Reviewerly/exHarmony huggingface dataset:
An Encryption System for Color Image Based on Compressive Sensing and Pixel Adaptive Diffusion Strategy

An efficient color image encryption scheme based on compressive sensing (CS) and pixel adaptive diffusion strategy is proposed in this paper. A zigzag confusion is utilized to scramble the coefficient matrices, and a four-dimensional (4D) hyperchaotic system is introduced to measure the coefficient matrices to obtain measurement value matrices. Subsequently, the chaotic sequence produced by two-dimensional (2D) Logistic ICMIC cascade map (2D-LICM) is employed for pixel adaptive diffusion. Finally, the elements of measurement value matrices are modified by diffusion to obtain the final cipher image. In this scheme, the initial values and parameters of the chaotic system are calculated by the SHA 512 hash value of color plain image and external keys. Therefore, the proposed encryption algorithm is highly sensitive to the plain image and can resist known-plaintext and chosen-plaintext attacks. The simulation results demonstrate the security and efficacy of the proposed algorithm.
A target anti‐occlusion method based on image processing and trajectory prediction in electro‐optical tracking system

In the electro-optical tracking system, due to the full occlusion of clouds or buildings, the traditional tracking algorithm fails to work effectively. In the authors’ study, a dual-channel anti-occlusion scheme based on image processing and trajectory prediction is proposed. On the one hand, the image-processing unit plays a significant role in evaluating the occlusion state of the target. An improved image occlusion judgement method based on Bhattacharyya coefficient occlusion judgement is proposed, and the ability to resist background interference and rapidity of the algorithm is enhanced. On the other hand, according to the different occlusion states, the dual operation modes of tracking and prediction are usually set. When the target is completely occluded, the prediction mode based on a robust predictive filtering method is investigated to predict the real-time trajectory of the target more accurately. Image occlusion judgement and trajectory prediction can solve the occlusion problems in tracking control. Otherwise, the feasibility and effectiveness of the proposed method are verified by experiments. The cover image is based on the Research Article A target anti-occlusion method based on image processing and trajectory prediction in electro-optical tracking system by Yao Mao et al., https://doi.org/10.1049/rsn2.12235.
Generation of Infographics Considering Information Recipients

Infographics convey information in attractive and effective way by combining various types of data, such as texts and pictures. Although variety of infographics designing tools have been proposed, most of them generally require proficient skill and deep understanding of the software upon operation. In this paper, we propose a user-friendly infographic generator by using natural language as its input. In addition, considering that the same information can bring different feelings to people by changing a writing style of texts and numerical expressions, our system provides a function to change the base color of infographic so that user can control the impression of infographic as “positive” or “negative”. For evaluation of quality of infographics, we conducted a questionnaire survey comparing infographics of different colors that assumed to affect an impression of presented information. As the results of analysis, it was found that the color-controlled infographics are more efficient to enhance the impression in the intended direction than comparative images without color control.
The Research on Artificial Intelligence in Computer Network Technology

With the great development of modern science and technology, the national economy is also further improving. At the same time, China also makes new requirements for the development of various high and new technologies. Artificial intelligence is a new industry derived from the continuous progress of scientific level, and its high scientific and technological value. The development of artificial intelligence has greatly promoted the optimization and upgrading of China’s industrial structure, and also has a great impact on computer network technology, providing a new breakthrough direction for the innovation and development of computer network technology. This paper mainly targets the concept of AI as the entry point to further study the application and development of AI in computer network technology.
From VIS To OVIS: A Technical Report To Promote The Development Of The Field

Occluded Video instance segmentation(OVIS) is a new vision task that has emerged in this years and is processed by video deep learning algorithms. It uses continuous video frames as input, generally ranging from a few frames to hundreds of frames. Before OVIS, there has a task called VIS. To tackle the task of OVIS and VIS, we design a new alghorithm called SimVTR, which based on DETR and VisTR. During the experiment, although we acquire the 27.66 mAP on OVIS test, 25.18m AP on OVIS val, and 31.9 mAP on VIS test, we have found a surprising phenomena that the evaluation mechanism is not sensitive to our mothod SimVTR. When we only use one frame to inference, the model can acquire the similar mAP as dozens frames. SimpleVTR trade off and optimizes the computing resources and effects of end-to-end video instance segmentation algorithm. We used one RTX1080Ti (11G) to experiment, and the batch size can change from 1 to 16 frames. We were surprised to find that only one frame can also get a very high score in inference. The VIS and OVIS cocoapi have some unreasonable place in ytvoseval.py. In this technical report, we prudently point out the phenomena that the evaluation mechanism could have some bug. If this is true, we need check our model to promote the process of the video instance segmentation.
An Experimental Evaluation of Load Balancing Policies Using Cloud Analyst

