Reviewerly/exHarmony huggingface dataset:
Enabling Efficient Decentralized and Privacy Preserving Data Sharing in Mobile Cloud Computing

Mobile cloud computing (MCC) is embracing rapid development these days and able to provide data outsourcing and sharing services for cloud users with pervasively smart mobile devices. Although these services bring various conveniences, many security concerns such as illegally access and user privacy leakage are inflicted. Aiming to protect the security of cloud data sharing against unauthorized accesses, many studies have been conducted for fine-grained access control using ciphertext-policy attribute-based encryption (CP-ABE). However, a practical and secure data sharing scheme that simultaneously supports fine-grained access control, large university, key escrow free, and privacy protection in MCC with expressive access policy, high efficiency, verifiability, and exculpability on resource-limited mobile devices has not been fully explored yet. Therefore, we investigate the challenge and propose an Efficient and Multiauthority Large Universe Policy-Hiding Data Sharing (EMA-LUPHDS) scheme. In this scheme, we employ fully hidden policy to preserve the user privacy in access policy. To adapt to large scale and distributed MCC environment, we optimize multiauthority CP-ABE to be compatible with large attribute universe. Meanwhile, for the efficiency purpose, online/offline and verifiable outsourced decryption techniques with exculpability are leveraged in our scheme. In the end, we demonstrate the flexibility and high efficiency of our proposal for data sharing in MCC by extensive performance evaluation.
Analysis of Deep Anomaly Detection Algorithms

Anomaly detection, or outlier detection, is an important branch of machine learning. It has a wide range of applications, such as industrial damage detection, fraud detection and video surveillance detection. However, there are many challenges in anomaly detection. For example, it is difficult to define whether it is normal or abnormal as the anomalies are different in different scenarios, and the normal behavior is constantly changing. There are several methods of anomaly detection, including graphic method, statistical method, distance based method, density based method and model-based method. This paper mainly introduces the distance based method k-th nearest neighbor (KNN), density based method local outlier factor (LOF), model-based method isolation forest (iForest), and the author intends to find out their advantages and disadvantages. Finally, it is found that different algorithms work well in their own area. In fact, most of the algorithms has the problem of the curse of dimensionality. Additionally, the author points out that anomaly detection, which is based on semi supervised or weak supervised, is likely to be the future research direction.
Segmentation of Ultrasound Thyroid Nodules Based on Encoder-Decoder Structure

This paper compares four ultrasonic segmentation methods of thyroid nodules. This research uses the encoder-decoder neural network structure to learn deeper semantic information in the down-sampling stage, gradually restore the position information in the up-sampling stage, and finally use skip connection integration features to complete the semantic segmentation of thyroid nodules. This paper tried four encoder-decoder structures on the ultrasound database of thyroid nodules. These experiments show that the structure is effective (maximum mIOU is 81.2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> ).
Personalized Cryptographic Protocols for Advanced Data Protection

In data protection processes, security is increasingly being used, which is to some extent related to the creator of a given solution. Data security techniques based on the use of personal methods of securing information are increasingly used. Such solutions guarantee full data protection. This paper presents personalized data security techniques dedicated to advanced data protection processes.
The Role of Independent Work in the Formation of Professional Reliability of Future Engineers

The article considers the organization of independent work as one of the foundations of the professional development of the future engineer's personality, considering it an important positive factor of his professional reliability. The ratio of the level of professional reliability of the student to the success of his independent work is revealed. The dynamics of the development of students’ independent work by means of a foreign language is presented.
Adversarial Constraint Evaluation on Biomedical Text Mining

