Reviewerly/exHarmony huggingface dataset:
Modified Elliptic Curve Cryptography Multi-Signature Scheme to Enhance Security in Cryptocurrency

Internet of Things (IoT) is an emerging technology that moves the world in the direction of smart things.But, IoT security is the complex problem due to its centralized architecture, and limited capacity.So, blockchain technology has great attention due to its features of decentralized architecture, transparency, immutable records and cryptography hash functions when combining with IoT.Cryptography hash algorithms are very important in blockchain technology for secure transmission.It converts the variable size inputs to a fixed size hash output which is unchangeable.Existing cryptography hash algorithms with digital signature have issues of single node accessibility and accessed up to 128 bytes of key size only.As well as, if the attacker tries to hack the key, it cancels the transaction.This paper presents the Modified Elliptic Curve Cryptography Multi Signature Scheme (MECC-MSS) for multiple node accessibility by finding nearest path for secure transaction.In this work, the input key size can be extended up to 512 bytes to enhance the security.The performance of the proposed algorithm is analyzed with other cryptography hash algorithms like Secure Hashing Algorithms (SHAs) such as SHA224, SHA256, SHA384, SHA512, SHA3-224, SHA3-256, SHA3-384, SHA3-512 and Message Digest5 by one-way analysis of variance test in terms of accuracy and time complexity.Results show that the MECC-MSS achieves 90.85% of accuracy and time complexity of 1.4 nano seconds with significance less than 0.05.From the statistical analysis, it is observed that the proposed algorithm is significantly better than other cryptography hash algorithms and also having less time complexity.
Artificial Fish Swarm Optimization with Deep Learning Enabled Opinion Mining Approach

Sentiment analysis or opinion mining (OM) concepts become familiar due to advances in networking technologies and social media.Recently, massive amount of text has been generated over Internet daily which makes the pattern recognition and decision making process difficult.Since OM find useful in business sectors to improve the quality of the product as well as services, machine learning (ML) and deep learning (DL) models can be considered into account.Besides, the hyperparameters involved in the DL models necessitate proper adjustment process to boost the classification process.Therefore, in this paper, a new Artificial Fish Swarm Optimization with Bidirectional Long Short Term Memory (AFSO-BLSTM) model has been developed for OM process.The major intention of the AFSO-BLSTM model is to effectively mine the opinions present in the textual data.In addition, the AFSO-BLSTM model undergoes pre-processing and TF-IFD based feature extraction process.Besides, BLSTM model is employed for the effectual detection and classification of opinions.Finally, the AFSO algorithm is utilized for effective hyperparameter adjustment process of the BLSTM model, shows the novelty of the work.A complete simulation study of the AFSO-BLSTM model is validated using benchmark dataset and the obtained experimental values revealed the high potential of the AFSO-BLSTM model on mining opinions.
Comprehending the use of intelligent techniques to support technical debt management

Technical Debt (TD) refers to the consequences of taking shortcuts when developing software. Technical Debt Management (TDM) becomes complex since it relies on a decision process based on multiple and heterogeneous data, which are not straightforward to be synthesized. In this context, there is a promising opportunity to use Intelligent Techniques to support TDM activities since these techniques explore data for knowledge discovery, reasoning, learning, or supporting decision-making. Although these techniques can be used for improving TDM activities, there is no empirical study exploring this research area. This study aims to identify and analyze solutions based on Intelligent Techniques employed to support TDM activities. A Systematic Mapping Study was performed, covering publications between 2010 and 2020. From 2276 extracted studies, we selected 111 unique studies. We found a positive trend in applying Intelligent Techniques to support TDM activities, being Machine Learning, Reasoning Under Uncertainty, and Natural Language Processing the most recurrent ones. Identification, measurement, and monitoring were the more recurrent TDM activities, whereas Design, Code, and Architectural were the most frequently investigated TD types. Although the research area is up-and-coming, it is still in its infancy, and this study provides a baseline for future research.
From money to culture: The practical indeterminacy of Bitcoin's values and temporalities