With the rapid growth of cloud computing, the number of customers accessing the cloud systems and the workload of the servers has been increased tremendously. Resource management of infrastructure as a service (IaaS) is an important aspect in cloud computing that involves scheduling the resources among customers, but improper scheduling of cloud resources degrades the performance of the overall cloud systems. Load balancing is a technique in cloud computing that provides a solution to this problem through efficient resource management which improves the resource utilization, scalability, and overall performance of the cloud systems. In this paper, we made a comparison on various static and dynamic load balancing methods and implement them using a Cloud Analyst simulator to study and assess its performance, and the performance analysis is also presented, and the primary metrics used to measure these algorithms are response time, data center processing time, and cost.
SpongeTraining: Achieving High Efficiency and Accuracy for Wireless Edge-Assisted Online Distributed Learning

Edge-assisted Distributed Learning (EDL) is a popular machine learning paradigm that uses a set of distributed edge nodes to collaboratively train a machine learning model using training data. Most of existing works implicitly assume that the fixed amount of training data is pre-collected and dispatched from user devices to edge nodes. In real world, however, training data in edge nodes are collected from user devices through wireless networks, and the volume and distribution of training data in edge nodes could exhibit temporal and spatial fluctuations due to varying wireless situations (e.g., network congestion, link capacity variation). In this way, existing solutions suffer from slow convergence and low accuracy. In this paper, we propose SpongeTraining to achieve high efficiency and accuracy for online EDL. To accommodate to fluctuations in training data, SpongeTraining uses a buffer at each worker to store received training data and adaptively adjusts training batch size and learning rate of each worker based on training data extracted from the buffer. Experiment results based on real-world datasets show that SpongeTraining outperforms existing solutions by accelerating the training process up to 50% for reaching the same training accuracy.
Fast Algorithm for Dyslexia Detection

This article describes a method for detection of cognitive defects based on eye tracking during reading. The aim of this research is to pursue and extend the experiments conducted in Sweden by introducing Conventional signal theory. The dataset used for experiment was acquired by the authors of the research article Screening for Dyslexia Using Eye Tracking during Reading. The provided data consist of 185 subjects divided into two groups. The first group comprises of 88 low risk (LR) subjects and the second group comprises of 97 high risk (HR) subjects. Our measurements achieved a classification accuracy score 86.164% by classifying the subjects into the correct groups.
Action Recognition Based on Cross Spatital Temporal Graph Convolution

Graph convolutional neural network has been widely used in the field of motion recognition based on bone sequence. In order to make the model have better robustness, it is an effective idea to design the model based on space-time relationship. However, most of the present methods only focus on spatial feature extraction or temporal feature extraction without considering the cross-temporal feature extraction. In this paper, a cross-temporal graph convolution method is adopted to extract the feature information of multiple frames simultaneously through sliding time window and carry out feature communication to achieve cross-temporal feature extraction. In order to further improve the reliability of the model, an adaptive dual-flow fusion network is used to weight the spatital temporal graph convolution network and the cross spatital temporal graph convolution network. The algorithm was tested on the public data set NTU RGB+D 60 and there is a certain improvement in the recognition rate.
VarASV: Enabling Pitch-variable Automatic Speaker Verification via Multi-task Learning

With the proliferation of voice assistants, using voice commands in Energy Internet becomes common practice, e.g., inspectors could control the substation inspection robot to perform automatic detection and information query by voice instructions. However, existing ASV systems rarely consider the change of the speaker's pitch and thus perform poorly on the pitch-variable speaker scenarios. In this paper, we propose VarASV, a pitch-robust automatic speaker verification system to verify the identity of the speaker whose pitch can be various in different situations. To overcome the challenge of variable pitch, we designed a multi-task learning (MTL) framework which contains speaker, gender, and language verification tasks, to train the feature extractor, which is based on residual network (ResNet), Then, we employ the feature extractor to generate the identity embedding that is used for verification. Since gender and language are the pitch-related labels in MTL, they could make the feature extractor more robust to pitch-variable utterances. Through the evaluation of an open-source dataset (i.e., JukeBox), we demonstrate that VarASV has a 21.68% EER value and an 18.37% improvement compared with the baseline model (i.e., i-vector model).
On the Information and Communicative Bases of the Coronavirus Pandemic

