Reviewerly/exHarmony huggingface dataset:
A Multispectral Image Enhancement Algorithm Based on Frame Accumulation and LOG Detection Operator

In multispectral transmission image, due to the strong scattering and strong absorption characteristics of biological tissue, the weak image signal, low signal-to-noise ratio, and blurred edges of the transmission image greatly hinder the detection of heterogeneity of transmission tissue. And image preprocessing is crucial to the complex image algorithm in the later experiments. In order to improve the quality of low-resolution multispectral transmission images, this paper proposes an enhancement algorithm for transmission tissue images through the simulation experiment of multispectral transmission images: Single-channel frame algorithm that combines with LOG detection operators. The experimental results show that the peak signal-to-noise ratio (PSNR) of the image processed by the enhanced algorithm in this paper is increased to 52.289 dB, which is 1.515 dB higher than the original image and is 1.386 dB higher than the only filtered image. The multispectral image processed by this algorithm shows that the contrast ratio at the edge of the image has been improved, and the image quality has been improved to a certain extent. Finally, through a comparative experiment, the effectiveness and practicability of the enhancement algorithm for improving the quality of multispectral transmission images are verified.
A Novel Approach Based on Blockchain to Enhance Security with Dynamic Policy Updating

The cipher-text policy attribute-based encryption is a promising technique to ensure the security in the third trust parties environment and offers opportunities to their users. However, the policy updating becomes a challenging issue when we use CP-ABE to construct access control schemes. The traditional method consists of presenting a huge work to the data owners, data retrieving, its re-encryption under the new access policy, and the re-sending back to the cloud. These interactions incur a heavy computation burden and a high communication on the data owner. In this paper, we propose a novel approach, in one hand, to enhance the security by using Blockchain technology, and in the other hand to update the access policy dynamically. We use Blockchain to deploy a policy in a manner that preserves security. We use also the cloud to store the data with CP-ABE, and especially, we focus on the delegation of the policy updating method to the cloud. This method can minimize the computation work and avoid the transmission of encrypted data by combining the ciphertext and previous access strategy. Moreover, we also design a policy updating algorithm. In our scheme, the security is occurred by two factors, the first one must satisfy the policy in the CP-ABE. The second one also must satisfy the policy deployed in the Blockchain to have the authorization token generated to access the desired resources.
Method for Detection of the Vessel Trace Anomalies in the Sea Surface Images Based on Analysis of Color Gradient Correlations

The paper presents the statistical method for detecting the ship trace manifestations in the sea surface images. The method is based on comparison of correlation parameters of image color gradients in different directions. The set of correlations for different parts of image is clustered, followed by checking the clusters obtained of the vessel trace lateral components for the compliance. The method efficiency is illustrated by example.
Feature Extraction and Classification of Unknown Types of Communication Emitter

In the field of cognitive radio, communication emitter recognition plays an important role. The traditional methods need to determine expert feature in advance, which leads to inefficiency in some case. One is that the emitters may not be observed in the training and we need to classify much larger new classes examples without knowledge of the label. Therefore, this paper proposes a semi-supervised method for extracting RF fingerprint based on supervised Convolutional Neural Networks and unsupervised clustering. Experimental results demonstrate that the features extracted from CNN can distinguish emitters and the proposed method has a great performance in classifying unknown types of emitters.
Method for checking key parameters of basic data of automation equipments in intelligent substation based on multi-source data fusion

The basic data quality of automation equipments in substation directly affects the accuracy of data analysis and rationality of decision-making. This paper proposes a method for checking key parameters of automation equipments basic data of intelligent substation based on multi-source data fusion. This method in this paper fuses multi-source data such as standard point tables of EMS, remote configuration description (RCD) files, and substation system configuration description (SCD) files, and can accurately point out the missing and wrong filling of automation equipment basic data of the automation basic data system, and realize the verification of multiple functions such as uniqueness, completeness and accuracy of key parameters of the system equipment. Compared with the manual verification, the examples show that this proposed method can save a lot of time and cost, and also provide support for promoting the improvement of the lean production management level.
Conflict Detection of Device Linkage Rules in Smart Home Systems

With the increasing types and functions of smart home devices, due to the lack of experience of users, they may cause unexpected errors and risks when configuring a large number of device linkage rules.In response to the above problems, in this paper, we propose an efficient conflict detection method to avoid unexpected situations.First, to reduce the complexity of subsequent rule conflict detection, we divide each rule into five categories according to its functions and characteristics. At the same time, we configure the priority, user rights, and execution area for each rule. Second, we filter out the rules of the same priority, user rights, and execution area. Third, we define five types of rule conflicts. Finally, the conflict detection algorithm is used to determine whether the filtered rules conflict with the rules in the corresponding database. We have created 379 non-conflicting rules in each type of rule storage database to detect whether the newly created rules conflict with them. Through experimental comparison, we find that our approach has more advantages than previous work in terms of performance and conflict detection types.
Automation in the Overlaying of Journal Maps

