Reviewerly/exHarmony huggingface dataset:
A Secure Electronic Voting System Using Blockchain Technology

By voting, people can decide the direction of change and development since the people have a sense of “Ownership of Government”. Voting is a very important process in any democratic country. For a secure voting, blockchain technology used by electronic voting is more effective and secure. Many digital services are being developed by the blockchain technology. The investigation on the blockchain topic is still proceeding; however, the research mainly focuses on its legal and technical issues, but this novel concept can be taken advantage of and can be used to create advanced digital services. This paper is going to grip the open-source blockchain technology, and we are going to propose an e-voting system based on this technology. We are using the SHA-256 hash function to keep the voter anonymous, and we are enabling the dual authentication process while the voters’ cast their vote. This will result in the minimalization of malicious activities since it is safe, reliable, secure, and trustworthy.
Advanced Neural Network Algorithms

In this chapter, recently developed neural network algorithms are introduced, including extreme learning machine and autoencoder. These algorithms are popularly adopted in deep learning models. Particularly, autoencoder can be used in shrinking the dimension of inputs and outputs as well as hidden nodes.
Research on The Application of Identifier Resolution and Blockchain Technology in The New Energy Field*

Abstract With the development of new energy power generation technology, distributed generation technology and small personal power generation have become an important development direction for new energy power generation. However, distributed generation technology may have the information island phenomenon. due to its construction, grid connection, transaction and other links with data scattered in various departments, enterprises. Besides, difference in products, equipment and data type and lower degree of data sharing in links aggravate this phenomenon. In order to get through and integrate the data information of each new energy system, realize the sharing and centralized management of platform data resources, and remove the business barriers between various departments and enterprises, this paper proposes to use the electric power Internet of things identifier resolution system to standardize the data of all links in the new energy system, and each link in the system uses open data to create more value. In addition, this paper also uses a combination of blockchain technology and the Identifier resolution system to optimize new energy power transactions, and proposes a method for sharing private data in a small range of users. Inject new impetus into the development of new energy distributed generation.
Time-Correlated Sparsification for Communication-Efficient Federated Learning

Federated learning (FL) enables multiple clients to collaboratively train a shared model without disclosing their local datasets. This is achieved by exchanging local model updates with the help of a parameter server (PS). However, due to the increasing size of the trained models, the communication load due to the iterative exchanges between the clients and the PS often becomes a bottleneck in the performance. Sparse communication is often employed to reduce the communication load, where only a small subset of the model updates are communicated from the clients to the PS. In this paper, we introduce a novel time-correlated sparsification (TCS) scheme, which builds upon the notion that sparse communication framework can be considered as identifying the most significant elements of the underlying model. Hence, TCS seeks a certain correlation between the sparse representations used at consecutive iterations in FL, so that the overhead due to encoding and transmission of the sparse representation can be significantly reduced without compromising the test accuracy. Through extensive simulations on the CIFAR-10 dataset, we show that TCS can achieve centralized training accuracy with 100 times sparsification, and up to 2000 times reduction in the communication load when employed together with quantization.
Donald V. Helmberger (1938–2020)

Obituary| January 20, 2021 Donald V. Helmberger (1938–2020) Thorne Lay Thorne Lay * 1Earth and Planetary Sciences Department, University of California Santa Cruz, Santa Cruz, California, U.S.A. *Corresponding author: tlay@ucsc.edu Search for other works by this author on: GSW Google Scholar Seismological Research Letters (2021) 92 (2A): 621–622. https://doi.org/10.1785/0220190473 Article history first online: 20 Jan 2021 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Tools Icon Tools Get Permissions Search Site Citation Thorne Lay; Donald V. Helmberger (1938–2020). Seismological Research Letters 2021;; 92 (2A): 621–622. doi: https://doi.org/10.1785/0220190473 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search nav search search input Search input auto suggest search filter All ContentBy SocietySeismological Research Letters Search Advanced Search Donald V. Helmberger, Ph.D. and Smits Family Professor of Geophysics Emeritus at Caltech, and one of the most impactful seismologists to have lived, died on 13 August 2020. Don was born 23 January 1938, the youngest child of 13, in the small rural town of Perham, Minnesota. He completed his bachelor’s degree in physics at the University of Minnesota in 1961. That summer, he participated in a cruise involving seismic imaging of the oceanic crust in the Bering Sea and was inspired by the challenge of interpreting the recorded seismic waveforms—the beginning of a lifelong passion. That experience drew him... You do not currently have access to this article.
Conceptual framework for quality assessment in human-centric 6G XR services

