Reviewerly/exHarmony huggingface dataset:
An Efficient Method For Generic Dsp Implementation Of Dilated Convolution

Dilated convolution is a well-known technique used in neural networks algorithms in AI/ML applications to increase receptive-field under analysis. Dilated convolution layer has an inherent property of capturing wider context in an image and long-term temporal characteristics in an audio signal. In this paper we propose a scheme that allows efficient/generic implementation of 2D dilated convolution and stride on typical DSPs where the instruction sets are well tuned for standard 1D and 2D filtering and convolution operations. The paper analyzes and morphs the basic structures of dilated convolution computations using a decomposition method similar to polyphase decomposition to map it to a friendly and efficient standard convolution operation. The method also naturally extends to include stride as a feature for dilated convolution. Using this scheme, we publish results on Tensilica’s HiFi5 platform achieving a computational cycle reduction in the order of 30X and memory reduction in the order of 150X against a standard implementation for dilation. We have also made the code available as a part of Cadence’s NN Library git hub code base on HiFi5 processor.
Comparing and Elucidating Blockchain Based Voting Mechanisms

Block chain is a distributed ledger technology that stores data on several servers globally, and anyone on the authenticated network can see data in near real-time. Data stored on a Block chain is immutable, time-stamped, unanimous, decentralized, secure, irreversible, fast, and consensus-dependent. Due to these benifits many of the real life software applications are adopting to block chain technology. Online voting process demands all the advantages offered by block chain technology, because block chain uses type of Distributed Ledger Technology that uses a cryptographic signature known as a hash to record transactions. There are numerous block chain based voting mechanicams/strategies have been proposed by various researchers. This paper aims at evaluating block chain based voting mechanisms versus a block chain voting strategy named “Performance Driven Framework for Mass e-Voting” strategy proposed by the authors of this paper [1] using Hyperledger Caliper. This paper also discusses detail the advantages, disadvantages and future possible research directions.
Optimizing Scrum's Effectiveness in Distributed Software Development Environment during the Pandemic Era: A Systematic Literature Review

The event of the Covid-19 pandemic and the prerequisite for social distance has caused significant disturbances in the realm of work, including software development companies, including software development companies. The implementation of Work From Home (WFH) is a step taken by the government and private organizations to remain productive in the pandemic era. Currently, there are many methods that companies can implement to support the software development process. The Scrum method is part of agile methods that can speed up and adaptability in software development project management. This study discusses a systematic literature review on optimizing the effectiveness of Scrum in a distributed software development environment during the pandemic era. Using data systematically extracted from this work, we offer a strategy to increase the effectiveness of Scrum implementations in a distributed software development environment during the pandemic era. This approach helps practitioners and researchers to better understand the problems of distributed software development projects and develop more effective project management solutions.
The Use of Field Notes and How to Document and/or Incorporate Notes into CSI Reports

Field notes are the notes taken by the crime scene investigator while on a scene or processing evidence in the lab. Field notes are imperative to an investigator when writing the final report. The field notes should be written in an organized fashion that the CSI can understand and interpret for their own purposes when they need to refer to them for the report. Field notes should include all pertinent information from the scene, including the date, time, weather, location, and any scene details that are needed for the report.
A Machine Translation Framework Based on Neural Network Deep Learning: from Semantics to Feature Analysis

This paper uses an encoder-decoder framework based on semantic to feature analysis to construct a neural machine translation model, let the machine automatically perform feature learning, transform corpus data into word vectors in a distributed representation, and use neural networks to implement source language and Direct mapping between target languages. A word alignment method based on deep neural network is proposed, which effectively utilizes the similarity of vocabulary and context information to model word alignment more accurately. This method uses neural network dimensionality reduction method to learn from unlabeled data. The low-dimensional vector representation of the ordering feature is then used to combine the low-dimensional feature representation with other features using a multi-layer neural network and integrated into a linear ordering model.
Toward English-Chinese Translation Based on Neural Networks

In traditional interactive English translation systems, English semantic context is not obvious in the process of the English translation, and the selection of optimal feature semantics does not reach the optimal translation solution, which leads to low translation accuracy. Toward this solation, a neural network (NN) based translation approach is proposed to predict word order differences in language translation and improve translation accuracy in long sentences. In this study, a multilayer NN model has been established to victories the unlabeled text words, realize the combination of word representation, vector features, and extract effective information of various sentences and semantics. In linear ranking frameworks, the NN is used to rank and score words, obtain semantic information of sample data, and predict the difference of word order. Experimental results show that the NN preorder model can significantly improve translation accuracy and system performance. The application of NN-based translation model in the practical translation process can reduce the effort of translation work, improve the efficiency of translation, and has a good practical significance.
Towards a Material Ethics of Computing