Ismael Rafols (personal communication) suggested to me in 2008 to use journal maps as a background for portfolio analysis and visualization. Such a tool could be used for informing science & technology policy debates. I had developed journal maps since the mid-1990s on the basis of aggregated journal-journal citations as these are yearly collected and published in the Journal Citation Reports (JCR) of the Science Citation Index and the Social Sciences Citation Index by ISI/Clarivateâ„¢. However, my focus had been on developing structural and dynamic analysis of the sciences. Portfolio analysis provides another option: the journal structure is then assumed as a baseline. In this report, I propose a tool that automates the visualization of portfolios.
A Project-level Investigation of Software Commit Comments and Code Quality

Useful and meaningful commit comments are assumed to contribute to an effective software development process. In fact, some investigations of software quality factors have assumed the existence of this relationship. However, to the best of our knowledge, no research has demonstrated an empirical link between commit comment quality and code quality. To fill this gap, in this work we acquired 120 open source projects from GitHub, examining the relationships between the commit comment quality and code quality over a 6-month period. We first calculated a set of comment quality metrics and a set of code quality metrics. Two of these metrics, comment expressiveness and code thrashing frequency, were newly developed as part of this research. Then we evaluated the relationship between these two sets of metrics. We found strong evidence that the new thrashing frequency metric is a valid measure of code quality. Considering our main hypothesis, we found comment uniqueness and percentage of comments that have bodies, both measures of comment quality, had significant relationships with thrashing frequency in the expected direction, namely that better comments were associated with lower thrashing frequency. Measures related to comment length and comment word frequency, however, produced some significant relationships in the opposite direction, suggesting that developers may write more elaborate comments when they are concerned about introducing defects. Overall, our results indicate that the relationships between comments and code are more complex than suggested by our original hypothesis.
Collaborative Adversarial Networks for Joint Synthesis and Segmentation of X-ray Breast Mass Images

In this paper, we propose Collaborative Adversarial Networks (CAN) to enable simultaneous forward synthesis and backward segmentation of X-ray breast mass image. The proposed CAN consists of a generator (G), an inverter (I) and a discriminator (D). G aims to reconstruct mass images from corresponding annotated masks, while I is trained for mapping images back to accurate segmentation masks. All the obtained mask-image pairs are fed to D trained in an adversarial learning scheme. Through the collaborative adversarial training using a joint loss function, G synthesizes realistic mass images consistent with provided masks and I effectively segments the tumor regions from the images. Qualitative and quantitative evaluations on publicly available INbreast database demonstrate the effectiveness of our model. Furthermore, different from conventional GANs-based methods that can only perform either image synthesis or segmentation, the proposed model can be generalized to other bidirectional image-to-image translation of multimodal medical data.
Rainbow Tables for Cryptanalysis of A5/1 Stream Cipher

Global System for Mobile communication (GSM) phones use A5/1 stream cipher for secure communication. It encrypts the information transmitted from a mobile user. Initially, we have studied all existing attacks on A5/1 cipher and performed a comparative analysis. In this paper, we propose an alternative approach for constructing rainbow tables for cryptanalysis of A5/1 algorithm. These pre-computed tables are used to construct the initial state of A5/1 cipher which can be used to recover the session key. These two methods are parallelized and implemented on 5 TeraFlop High Performance Computing (HPC) facility.
Design of High Performance RNN Accelerator Based on Network Compression

As the size of the neural networks expands, the computation and storage consumption of recurrent neural networks is increasing. To solve this problem, this paper proposes a recurrent neural network accelerator which can reduce computation redundancy, memory overhead and energy consumption. A novel network compression method based on pruning and hybgrid quantization is also proposed to reduce computation and memory overhead. Based on the designs above, a precision adaptive approximate calculation based accelerator is designed to achieve high energy efficiency. The experimental results show that under the TSMC 28nm process, when the data bit width is 4bit and the working voltage is 0.8V, the peak performance of proposed accelerator is not reduced, the power consumption is 38.4mW, and the energy efficiency is 2.7TOPs/W. The energy efficiency of proposed accelerator is 2.5 times more than that of the state-of-the-art recurrent neural network accelerators.
A Linear Approach to Absolute Pose Estimation for Light Fields