Bitcoin, regarded as a decentralized currency of the future as well as a digital gold, faces various challenges, such as scalability, the geographical concentration of mining, its politically informed design and history, its high market volatility, and inequalities in the proportion of accumulation. However, the number of Bitcoin owners has risen exponentially, and relevant socioeconomic and political groups have become increasingly diverse. Consequently, this article argues that what has contributed to the global diffusion of Bitcoin and its embeddedness in different human societies is its practical indeterminacy. Practical indeterminacy characterizes the fundamentally undefinable, indeterminate nature of Bitcoin's value, as it can change its form depending on who it encounters. In terms of temporality, practically indeterminate Bitcoin can urge potential owners and users to compare their pasts and futures, thus driving them to perceive, own, and use Bitcoin for their own purposes. By paying attention to the agency of Bitcoin, practical indeterminacy explains how individuals form their own relations with Bitcoin and how these relations lead to Bitcoin's further sociocultural embeddedness. The proliferation of such a wide range of human–Bitcoin relations shows that Bitcoin is not only monetary but also cultural, as it offers different meanings to users and owners.
Quantum simulation and quantum computation of noisy-intermediate scale

In the past years, great progresses have been made on quantum computation and quantum simulation. Increasing the number of qubits in the quantum processors is expected to be one of the main motivations in the next years, while noises in manipulation of quantum states may still be inevitable even the precision will improve. For research in this direction, it is necessary to review the available results about noisy multiqubit quantum computation and quantum simulation. The review focuses on multiqubit state generations, quantum computational advantage, and simulating physics of quantum many-body systems. Perspectives of near term noisy intermediate-quantum processors will be discussed.
Improved Siamese classification and regression adaptive network for visual tracking

To solve the problems of the complex multi-scale searching and the low correlation between classification confidence and location accuracy in the visual tracking process, we propose a novel visual tracking framework named ISiamCRAN. ISiamCRAN uses modified ResNet-50 as the backbone network to extract depth features. Then, these extracted features are fed into an improved classification-regression adaptive head for depth feature cross-correlation operation. Different from existing trackers, in order to remove low-quality prediction bounding boxes, we integrate a quality assessment branch to the classification-regression adaptive head. Moreover, we use an elliptical sample label assignment strategy to replace traditional strategy, by this way our tracker can more accurately distinguish the foreground and background. Finally, for these feature response maps obtained by depth feature cross-correlation operation, the position and scale of the target are predicted directly in a unified fully convolutional network by an anchor-free manner. Extensive experiments on OTB100 and VOT2018 benchmarks indicate that our ISiamCRAN outperforms other traditional trackers, and is robust to motion blur and scale variation. Our tracker runs at approximatively 35 fps on GPU.
Wireless Play Music Beep Sensor to Assist in Music Fountain Control

Musical water landscape combines landscape design with audiovisual art and is loved by the general public. However, the performance control form of musical water landscape generally adopts the manual offline preprogramming method, which needs to use the human subject’s consciousness to perceive the style, emotion, and other advanced features of the music first and then choreographs the corresponding landscape performance, which is a high-cost design form and low usage rate of the landscape and is not conducive to the expansion of the music library. In this paper, we design and implement a musical water landscape simulation system using OpenGL and 3Dmax technology in combination with wireless music buzzer sensors, extract and analyze the music features based on this system, and study the system control of musical water landscape. Open GL particle system technology is used to realize the dynamic simulation of weather and fountain inside the landscape and improve the drawing scheme from both modeling and rendering aspects. For the fountain, the physical model and the wireless music beep sensor are combined to design a variety of water type actions for a single spout, and the parameter interface to control the water type changes is reserved. We propose a style recognition method based on CRNN and residual network, which has a higher accuracy rate compared with the existing methods; we use the DP algorithm to segment the pitch sequence of the music to reduce the redundancy of information and improve the calculation speed of the system.
A Survey - Insights of ML and DL in Health Domain

For the past few years, concepts like Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL) have been slowly infiltrating the health industry, bringing with them technology and solutions that are transforming the face of healthcare. Each of these technologies is interconnected, offering something unique to the business and altering how medical professionals manage their duties and provide patient care. While AI is the most well-known of the technical terminology, DL in healthcare is a subset of AI that has disruptive potential and adds a new layer to medical technology solutions. DL is gradually becoming the leader in the field of ML, which is entering its golden age. To develop computer models, DL employs numerous layers to represent data abstractions.
Generalized Filtering with Transport Planning for Joint Modulation Conversion and Classification in AI-enabled Radios