As concerns around climate change escalate, the need to address the environmental impacts of computing becomes more dire. While urgent action is needed, there is also opportunity to rectify longstanding inequities and injustices present in the relationship between computing and the environment. The aim of this one-day hybrid workshop is to gather researchers and practitioners and develop a material ethics of computing. We frame material ethics as a shared understanding of the relations between material and labor that shape digital infrastructures. Through presentations, discussions, and facilitated activities, we aim to build a research community to understand how computing facilitate sites of environmental damage and degradation, and also spaces for justice, change, and hope.
Feature Sharing Attention 3D Face Reconstruction with Unsupervised Learning from In-the-Wild Photo Collection

Abstract 3D face reconstruction is an active research topic in the field of computer vision research. It has a wide range of application scenarios. The number of 3D face annotation samples is small and the accuracy of the 3D reconstruction algorithm is insufficient. In this paper, we propose a shared attention module and a feature point constraint mechanism, and proposes a 3D face reconstruction algorithm that takes a single face image as input and uses a encoder-decoder network to predict the 3D face. The prediction view and light network introduces the attention mechanism module, which can improve the feature extraction ability of the network. The albedo and depth introduces shared features to help extract richer facial feature information. At the same time, the self-monitoring method of single image input avoids the high requirements of traditional methods for datasets. We conducted comparative experiments and ablation experiments on the BFM, and CelebA face datasets. The experimental results show that compared with Unsup3d and other representative 3D Face reconstruction algorithms, it has a certain improvement in the two evaluation indexes of Scale Invariant Depth Error (SIDE) and Mean Angle Deviation (MAD).
Accuracy Measure for Identification of Fake Blog Articles in Social Website Using Random Forest Over Naive Bayesian Classifier

Aim: To identify fake blog articles that are posted in social media and avoid spreading of fake news in social websites using Random Forest over Naive Bayesian Classifier. Materials and Methods: Fake news is identified using Novel Random Forest (n=3) and Naive Bayesian Classifier (n=3). The performances of the classifiers are evaluated based on their accuracy rate using twitter media dataset. Results and Discussion: The accuracies of Random Forest (99.07%) and Naive Bayesian Classifier (89.40%) and statistical significance (p=0.00) are identified. Conclusion: Random Forest produces significantly better accuracy value than the Naive Bayesian Classifier techniques.
Decarbonization of the economy: problem analysis with allowance for regional speciﬁcs and the need to develop the educational system

Aim. The presented study aims to analyze problems associated with the decarbonization of the economy in relation to regional specifics and the development of the educational system in the Russian Federation. Tasks. The authors investigate the concept of "decarbonization of the economy"; provide arguments in favor of decarbonization based on the systems approach, making allowance for the leading role of the Ministry of Science and Higher Education of the Russian Federation and the need to involve higher educational institutions and scientific institutions in solving the problem. Methods. This study uses general scientific methods of cognition, including analysis and synthesis, and the systems approach to identify the main problems of decarbonization, including in terms of the country's environmental and economic security. Results. Low-carbon economy, carbon taxes, and sequestration industry are trends that are likely to determine human development across the world in the next 50 years. Preservation of the environment should be a prerequisite for the development of civilization, but it should not be based on superficial solutions that are insufficiently substantiated scientifically. Solving the problems of decarbonization requires a targeted impact on the environment to reduce the load on it effectively and consistently. In this regard, studies conducted in Russia under the auspices of the Ministry of Science and Higher Education of the Russian Federation are particularly relevant. The expert center of the Ministry of Education and Science of the Russian Federation on carbon polygons is acting as a mediator between the activities of educational and scientific institutions. This center is also developing a strategy for monetizing the sequestration potential of carbon polygons in the regions. They provide territories with a unique ecosystem for the implementation of control measures for climate-active gases through universities and scientific organizations. The first elements of the national greenhouse gas accounting system have emerged. This, in turn, will not only increase the validity of the decarbonization policy, but will also allow regions to achieve social effects. Conclusion. Nowadays, as decarbonization projects are being implemented, it is necessary to develop the existing and open new carbon polygons operated by higher educational and scientific institutions, with the strengthening of the role of scientific institutions. This will make it possible to systematically decarbonize the economy, making allowance for the specifics and peculiarities of the regions, in cooperation with regional authorities and in broad public discussion.
Novel Framework for the Improvement of Object Detection Accuracy of Smart Surveillance Camera Visuals Using Modified Convolutional Neural Network Technique Compared with Global Color Histogram

