to submit a punctum to Autophagy, my e-mail includes the following: “Note for international authors: I would like to point out that I personally edit all the puncta for accuracy, but also for English grammar and spelling. I make this point to all international authors as I do not want you to worry extensively about the writing. As a native English speaker, it is easy for me to make small changes of this nature.” I do not claim to be an expert in English grammar; however, I am indeed a native English speaker, I read a lot, and I am even fond of using the dictionary (both hard copy and online). Also, I do a lot of editing. Thus, I thought I would share some common mistakes to help reduce the required edits for papers that are submitted to Autophagy.
Covered Information Disentanglement: Model Transparency via Unbiased Permutation Importance

Model transparency is a prerequisite in many domains and an increasingly popular area in machine learning research. In the medical domain, for instance, unveiling the mechanisms behind a disease often has higher priority than the diagnostic itself since it might dictate or guide potential treatments and research directions. One of the most popular approaches to explain model global predictions is the permutation importance where the performance on permuted data is benchmarked against the baseline. However, this method and other related approaches will undervalue the importance of a feature in the presence of covariates since these cover part of its provided information. To address this issue, we propose Covered Information Disentanglement CID, a framework that considers all feature information overlap to correct the values provided by permutation importance. We further show how to compute CID efficiently when coupled with Markov random fields. We demonstrate its efficacy in adjusting permutation importance first on a controlled toy dataset and discuss its effect on real-world medical data.
Development of a personalized model of teaching mathematics by means of interactive novels to improve the quality of pupils' educational results

The problem and the aim of the study. The implementation of educational programs in different subjects of modern school, including mathematics, is based on the principles of personalized learning, the active use of digital technologies and means of interactive interaction to ensure a high level of mathematical education. The purpose of the article is to investigate the features of the development of a personalized model of teaching mathematics by means of interactive novels to improve the quality of educational results. Research methods. The theoretical analysis and generalization of scientific literature on the problems of improvement of the quality of mathematical education, of the personalization of education by digital technologies, of the didactic role of the visual interactive literature as a kind of computer games, are applied. The main methodological principle of the study is determined by the key condition of personalized learning. That is the freedom of students to choose their educational path. The empirical methods were used: observation, analysis of the results of work with AXMA Story Maker application (answer selection, number of attempts to find a solution and of publications read). The study involved 121 7th-grade pupils of school №11, Kirov. In the experiment, the Fisher criterion was used to process the results. Results. The members of the experimental group were involved in the study of mathematical theory, solving of the tasks according to their personalized educational path in the interactive AXMA Story Maker space. Personalization is supported by self-selection of the pupil's response in a non-linear visual novel environment. Statistically significant differences between the experimental and control groups in terms of the level of educational results were revealed (φcrit = 1.64 &lt;φemp = 2.964). In conclusion, the features that should be considered when designing a personalized model of teaching mathematics by means of interactive novels are summarized: correlating the didactic goal and the result of educational research work in a non-linear environment, choosing a plot for a visual short story, considering the text component and the personalized trajectory of cognition etc.
Deep learning-based hydrothermal alteration mapping using GaoFen-5 hyperspectral data in the Duolong Ore District, Western Tibet, China

Hydrothermal alteration mapping is of great significance for porphyry copper deposit (PCD) exploration. Due to different metallogenetic environments, hydrothermal processes are heterogeneous especially in large-scale ore districts, leading to diverse physicochemical properties and spectral variations of wall-rock alteration. Conventional mapping methods mainly rely on shallow spectral features to identify hydrothermal alteration and cannot fully characterize the properties of the materials. By contrast, deep learning (DL) frameworks with multiple nonlinear layers can extract deep spectral and spatial features from hyperspectral imagery which is more robust and discriminative. Deep models were employed to the GaoFen-5 (GF-5) hyperspectral dataset in the Duolong district, to investigate the role of deep features in hydrothermal alteration mapping. For this purpose, we assess stacked autoencoder and several convolution neural networks which are computationally affordable for achieving large-area mineral exploration. More importantly, the mixed convolutions and covariance pooling algorithm achieved the best mapping results with the overall accuracy of 96.77%. The feature fusion method is also recommended, because of its lightweight structures. These two spectral–spatial classifiers produced appealing classification performance while reducing the misclassification among spectrally similar minerals and alleviating noise, demonstrating their powerful learning capabilities. Finally, spatial patterns of the hydrothermal alteration in the Duolong district can be taken as an important indicator of erosion degree of the porphyry-epithermal system. It is proved that the combination of the DL methods and the GF-5 hyperspectral data is effective in large-area mineral exploration and can be applied to other PCDs in the neighboring Bangong Co-Nujiang metallogenic belt.
CNNBiF: CNN-based Bigram Features for Named Entity Recognition