Abstract The 6G communication networks and technologies have to enable the development of wide variety of low latency and ultra-reliable services. Furthermore, the 6G is foreseen to be the era of new devices which integrate our natural human senses in order to create a fully new concept of human-machine interactions. The eXtended Reality (XR) based wearables will surely improve the way we collaborate with modern technologies by merging the real and virtual world in one innovative continuum in order to expand the possibilities of using and interacting with different types of modern or already well-known IT services. In this context, a vital 6G research area is the design of different flexible quality assessment metrics, targets and measures that integrate human perceptions and physical factors from human physiology. This metric design should consider the multisensory nature of XR devices by incorporation of different objective network-related as well as strongly subjective human-related features like sight, touch, smell, taste, body physiology, psychological traits, gestures and even sixth sense. In this paper a conceptual framework incorporating a mixture of new and classical quality assessment constructs like Quality of Service (QoS) and Quality of Experience (QoE) is proposed for 6G networks from human-centric point of view. It considers the development of new types of intelligent XR technologies and give an insight in regard to overall human experience assessment, modelling and prediction.
Modification of the Algorithm Used by Automated Hematology Analyzer XN-3000 Improves Specificity in the Detection of Schistocytes

Background This study investigated the feasibility and accuracy of an automated hematology analyzer in the detection of schistocytes. Methods In total, 1,026 peripheral blood samples were collected. Schistocytes were morphologically diagnosed by manual examination of digital microscopic red blood cell images captured by a Sysmex DI-60. Automated diagnoses were performed using a Sysmex XN-3000. Result The accuracy of automated diagnosis using the XN-3000 with the default algorithm fragments? was determined through comparison with the findings of morphological examination. The comparison showed a sensitivity of 100% and a specificity of 41.6% for automated diagnosis. To improve the low specificity, a two-step analysis was performed. Use of the algorithm fragments? in XN-3000 followed by an off-line analysis using the cell parameter %FRC (percent fragmented red blood cells) yielded a sensitivity of 86.5% and a specificity of 70.3%. Conclusions Our study indicated that combined use of the %FRC parameter with the default algorithm of the Sysmex XN-3000 automated hematology analyzer can improve the low specificity of the default algorithm in rapid screening for schistocytes.
Person Re-Identification Based on Significant Color With the Spatial Correspondence

Video surveillance has played a key role to find an individual just in case of a criminal offense. More studies were done to make the surveillance process autonomous. In this, the person re-identification technique helps to identify people. The surveillance cameras are normally mounted at a height above the head of a person. With such a position of camera, it is difficult to identify the person. Therefore, video surveillance is an application in real time. The images of the same individual may vary appreciably based on different camera field of view. Color content in an image remains an important cue to identify a person. Under the assumption that the clothing color remains unchanged over the period of surveillance, a method based on significant colors with its spatial correspondence in image is proposed. The method is applied on standard data sets like GRID, PRID450s and VIPER. The results are plotted as cumulative matching characteristic curve and compared with other methods. The approach is both computationally efficient and delivers better performance.
The Application of Cloud Computing in College English Teaching

Abstract In the era of Internet linking everything, education faces all-round multi-dimensional challenges and opportunities. In order to utilize the advantages of technology, college English educators should extend the traditional classroom aided by big data and Cloud computing in education. Cloud computing is emerging as a powerful computing paradigm with its aim of efficient resource utilization, providing a new solution for online English teaching and learning. With specific teaching targets of college English, the pre-class, in-class and after-class teaching phases aided by Cloud computing are presented, which aims to provide inspiration for educators, administrators and learners.
A Survey of Artificial Intelligence-Based E-Commerce Recommendation System

Recommendation systems have led to the growth of many e-commerce and content providing companies because they perform well in modelling consumer’s habit and providing personalization to users. Recommendation system used in E-commerce has been extensively researched and a number of algorithms/methods have been proposed. These methods can be a linear or a non-linear approach. Neural networks are proven non-linear techniques that outperform the linear techniques and have produced satisfactory results on various available recommendation datasets. Deep learning neural networks have also proven their efficiency in domains such as computer vision and natural language processing and of recent is been applied to recommender systems because of high performance. In this paper, we reviewed deep learning methods that have been applied to recommender systems in E-commerce both in academics and industry.
Quantum Polarization of Qudit Channels.