The objective of the proposed work is to evaluate the accuracy of Global Color Histogram (GCH) and Modified Convolutional Neural Networks Techniques (MCNNT) by changing the hierarchical data processing for Smart Surveillance System. Methods and materials: Our novel framework with MCNNT uses the data in hierarchical models of data processing, which is constructed with fully connected layers and the neurons were connected to every other node to handle complex object detection tasks and evaluated using the sample size of 20 per group with model image dataset. Result: The recorded mean accuracy of MCNNT (95.28%) is better than GCH (80.48%). There is a statistical significant difference in accuracy for two algorithms performed by independent sample t-test (P&lt;0.05) for the Confidence Interval (CI) 95%. Conclusion: MCNNT appears to have better accuracy than GCH for object detection of Smart Surveillance System.
Deep learning and transfer learning of earthquake and quarry-blast discrimination: Applications to southern California and eastern Kentucky

Earth and Space Science Open Archive This preprint has been submitted to and is under consideration at Journal of Geophysical Research - Solid Earth. ESSOAr is a venue for early communication or feedback before peer review. Data may be preliminary.Learn more about preprints preprintOpen AccessYou are viewing the latest version by default [v1]Deep learning and transfer learning of earthquake and quarry-blast discrimination: Applications to southern California and eastern KentuckyAuthorsJunZhuiDLihuaFangFajunMiaoLipingFanJiZhangiDZefengLiiDSee all authors Jun ZhuiDUniversity of Science and Technology of ChinaiDhttps://orcid.org/0000-0002-7244-4511view email addressThe email was not providedcopy email addressLihua FangInstitute of Geophysics, China Earthquake Administrationview email addressThe email was not providedcopy email addressFajun MiaoJiangsu Earthquake Administration, China Earthquake Administrationview email addressThe email was not providedcopy email addressLiping FanChina Earthquake Administrationview email addressThe email was not providedcopy email addressJi ZhangiDUniversity of Science and Technology of ChinaiDhttps://orcid.org/0000-0001-9582-7577view email addressThe email was not providedcopy email addressZefeng LiiDCorresponding Author• Submitting AuthorUniversity of Science and Technology of ChinaiDhttps://orcid.org/0000-0003-4405-8872view email addressThe email was not providedcopy email address
Healthcare Chain Network Framework for Monitoring and Verifying Pharmaceutical Supply Chain

Prescription drug abuse and doctor shopping are major problems in the healthcare field that have led to a significant number of deaths as a consequence of drug misuse. Doctors prescribe too much fentanyl, Demerol, and sleeping pills, along with anxiety medications, that are legal and frequently abused. In order to combat prescription drug abuse, it is imperative to develop an efficient monitoring and identification system. Blockchain technology is a promising solution to tracking prescriptions because of its decentralization and auditability, which allow medical records to be more secure as well as more reliable. Blockchain technology can help ensure the authenticity, integrity, and transparency of drug distribution between pharmacies and doctors. In this paper, we aim to develop a system of managing the integrity of the drug supply chain by integrating Hyperledger Fabric blockchain technology with Interplanetary File System (IPFS). Because of its secure encryption framework and anti-fraud and anti-forgery measures, it has prevented prescription abuse. A smart health care ecosystem is created by using smart contracts to track drugs on the blockchain. In addition, by using this approach we are able to recognize and track drugs that may be misused by individuals who shop for doctors and hop between pharmacies. The cryptographically safe and reliable framework created by Healthcare Chain can be used by doctors, pharmacists, and patients to improve how opioid prescriptions and medication are administered.
TREASURES OF THE FUND OF RARE BOOKS AND ARCHIVAL DOCUMENTS OF THE NATIONAL LIBRARY OF THE REPUBLIC OF SAKHA (YAKUTIA)