This paper presents the first absolute pose estimation approach tailored to Light Field cameras. It builds on the observation that the ratio between the disparity arising in different sub-aperture images and their corresponding baseline is constant. Hence, we augment the 2D pixel coordinates with the corresponding normalised disparity to obtain the Light Field feature. This new representation reduces the effect of noise by aggregating multiple projections and allows for linear estimation of the absolute pose of a Light Field camera using the well-known Direct Linear Transformation algorithm. We evaluate the resulting absolute pose estimates with extensive simulations and experiments involving real Light Field datasets, demonstrating the competitive performance of our linear approach. Furthermore, we integrate our approach in a state-of-the-art Light Field Structure from Motion pipeline and demonstrate accurate multi-view 3D reconstruction.
CompAcc: Efficient Hardware Realization for Processing Compressed Neural Networks Using Accumulator Arrays

Compression of neural networks is an effective way to satisfy the requirement of memory-constrained edge devices. We propose a novel array microarchitecture that exploits compressed neural networks with nonlinearly quantized weights and supports variable activation and compressed weight bit widths. Computation is made more efficient by accumulating all the activations multiplied by the same weight prior to multiplication. This design has been fabricated in TSMC 28nm technology. It achieves 3.4 TOPS/W with 16b activations and 16b weights (4b compressed) and 3.7 TOPS/W on the convolutional layers of AlexNet (8b activations, 4b compressed weights) with the ImageNet dataset, consuming 15.6mW at 44fps. This is comparable to state-of-the-art chip implementations, while introducing increased flexibility with a simple array structure.
Natural Language Model for Image Caption

Image caption is a technology that combines the knowledge of computer vision with natural language processing. In this paper, we offer some natural language models by human beings in picture description in hope that they might shed light on relevant research on image caption. The paper concludes that picture descriptions by human beings are rules governed as well as experience oriented. Picture descriptions by human beings are characterized with background knowledge about the world which is lacking in present-day image caption systems.
Visual salience accelerates lexical processing and subsequent integration: an eye-movement study

This study examined how visual salience affects the processing of salient information it highlights (here after called visually salient information), as well as its connection with associated content during online reading. Participants were asked to read descriptive concepts that contained a two-character key concept term with a short definition, and subsequently complete a memory test. The visual salience of the key concept terms was manipulated. The results show that visual salience shortened the reading times of key concept terms, as well as the go-past times of concept definition. In addition, improving the visual salience of the key concept terms helped subjects in the subsequent memory test to make quicker and more accurate judgments regarding incorrect concepts. These results indicate that visual salience accelerates the lexical processing of visually salient information and helps readers build faster and more elaborate connections between visually salient information and associated content in the subsequent integration.
Varying Definitions of LMS to Promote and Enhance Transactional Commitments on Account of Widespread Pandemic

This paper is going to focus on concepts of space-based architecture (Blackboard system) to evaluate how well the components of the system function using the space paradigm and how it could be further implemented to the benefit of the students. The purpose of it is to increase space and performance in the data grid, which is composed of units that are independent of each other to attain high-performance application since application workloads are amplifying regularly. Especially in the current scenario of the current pandemic, every educational institution is required to continue their classes in an online delivery mode. The blackboard system is assimilated from a set of processing units. Blackboard has gained its popularity since the early stages, it is used by many educational managements including some of the health care services to teach, provide, and associate with one another.
A Hybrid Approach for Neural Collaborative Filtering

Recommender systems enable better personalization for e-commerce and online entertainment services and thus gain significant attention from researchers. Alongside the traditional matrix factorization approach, neural networks have been recently a promising trend for collaborative filtering-based systems thanks to considerable improvements in the quality of the recommendations. This paper introduces a hybrid collaborative filtering framework that applies both approaches in a parallel manner to learn knowledge from implicit feedback data. Embedding vectors representing the information of users and items are first mapped from data. Matrix factorization is generalized by the element-wise product of these embeddings, while the neural network takes as input a 2-D interaction map formed from the stacking of two vectors. The framework fuses the element outputs by concatenation to produce an accurate estimation of the correlation between users and items. The proposed method outperformed several baselines in the experiments on standard datasets, including MovieLens, Yelp, and Pinterest. This advantage suggests more considerations on the integration of deep learning to collaborative filtering for effective recommender systems.
Research Hotspots and Development Strategies of Smart Classroom in China