The concept of the information and communication nature of the COVID-19 virus is presented, based on the results of research conducted by the author in the field of social communication. Some provisions of the author’s theory of information viruses and their ability to influence the human body and large masses of the population are considered. It is proposed to consider the mechanism of the influence of coronavirus on the population as intersystem communication of the resonant field type.
Research and application of recommendation algorithm based on bidirectional attention model

Since the beginning of the 20th century, with the continuous development of computer technology, more and more people began to use the convenience of computer to improve efficiency, but just because a large number of Internet users continue to increase, there is also a situation of information overload, which will lead to some problems, such as the huge amount of data, the extraction and utilization of effective information will increase Difficulties. The second is the previous recommendation algorithm, most of which predict through the score, but if only through the score, it will not make full use of other data information in the data set.In order to make the experimental results more convincing, this experiment uses a recommendation algorithm based on two-way attention model. First, the movie attributes and user attributes are processed by Convolutional Neural Network (CNN), and then through the full connection layer and the built attention model, effective information is obtained. Finally, the predicted score is generated and compared with the real score, and the final result is obtained Fruit.This experiment uses the movielens data set, by changing the parameters to affect the experimental results, so as to determine the final value of each parameter. This experiment is a comparative experiment, the recommendation algorithm with two-way attention model is compared with many previous algorithms, and the final conclusion is drawn. The experimental results are expressed by RMSE and MAE. According to the index results, the recommendation algorithm proposed in this experiment has better performance.
Image compression based on SVD algorithm

SVD(matrix singular value decomposition) through the singular value decomposition of the digital image matrix, the image is transformed into a singular value matrix containing non-zero value, to realize image compression and facilitate the storage and preservation of the image. Based on SVD image compression, this paper proposes a new method for determining the first K eigenvalues. The method of the matrix singular value decomposition to determine the first K eigenvalues is well supported by the python experimental environment. Experiments show that the compression ratio has reached 5.3 at the threshold of 0.5. The compression ratio is effectively increased when the threshold changes in the range of 0.01.
The Dark Side of Due Process – Part III: How to Use Irreverent Double-Talk to Speak Back to Bad Men

This is Part III of a three part series known as The Dark Side of Due Process published at St. Mary’s Law Journal. Parts I and II precede this article in consecutive issues of this volume at St. Mary’s Law Journal. An Abstract and Foreward for this project are printed at the beginning of Part I, and a general conclusion is printed at the end of Part III. Due process is the fountainhead of legitimate government coercion. When an individual’s rights of life, liberty, or property are at stake, the government is meant to apply due process of the law or suffer reversal of its intrusions as a plain trespass. However, such reversals are merely theoretical, premised upon the willingness of federal judges to interpose their power for the protection of ordinary individuals.The willingness of federal jurists to check the other branches of government for individual rights is transient at best. They do not usually check the global, dragnet U.S. surveillance programs that clearly violate the holding in Kyllo v. United States. Their prophylactic measures like Miranda warnings and the exclusionary rule have proven mere symbols of contradiction and irony. Whenever our institutions appear to be overrun with injustice, well-meaning lawyers always seem to suggest that a Mathews balancing test could solve everything. The seductive belief that a utopia lies just on the other side of a balancing test confirms our doom under the ironies of panoptic Benthamism. As Justice Brennan argued, in dissent of Mathews’ sister case Stone v. Powell, the new cost/benefit balancing tests could be a mere “garb” to add an air of respectability to judicial error.Justice Powell, the author of Mathews and Stone, was himself a trained Bernaysian propagandist. While on the bench, Powell’s public relations agenda seemed to favor the normalization of injustice through cost/benefit due process ideologies. If Justice Brennan searched a little further into the claims Justice Powell made about the Lochner era, he might have exposed Mathews even more effectively. For it appears that Mathews balancing tests were derived from Buck v. Bell and eugenic pseudo-science. Despite an aversion to the darkness, this project does not seek to excoriate the dark side of due process. Rather, like Goethe’s Faust, it positions a listening ear in the direction of dark spaces. The aim of this project is to illuminate the substance of penumbral rights and cost/benefit balancing tests—especially their role in systems of oppression. The intended result will be a foothold for seekers of justice in one of our darkest eras yet.
Risks and Countermeasures of Cyber Security Enterprises