AI-empowered Cognitive Radio (i.e., AI-enabled radios) is a paradigm shift to achieve the highest level of Self-Awareness in future wireless communications. This work proposes a joint automatic modulation conversion and classification (AMCC) framework, which allows an AI-enabled wireless node to predict signals' dynamics of different modulation schemes and explain how it can be transported (converted) with minimal effort and forwarded with higher spectral efficiency. To achieve this goal, we propose a Generalized Filtering framework integrated by Transport Planning to learn the way of converting low-order modulations to high-order modulations, which has also been validated by performing the automatic modulation classification. Simulation results demonstrate the effective performance of our novel framework on converting and classifying multiple modulation formats.
AI Text Generators and Text Producers

AI-generated text production is becoming increasingly important in many industries, and it has already brought about dramatic changes in the ways we write texts and generate content. The article draws on empirical data from a descriptive-analytical study involving 70 test subjects. The population comprised 115 test persons, who received an e-mail with instructions. A sample of 70 test subjects participated in the study. First, the test subjects were asked to test a specific AI text generator (ATG) and conduct three prompting operations with the same linguistic content. Second, having tested the ATG, the test subjects were asked to participate in a questionnaire with ten questions focusing on how they experienced the performance of the ATG and how they worked with the ATG. The majority of the test subjects found that the tested ATG was easy to use when producing texts. When asked about the perceived quality of the AI-generated content, the respondents were not impressed with the quality and indicated that they needed to perform several editing operations. The data also indicate that ATGs need help before, during and after. This paper presents a three-phase editing framework, which can be used when using and teaching ATGs.
A Dynamic Load Balancing Scheme Based on Network Sharding in Private Ethereum Blockchain

Applications in the private Ethereum blockchain environment are required by many enterprises. Although, un-like public Ethereum platforms, private Ethereum platforms allow some specific parameters to be configured at setup time to increase throughput, its throughput performance is still not sufficient for enterprise-level applications due to the low scalability of the Ethereum blockchain. This paper analyzes and tests the throughput of the private Ethereum blockchain at the hardware level, and according to the analysis results, proposes a parallelized dynamic load balancing scheme based on network sharding. On this basis, various charts are created to visually show the impact of changing hardware parameters on performance, and experiments are conducted to verify that the model can further improve the performance of private Ethereum blockchains through effective load balancing.
An Encoder-Decoder Network for Automatic Clinical Target Volume Target Segmentation of Cervical Cancer in CT Images

Cervical cancer is a common gynecological cancer, and its common treatment method radiotherapy depends on target area delineation. The manual delineation work takes a long time and has low accuracy, so automating such delineation is important. At present, some traditional image segmentation algorithms for target area delineation have low accuracy rates. Deep learning algorithms also face some difficulties, such as insufficient data and long training time. As the popular network used in medical image segmentation, U-net still has several disadvantages when handling small targets with unclear boundaries. According to the characteristics of the clinical target volume target segmentation task of cervical cancer, this study modified the U-net structure and optimized the training loss to improve the accuracy of small target detection. The modified structure could handle target boundaries well with operations such as bilinear upsampling. Finally, the proposed algorithm was evaluated on the dataset and compared with several deep learning-based algorithms. Results indicate that the proposed approach has certain superiority.
A Systematic Approach to the Methodology of Typhlopedagogy on the Example of Mathematical Analysis Problems

The article discusses some of the problems that arise when working with students with visual defects. In particular, the authors discuss ways to improve the culture of mathematical thinking of students with visual defects. The authors rely on the experience of working at the Faculty of Information Technologies of Moscow State Moscow State University of Psychology &amp; Education.
The readability of abstracts in library and information science journals

Purpose The purpose of the research was to find out if there are any differences in the readability score between abstracts published in scientific journals from library and information science with and without an impact factor. Therefore, the author made a comparison between the readability of abstracts from one journal with (Journal of Documentation) and one journal without (Knjižnica or Library) an impact factor. Design/methodology/approach As a measure of readability, the Flesch Reading Ease Readability Formula was used. Then, with the help of statistical experts, a comparison of the readability scores between the abstracts of two selected journals was performed. Findings The results showed that some statistically important differences exist between the abstracts published in the Journal of Documentation and Knjižnica. The statistically important differences were found in the number of words and sentences in abstracts and in the readability of abstracts included in the research. Therefore, it can be said that there exists a statistically important difference between abstracts with and without an impact factor. Originality/value The primary purpose was to find out whether there is a statistically important difference in the readability score of abstracts with and without an impact factor in the field of library and information science. Some similar research studies have been conducted in other scientific fields.
Attention Mechanism Based Mixture of Gaussian Processes