We provide a generalization of quantum polar codes to quantum channels with qudit-input, achieving the symmetric coherent information of the channel. Our scheme relies on a channel combining and splitting construction, where a two-qudit unitary randomly chosen from a unitary 2-design is used to combine two instances of a qudit-input channel. The inputs to the synthesized bad channels are frozen by sharing EPR pairs between the sender and the receiver, so our scheme is entanglement assisted. Using the fact that the generalized two-qudit Clifford group forms a unitary 2-design, we conclude that the channel combining operation can be chosen from this set. Moreover, we show that polarization also happens for a much smaller subset of two-qudit Cliffords, which is not a unitary 2-design. Finally, we show how to decode the proposed quantum polar codes on Pauli qudit channels.
Loss Landscape Matters: Training Certifiably Robust Models with Favorable Loss Landscape

In this paper, we study the problem of training certifiably robust models. Certifiable training minimizes an upper bound on the worst-case loss over the allowed perturbation, and thus the tightness of the upper bound is an important factor in building certifiably robust models. However, many studies have shown that Interval Bound Propagation (IBP) training uses much looser bounds but outperforms other models that use tighter bounds. We identify another key factor that influences the performance of certifiable training: \textit{smoothness of the loss landscape}. We consider linear relaxation based methods and find significant differences in the loss landscape across these methods. Based on this analysis, we propose a certifiable training method that utilizes a tighter upper bound and has a landscape with favorable properties. The proposed method achieves performance comparable to state-of-the-art methods under a wide range of perturbations.
A Vision-Based Framework for Spotting and Segmentation of Gesture-Based Assamese Characters Written in the Air

The task of automatic gesture spotting and segmentation is challenging for determining the meaningful gesture patterns from continuous gesture-based character sequences. This paper proposes a vision-based automatic method that handles hand gesture spotting and segmentation of gestural characters embedded in a continuous character stream simultaneously, by employing a hybrid geometrical and statistical feature set. This framework shall form an important constituent of gesture-based character recognition (GBCR) systems, which has gained tremendous demand lately as assistive aids for overcoming the restraints faced by people with physical impairments. The performance of the proposed system is validated by taking into account the vowels and numerals of Assamese vocabulary. Another attribute to this proposed system is the implementation of an effective hand segmentation module, which enables it to tackle complex background settings.
STC: Enabling 16K VR streaming on mobile platforms with FoV tracking

16K VR videos are coming to ages. But it could overwhelm mobile hardware for its huge bandwidth consumption and decoding complexity. To enable 16K VR video streaming over mobile platforms, we present a novel ShiftTile-Tracking (STC) streaming system, which crops and transmits video by tracking the Field-of-View (FoV) movement of users. The video chunk is split into ShiftTiles with frame granularity, which always covers FoV areas along the FoV movement trajectory. This transforms a 360-degree VR video into a traditional planar video, which leads to huge bandwidth saving and faster decoding speed. In the system design, we mainly entail two contributions. 1) To accommodate various FoV movement trajectories with a limited number of ShiftTiles, we propose an optimal tiling algorithm by FoV trajectory clustering. 2) To be resilient to the FoV prediction errors, we propose an accuracy-sensitive streaming algorithm, which expands the FoV area if the FoV prediction errors are high. The evaluation shows that under the same real-world 4G network conditions, the proposed STC improves 0. 9dBV-PSNR, reduces 12.4% buffering ratio, and achieves 45% faster decoding speed (64 frames per second) on average compared with the state-of-the-art solutions. This enables 16K VR video streaming on current mobile platforms.
Fusion of IoT, Blockchain and Artificial Intelligence for Developing Smart Cities

Smart cities are aiming to enhance the quality of everyday lives in urban areas by integrating information technology solutions into aspects of routine life. Smart cities promote ecological development by providing smart solutions for e-governance, waste management, water management, traffic management, healthcare services, and more. This involves complex interactions between citizens, government organizations, and essential private sectors. These complicated interactions can be handled by using technologies like artificial intelligence (AI), blockchain (BC) and Internet of Things (IoT). In IoT, devices regularly used by individuals are cars, household items, office tablets, and parking lots by communicating with each other with the help of sensors and microprocessor chips housing within them. The data collected using sensors are analyzed to gain insight on how to manage resources efficiently. The data collected from individuals, devices, and other sources are processed and analyzed. Large amounts of data have to be processed and analyzed properly in a smart city in order to provide a sustainable environment. Since a large volume of data is involved it is necessary to have a technical solution for data processing and analyzing. AI technology can be integrated into a smart city environment to provide exceptional decision-making skills for improving public services. When the smart systems are implemented, certainly security and privacy issues will arise as a major challenge. Hence it is important to have effective countermeasures to handle the hurdles. Blockchain technology ensures data security processed by AI. It also ensures various entities like smart contracts, smart assets, digital identity of the individuals, and others will be securely handled with blockchain technology. All the three technologies can be combined together to provide a fully functional smart city.
Leveraging Machine Learning to Explain the Nature of Written Genres