While the Internet penetration rate in China continues to increase, cyber security problems occur more frequently. The improvement of the level of risk management of cyber security enterprises is of great significance to the quality of the development of cyber security enterprises. Cyber security companies have the characteristics of fierce market competition, high R&D requirements, weak corporate management awareness, and large impacts from macro policies. Through public data analysis and the use of financial model of listed companies, it can be found that they face R&D risks, human resources risks, market risks, financial risks, and macro risks. Cyber security companies should strengthen product technology while paying attention to market demand, focusing on technical personnel and corporate management talents, flexibly choosing their own development strategies according to market conditions, and focusing on financial risks to reduce the above risks and achieve sustainable high-quality development. The government should increase support for cyber security companies and promote the importance of cyber security to promote the development of the cyber security industry.
Comparison of Algorithms on Breast Cancer Detection in Developing Countries

All over the world including Bangladesh Breast Cancer is one of the major cancer in women. According to a survey per minute, approximately 1 in 4 cancer patients in the world is who are diagnosed with breast cancer. Immoderate 41,000 women die because of breast cancer annually. Breast cancer is caused by several environmental factors and is caused by interactions between genetically sensitive hosts. Normally, other cells in the body divide and stop over and over again as needed. But in the cancer cell, this process occurs and does not stop. It can be one of the nothing but common cancers in both men and women, although women are in addition likely to get the disease. The risk of death can be greatly reduced by the early detection of cancer and with proper medical care. There are certain signs and symptoms of this cancer that can be used to predetermine if he/she is suffering from this disease. This paper aims to analyze the signs and symptoms with the help of machine learning. To diagnose the disease with better accuracy, the field of machine learning can help the medical professionals by using algorithms such as decision tree algorithm, random forest algorithm, linear regression algorithm, logistic regression algorithm, naive Bayes, and support vector machine. In this paper, we used a number of machine learning algorithms to detect the breast cancer patients. After using the algorithms, gradually and comparing these algorithms will find which algorithm gives principle accuracy. Using these algorithms for detecting patients can get accurate results.
Research on the application of text generation in medicine

Short text extension is a challenging task in text generation. This paper aims to generate a long abstract text with rich information, smooth and close to the topic by inputting a short sentence, providing referable ideas for researchers, standardizing people’s paper writing and improving efficiency. In this work, we constructed a medical related English data set from PubMed on data mining, natural language processing and machine learning. On this data set, we used the dynamic_kp sampling method for training based on minGPT, and compared it with top_k sampling and top_p sampling methods. Then we evaluate the results produced by these methods both automatically and manually. The results show that the abstracts generated by our method are more consistent, more fluent and more human-like.
A Dark Flash Normal Camera

Casual photography is often performed in uncontrolled lighting that can result in low quality images and degrade the performance of downstream processing. We consider the problem of estimating surface normal and reflectance maps of scenes depicting people despite these conditions by supplementing the available visible illumination with a single near infrared (NIR) light source and camera, a socalled "dark flash image". Our method takes as input a single color image captured under arbitrary visible lighting and a single dark flash image captured under controlled front-lit NIR lighting at the same viewpoint, and computes a normal map, a diffuse albedo map, and a specular intensity map of the scene. Since ground truth normal and reflectance maps of faces are difficult to capture, we propose a novel training technique that combines information from two readily available and complementary sources: a stereo depth signal and photometric shading cues. We evaluate our method over a range of subjects and lighting conditions and describe two applications: optimizing stereo geometry and filling the shadows in an image.
User Identity Recognition Based on Wireless Sensor Network and Internet Finance Development

With the rapid development of computer network technology, the concept of "Internet +" has become more and more popular in recent years. The combination of the Internet and finance has particularly attracted people's attention, and the operating modes of many industries have also changed. Since the use of Internet technology can achieve data sharing and information exchange, the "Internet + Finance" model has broken the barriers of information asymmetry in the financial sector in the past and has made great contributions to China's multiple improvements. The financial market is very important to China's economic development. The identification of the ID function of the wireless sensor network is susceptible to interference and the identification accuracy is reduced. We propose an adaptive identification feature recognition algorithm based on an improved minimum gray tree. After calculating the similarity, the nearest neighbor matching algorithm is directly used to obtain the minimum matching cost corresponding to the wireless sensor network registration that is regarded as the recognized identity so as to realize the identity function adaptive recognition. In this regard, the simulation results show that the proposed algorithm has high recognition accuracy. With the pace of financial innovation, financial institutions have achieved rapid development on the basis of Internet service platforms. At the same time, as the core of preventing money laundering activities, financial institutions are also facing many issues in identifying "customers" in their work. This article analyzes the main content, implementation effects, and difficulty of customer identification in financial institutions and proposes relevant improvement plans.