• We connect the attention mechanism and mixture of Gaussian processes • We design two novel mixture of Gaussian processes models based on the attention mechanism. • The proposed models are applicable to the case that the input variable lies on a manifold or a graph. The mixture of Gaussian processes (MGP) is a powerful model, which is able to characterize data generated by a general stochastic process. However, conventional MGPs assume the input variable obeys certain probabilistic distribution, thus cannot effectively handle the case where the input variable lies on a general manifold or a graph. In this paper, we first clarify the relationship between the MGP prediction strategy and the attention mechanism. Based on the attention mechanism, we further design two novel mixture models of Gaussian processes, which do not rely on probabilistic assumptions on the input domain, thus overcoming the difficulty of extending MGP models to manifold or graph. Experimental results on real-world datasets demonstrate the effectiveness of the proposed methods.
Object-Occluded Human Shape and Pose Estimation With Probabilistic Latent Consistency

Occlusions between human and objects, especially for the activities of human-object interactions, are very common in practical applications. However, most of the existing approaches for 3D human shape and pose estimation require that human bodies are well captured without occlusions or with minor self-occlusions. In this paper, we focus on the problem of directly estimating the object-occluded human shape and pose from single color images. Our key idea is to utilize a partial UV map to represent an object-occluded human body, and the full 3D human shape estimation is ultimately converted as an image inpainting problem. We propose a novel two-branch network architecture to train an end-to-end regressor via a latent distribution consistency, which also includes a novel visible feature sub-net to extract the human information from object-occluded color images. To supervise the network training, we further build a novel dataset named as 3DOH50K. Several experiments are conducted to reveal the effectiveness of the proposed method. Experimental results demonstrate that the proposed method achieves state-of-the-art compared with previous methods. The dataset and codes are publicly available at https://www.yangangwang.com/papers/ZHANG-OOH-2020-03.html.
PresSafe: Barometer-Based On-Screen Pressure-Assisted Implicit Authentication for Smartphones

Graphic-pattern-based implicit authentication has been successfully exploited to elevate the security of smartphones. On-screen pressure is one of the key features in such an approach since it can reveal users’ touch pattern. However, state-of-the-art approaches rely on a system API to obtain on-screen pressure, which is not adequately accurate and cannot meet the demands of robust implicit authentication. To bridge this gap, we propose PresSafe, a novel implicit authentication system that utilizes the smartphone’s built-in barometer sensor to measure pressure during the unlocking process, and to utilize the pressure data in authentication. A key technical challenge in utilizing barometer sensing, however, is to understand the user activity through measured pressure. To overcome this challenge, PresSafe leverages barometer data along with data from other conventional but heterogeneous ambient sensors to produce accurate and robust user activity descriptions. PresSafe utilizes a transfer-learning-based hybrid workflow to integrate user activity representation learning with a lightweight classical authentication algorithm to obtain a unified model. This approach offloads the computational cost from the terminal and addresses privacy concerns. To ensure applicability of our approach despite data heterogeneity and insufficient training data, we utilize a channel-adaptive data processing mechanism. Extensive experiments utilizing more than 70000 records from 23 volunteers in six different locations show that PresSafe achieves an FAR of 0.45%, an FRR of 0.49%, and an EER of 0.47%, which clearly demonstrate its superiority over several existing solutions.
KDCRec: Knowledge Distillation for Counterfactual Recommendation Via Uniform Data

The bias problems in recommender systems are an important challenge. In this paper, we focus on solving the bias problems via uniform data. Previous works have shown that simple modeling with a uniform data can alleviate the bias problems and improve the performance. However, the uniform data is usually few and expensive to collect in a real product. In order to use the valuable uniform data more effectively, we propose a novel and general knowledge distillation framework for counterfactual recommendation with four specific methods, including label-based distillation, feature-based distillation, sample-based distillation and model structure-based distillation. Moreover, we discuss the relation between the proposed framework and the previous works. We then conduct extensive experiments on both public and product datasets to verify the effectiveness of the proposed four methods. In addition, we explore and analyze the performance trends of the proposed methods on some key factors, and the changes in the distribution of the recommendation lists. Finally, we emphasize that counterfactual modeling with uniform data is a rich research area, and list some interesting and promising research topics worthy of further exploration. Note that the source codes are available at <uri>https://github.com/dgliu/TKDE_KDCRec</uri>.