The article examines the owner's collections of northern scholars in the fund of the Scientific Research Center of Book Monuments of the National Library of the Republic of Sakha (Yakutia). The library has a collection of rare books and valuable documents, significant in number and content. The staff of the main book depository of the northern republic is carrying out a large-scale and socially significant in terms of goals and objectives systematic work to identify, study and popularize book monuments and archival documents. The paper provides a brief history of the formation of the center's fund, information from the biographies of the owners of private libraries: linguist and ethnographer Glafira Makarievna Vasilevich, scholar-northern scholar Claudia Aleksandrovna Novikova, the scientist-northern scholar Chuner Mikhailovich Taksami, historian-ethnographer Ilya Samuilovich Gurvich, ethnologist Ulla Johansen. The quantitative and qualitative characteristics of rare collections transferred to the National Library of the Republic of Sakha (Yakutia) are substantiated, the value of personal libraries as cultural phenomena — book monuments and information sources on languages, history, ethnography, folklore of the peoples of the North, Siberia and the Far East. The National Library of the Republic of Sakha (Yakutia) fulfills a high mission — it carries out a huge range of measures to preserve rare books and documents, and at the same time provides access to the intellectual and cultural heritage of Russian northern scholars. The article actualizes the problem of preserving and popularizing knowledge about culture, languages, folklore, ethnography of the peoples of the North, Siberia and the Far East in the context of the loss of traditional values of material and spiritual culture by the indigenous people of the North and the Arctic at the beginning of the 21st century. The authors consider the study of the richest funds of the National Library of the Republic of Sakha (Yakutia) in the process of a comprehensive analysis of the owner's collections of book monuments and the archival heritage of Russian northern scholars as a priority task of the historical and cultural sciences. The paper considers the main directions of the activities of the National Library of the Republic of Sakha (Yakutia) in the field of preserving the phenomena of the cultural heritage of the northern peoples; the activity of the Scientific Research Center of Book Monuments has been analyzed; the personal collections of northern scholars are described and systematized. As a general task, the postulate of the development of the concept of preservation for future generations of the values of the cultural heritage of the peoples of the North and the Arctic is put forward.
A flexible Bayesian mixture approach for multi-modal circular data

In this article, we consider multi-modal circular data and nonparametric inference. We introduce a doubly flexible method based on Dirichlet process circular mixtures in which parameter assumptions are relaxed. We assess and discuss in simulation studies the efficiency of the proposed extension relative to the standard finite mixture applications in the analysis of multi-modal circular data. The real data application shows that this relaxed approach is promising for making important contributions to our understanding of many real-life phenomena particularly in environmental sciences such as animal orientations.
Analyzing the effective role of formal credit and technological development for rice cultivation

We investigated the short-term (ST) and long-term (LT) effects of formal credit, technological development (via improved quality seeds, and use of chemical fertilizers) on rice cultivation in Nepal over the period 1991 to 2019. The ARDL bounds testing co-integration was applied to estimate the long-term association between formal credit, technological development, cultivated area, energy consumption, and rice cultivation. Simultaneously, the VECM method was applied to discover the direction of the causal association between the considered variables. The findings were as follows: (1) the present study verified a long-term co-integration connection between the variables. (2) Formal credit significantly increased rice cultivation in the LT and ST. (3) Technological development factors such as improved quality seeds significantly augmented rice cultivation only in the ST. (4) Correspondingly, chemical fertilizers significantly enhanced rice cultivation in LT and ST. (5) The causality outcomes confirmed a strong LT impact of all variables on rice production. (6) The ST bidirectional causality runs from formal credit and cultivation area to rice cultivation, whereas the causal association of the remaining variables is uni-directional. (7) Additionally, the outcomes of IRF and VDM again confirmed that all factors influence rice cultivation in LT. This study verified that credit played a dynamic role in enhancing rice production and improving food security in Nepal. Consequently, based on our findings, we recommend efforts to increase rice production and improve credit supply in farming communities through financial institutions at flexible interest rates and ensure proper credit utilization.
Compressed Data Sharing Based On Information Bottleneck Model

In this paper, we consider privacy-preserving compressed image sharing, where the goal is to release compressed data whilst satisfying some privacy/secrecy constraints yet ensuring image reconstruction with a defined fidelity. The privacy-preserving compressed image sharing is addressed using a machine learning framework based on an information bottleneck with a shared secret key for authorized users. In contrast, an adversary observing the protected compressed representation tries to either reconstruct the data or deduce some privacy-sensitive attributes such as gender, age, etc. The inference task on the adversary’s side is performed without the knowledge of the shared secret key and is based on an adversarial mutual information maximization between the privacy-protected compressed representation and targeted attributes. The proposed framework is experimentally validated on the CelebA dataset.
Joint and Adversarial Training with ASR for Expressive Speech Synthesis

Style modeling is an important issue and has been proposed in expressive speech synthesis. In existing unsupervised methods, the style encoder extracts the latent representation from the reference audio as style information. However, the style information extracted from the style encoder will entangle some content information, which will cause conflicts with the real input content, and the synthesized speech will be influenced. In this study, we propose to alleviate the entanglement problem by integrating Text-To-Speech (TTS) model and Automatic Speech Recognition (ASR) model with a share layer network for joint training, and using ASR adversarial training to eliminate the content information in the style information. At the same time, we propose an adaptive adversarial weight learning strategy to prevent the model from collapsing. The objective evaluation using word error rate(WER) demonstrates that our method can effectively alleviate the entanglement between style and content information. Subjective evaluation indicates that the method improves the quality of synthesized speech and enhances the ability of style transfer compared with the baseline models.