The analysis of discourse and the study of what characterizes it in terms of communicative objectives is essential to most tasks of Natural Language Processing.Consequently, research on textual genres as expressions of such objectives presents an opportunity to enhance both automatic techniques and resources.To conduct an investigation of this kind, it is necessary to have a good understanding of what defines and distinguishes each textual genre.This research presents a data-driven approach to discover and analyze patterns in several textual genres with the aim of identifying and quantifying the differences between them, considering how language is employed and meaning expressed in each particular case.To identify and analyze patterns within genres, a set of linguistic features is first defined, extracted and computed by using several Natural Language Processing tools.Specifically, the analysis is performed over a corpora of documents-containing news, tales and reviews-gathered from different sources to ensure an heterogeneous representation.Once the feature dataset has been generated, machine learning techniques are used to ascertain how and to what extent each of the features should be present in a document depending on its genre.The results show that the set of features defined is relevant for characterizing the different genres.Furthermore, the findings allow us to perform a qualitative analysis of such features, so that their usefulness and suitability is corroborated.The results of the research can benefit natural language discourse processing tasks, which are useful both for understanding and generating language.
A Novel Algorithm for Training Hidden Markov Models with Positive and Negative Examples

In this paper, we present a novel training method based on Baum-Welch algorithm for hidden Markov models (HMM), named as Comprehensive HMM (CompHMM), which changes the traditional approach of training HMM from positive examples only to be able to utilize both positive and negative examples in training HMMs. By comparison, our method outperformed the standard Baum-Welch method and another HMM discriminative training method significantly through both synthetic and real data in membership prediction task.
Comparison of PSO, FA, and BA for Discrete Optimization Problems

Swarm intelligence (SI) is widely applied for optimizing both continuous and discrete problems. Many papers have investigated them for continuous optimizations since most swarm-based algorithms are designed based on continuous movements, which are simply calculated using vector-based mathematical operations. It is quite easy to select the best SI algorithm for a given continuous problem. However, it is quite hard to pick an optimum SI algorithm for a discrete problem since the individual movement is difficult to develop. Therefore, in this paper, three SI algorithms: particle swarm optimization (PSO), firefly algorithm (FA), and bat algorithm (BA), are compared to solve some cases of traveling salesman problem (TSP). Evaluation on four TSP cases show that FA is the most effective and efficient since it dynamically evolves some individuals' groups and balances the exploitative-explorative movements.
GAN based Single Image Super-Resolution via Spatially Adaptive De-normalization

Despite recent advances in technologies on single image super-resolution using deep neural networks, the key question still remains how to recover finer textures and edges. To solve this super-resolution problem, many recent researches have been using conditional generative adversarial network. However, restoring high resolution images using conditional generative adversarial network is disadvantageous in expressing fine textures and edges because there occurs the loss of spatial and high frequency informations. In this paper, informations on images in different scales are added hierarchically by using a spatially adaptive de-normalization method. This method can restore fine textures and edges of an image by inserting different scale informations for each layers in pyramid structure. In experimental results, the efficiency of the proposed method is proved by showing better performance to restore textures and edges in high quality, comparing with other state-of-the art techniques.
A Scalable Low-power Accelerator for Sparse Recurrent Neural Networks

As the deep learning techniques develop, the Recurrent Neural Networks are widely used, especially in speech recognition and natural language processing applications. However, the data dependency and low data reuse make RNNs hard to process on low power platforms. In this paper, we designed a voltage-scalable Low power Accelerator for SparsE RNN named LASER. Firstly, the sparse-RNN is analyzed and the processing array is designed after the network compression approach. Due to the imbalanced workload of sparse-RNN, we introduced the voltage-scaling method to keep the architecture with low power and high throughput. As the methods applied to EESEN, the system power efficiency can be improved significantly.