Transformer models fine-tuned with a sequence labeling objective have become the dominant choice for named entity recognition tasks.However, a self-attention mechanism with unconstrained length can fail to fully capture local dependencies, particularly when training data is limited.In this paper, we propose a novel joint training objective which better captures the semantics of words corresponding to the same entity.By augmenting the training objective with a group-consistency loss component we enhance our ability to capture local dependencies while still enjoying the advantages of the unconstrained self-attention mechanism.On the CoNLL2003 dataset, our method achieves a test F1 of 93.98 with a single transformer model.More importantly our fine-tuned CoNLL2003 model displays significant gains in generalization to out of domain datasets: on the OntoNotes subset we achieve an F1 of 72.67 which is 0.49 points absolute better than the baseline, and on the WNUT16 set an F1 of 68.22 which is a gain of 0.48 points.Furthermore, on the WNUT17 dataset we achieve an F1 of 55.85, yielding a 2.92 point absolute improvement.
3D Mobile Data Visualization

This chapter surveys the space of three-dimensional (3D) mobile visualizations, that is, 3D abstract or spatial data on mobile 2D displays or 3D immersive displays. As a playful “case study” a scenario from the film Aliens is used, where the marines are overrun by aliens in the ceiling, as their mobile visualization device fails to show them the height dimension of the space around them. This example is used to illustrate how different mobile and 3D interaction techniques could have prevented the misunderstanding, using both hypothetical descriptions of the improved movie action and a scientific discussion of these scenarios and their implications.
Combining data augmentation and domain information with TENER model for Clinical Event Detection

Abstract Background In recent years, with the development of artificial intelligence, the use of deep learning technology for clinical information extraction has become a new trend. Clinical Event Detection (CED) as its subtask has attracted the attention from academia and industry. However, directly applying the advancements in deep learning to CED task often yields unsatisfactory results. The main reasons are due to the following two points: (1) A great number of obscure professional terms in the electronic medical record leads to poor recognition performance of model. (2) The scarcity of datasets required for the task leads to poor model robustness. Therefore, it is urgent to solve these two problems to improve model performance. Methods This paper proposes a combining data augmentation and domain information with TENER Model for Clinical Event Detection. Results We use two evaluation metrics to compare the overall performance of the proposed model with the existing model on the 2012 i2b2 challenge dataset. Experimental results demonstrate that our proposed model achieves the best F1-score of 80.26%, type accuracy of 93% and Span F1-score of 90.33%, and outperforms the state-of-the-art approaches. Conclusions This paper proposes a multi-granularity information fusion encoder-decoder framework, which applies the TENER model to the CED task for the first time. It uses the pre-trained language model (BioBERT) to generate word-level features, solving the problem of a great number of obscure professional terms in the electronic medical record lead to poor recognition performance of model. In addition, this paper proposes a new data augmentation method for sequence labeling tasks, solving the problem of the scarcity of datasets required for the task leads to poor model robustness.
Engineering Document Summarization Using Sentence Representations Generated by Bidirectional Language Model

Abstract In this study, the extractive summarization using sentence embeddings generated by the finetuned BERT (Bidirectional Encoder Representations from Transformers) models and the K-Means clustering method has been investigated. To show how the BERT model can capture the knowledge in specific domains like engineering design and what it can produce after being finetuned based on domain-specific datasets, several BERT models are trained, and the sentence embeddings extracted from the finetuned models are used to generate summaries of a set of papers. Different evaluation methods are then applied to measure the quality of summarization results. Both the automatic evaluation method like Recall-Oriented Understudy for Gisting Evaluation (ROUGE) and the statistical evaluation method are used for the comparison study. The results indicate that the BERT model finetuned with a larger dataset can generate summaries with more domain terminologies than the pretrained BERT model. Moreover, the summaries generated by BERT models have more contents overlapping with original documents than those obtained through other popular non-BERT-based models. It can be concluded that the contextualized representations generated by BERT-based models can capture information in text and have better performance in applications like text summarizations after being trained by domain-specific datasets.
Research on Intelligent Robot Path Planning Based on Spatiotemporal Grid Map in Dynamic Environment

To improve the adaptability of the intelligent robot in a dynamic environment, a spatiotemporal grid map is constructed, which can integrate static and dynamic obstacles in a unified manner. Then, an improved spatiotemporal dynamic window approach (DWA) algorithm is proposed, which can perform local path planning in spatiotemporal grid maps. Finally, the A* algorithm is integrated with the improved spatiotemporal DWA algorithm, and a path planning method of spatiotemporal grid map combined with the A* algorithm is proposed. Experiments have shown that this method can not only ensure global optimality of the path but also can better adapt to the dynamic environment and avoid dynamic obstacles.
Monitoring the quality of the work of experts when they conduct accreditation examinations of educational programs

The purpose of the study is to monitor the quality of the work of experts when they conduct accreditation examinations of educational programs. Today, the key element of accreditation of educational programs is the assessment of the activities of higher education institutions on this program by an independent visiting expert group. The expert group is formed for the educational program and consists of three people: two authoritative scientific and pedagogical (scientific) workers who have experience in educational activities in the relevant specialty and one authoritative representative of the student body who has experience in training in this specialty (in t of a graduate student, if it comes to accreditation of third-level higher education programs). The result of the study is the identification of key aspects of the features of monitoring the quality of the work of experts when they conduct accreditation examinations of educational programs