Based on 178 journal papers about smart classroom published in the Chinese Core Collection and Chinese Social Sciences Citation Index from 2008 to 2019, this paper has explored and quantified research updates and hotspots of smart classroom by means of co-word analysis and SPSS software. A summary of characteristics and tendencies is given to provide reference for deeper study of smart classroom.
Separable Convolution Neural Network for Abnormal Activity Detection in Surveillance Videos

Anju, S. S. Kavitha, K. V.Nowadays, the process of abnormal activity detection has gained a wide variety of applications in security and surveillance. Most of the public spaces are now installed with CCTV Cameras and the videos are continuously recorded. At present, the major part of surveillance cameras are tend to merely record the video for post-incident manual analysis. In this scenario, dependability on human monitors increase with a decrease in the reliability of the surveillance system. The security is at risk if the attention levels of the human monitors degenerate to unacceptable levels. The main aim is to enable surveillance cameras to understand activities in their environment instantly and report to the concerned users in case of events that need attention and to maximize the reliability of the surveillance system so that it could ensure maximum human benefit and security. The activities being recorded in a surveillance camera can be classified as normal and abnormal activities like accidents, theft, fire, crimes etc. This study proposes a separable convolution neural network which is trained and evaluated for finding better performance in the classification of activities based on surveillance videos. In particular, it examines the use of different optimizers namely Adam, RMSProp, and SGD in the proposed separable convolution neural network. The evaluation metrics used here are confusion matrix, accuracy, classification report with sensitivity, specificity, precision and f1-score. During evaluation the results showed that the proposed separable convolution neural network model trained with SGD optimizer has gained 94% accuracy with the test data.
Long-Term Secure Deniable Group Key Establishment

In this paper we present a long-term secure deniable group key establishment protocol. Long-term security provides resistance again-st an adversary, even if some underlying hardness assumptions become invalid later, after completion of the protocol. Deniability feature of a group key establishment protocol allows each participant to join a protocol session and authenticate a message for the other group members, but the receiver(s) cannot convince a third party that such involvement of the group members in the process ever took place. The protocol presented here is a long-term secure deniable group key establishment protocol in the random oracle model which remains secure if either a Computational Bilinear Diffie Hellman problem is hard or a server, who shares a symmetric key with each user, is uncorrupted. The technical tools used for the protocol are ring signature, multiparty key encapsulation, and message authentication code.
An Incentive-Compatible Smart Contract for Decentralized Commerce

We propose a smart contract that allows two mutually distrusting parties to transact any non-digital good or service by deploying a smart contract on a blockchain to act as escrow. The contract settles disputes by letting parties wager that they can convince an arbiter that they were the honest party. We analyse the contract as an extensive-form game and prove that the honest strategy is secure in a strong game-theoretic sense if and only if the arbiter is biased in favor of honest parties. By relaxing the security notion, we can replace the arbiter by a random coin toss. Finally, we show how to generalize the contract to multiparty transactions in a way that amortizes the transaction fees.
Speech Feature Extraction Based on Linear Prediction Residual

Linear prediction coding (LPC) is the core technology in speech processing, which has been successfully applied in speech recognition, synthesis and coding. LPC coefficient can well represent the speaker's vocal tract information, and is widely used in the field of speaker recognition. However, the epiphytic LPC residual is often ignored. This paper shows that the LPC residual contains information, which can reflect the characteristics of the speaker himself. We extracted new feature parameters (Second moment, third moment) from the LPC residual, combined with LPC coefficients, and input them into a speaker recognition system based on the GRU network. Cross entropy loss is used as a loss function to train the classifier. The experimental results show that the combined parameters effectively improve the system recognition rate by 5% relative to the LPC coefficients.
Design and Analysis of Mobile-Based Tourism Security Application: Concepts, Artifacts and Challenges

Tourism security has become a matter of great concern over the years for tourists around the world, and this attribute plays an important role in the selection of a destination. The application of technology can greatly enhance the safety and security environment of a tourist in a particular destination. This work proposes the analysis and design of a mobile-based application targeted to provide security and safety to a tourist. The design is based on requirements gathered from the tourist and tourism business personals through online and offline interviews which are grouped and coded into user requirements. Unified Modeling Language (UML) basic notation and tools are used to analyze and design the system in a comprehensive manner. Pokhara city of Nepal is chosen as the destination to deploy destination city to deploy and test the system. Further, the challenges and limitations of the work are discussed in light of privacy and system implementations. This work has a great significance in the application of technology for personal security especially in the tourism area. It is one of the first kind of work done in the tourism sector of Nepal which adds a great value to the tourism industry. The work also contributes as a knowledge domain for the digital and mobile technology implementations in tourism destinations.