Language model pre-training has been shown to be effective for improving many NLP tasks, relying on its ability of representing complex context. The BERT language model (LM) has achieved remarkable results in standard performance indicators for tasks such as Named Entity Recognition (NER) and Semantic Text Similarity (STS), and has made significant progress in the field of biomedical natural language processing. This paper proposes a adversarial evaluation scheme for medical NER and medical STS. To this end, we propose three types of attack constraints, including semantics, grammaticality, and non-suspicion, as well as their corresponding automatic evaluation mechanisms. In these adversarial environments, the accuracy of the model drops significantly, and we quantify the extent of this performance loss. We also demonstrate that through adversarial training of each comparative model, the robustness of the model can be significantly improved.
A Website Fingerprinting Attack Based on the Virtual Memory of the Process on Android Devices

Website Fingerprinting Attack (WFA) which identifies websites browsed on Android devices is extremely dangerous because it creates an opportunity for stealing private information. As the most feasible WFA method, we focus on a method that can identify a website by using the power consumption model restored from CPU data. However, that is not effective in a real situation where multiple background tasks run because CPU data which are unrelated to browsing are confused. Furthermore, the previous method cannot accurately identify simple websites that are subject to background tasks. Thus, a more feasible method is required to indicate the dangers. In this paper, we propose a website fingerprinting attack based on virtual memory of process on Android device. We focus on the fact that a specific process about browsing websites works when a website is browsed. Because each process has its virtual memory which is independent of each other, the useful feature of a task can be extracted from the virtual memory without noise. Therefore, the proposed method can precisely identify a browsed website by using the virtual memory-based features even if background tasks work. Furthermore, the proposed method can obtain effective information even for a simple website. By computer simulation with a real dataset, we demonstrate that the proposed method can improve up to 86%, 89%, and 82% in precision, recall, and F-measure, respectively for websites which the previous scheme cannot identify at all.
Evaluation and Analysis of Robustness of Adversarial Examples Attacks in Deep Neural Networks

Neural networks have revolutionized the field of artificial intelligence. They have given rise to several applications in various areas. However, it has been shown that they have flaws that could be leveraged by an attacker to perform an adversarial examples attack. Several studies have attempted to design defensive mechanisms that would ensure the security of neural networks. Nevertheless, these mitigation techniques remain insufficient to entirely address all the vulnerabilities that may reside in these architectures. In this article, we will extensively address the security of neural networks. We will begin with a study of the different approaches towards the security of neural networks. Then, we will detail significant sources of vulnerabilities in neural networks. Afterward, we will examine the theory of adversarial examples attack as well as the optimization problem related to them. Thereafter, we will conduct a comparative study of the most common mitigation techniques in the literature. Finally, we will propose a framework devoted to assessing adversarial examples robustness.
Research on GFF Module of Instance Segmentation Based on Deep Learning

Instance segmentation based on deep learning is one of the popular research direction in the field of artificial intelligence. It is widely used in fields such as intelligent manufacturing and autonomous driving. At present, most of the instance segmentation are based on the object detection. The defect of this scheme is that the pixels used to predict the mask cannot fully contain all the information of the target object, resulting in the decline of segmentation accuracy. The accuracy of instance segmentation will affect the reliability of the application that applies computer vision programs. To solve the above problems, the paper proposes a global feature fusion module to improve the prediction accuracy of mask in instance segmentation. GFF module can extract the feature of the whole size feature map directly. GFF module can make up the target information of the pixel corresponding to the receptive field of the feature map that cannot be covered. The actual receptive field is smaller than the theoretical receptive field, which leads to some loss of information in the high-level feature map. The feature map produced by GFF module can also be used as supplementary information to add to the prediction of the mask to solve the problem of the limitation of theoretical receptive field and practical receptive field. At the same time, GFF module only uses one hyperparameter, which reduces the difficulty of network hyperparameter adjustment. It was added to YOLACT to improve the mask prediction accuracy of the mask. Although the structure of YOLACT becomes more complex after adding GFF module, the overall parameters of the network do not change much. The reasoning speed of YOLACT-GFF with resnet50 as the backbone is 20.79 FPS and mAP is 28.3 under the condition of GTX 2080ti.
Nano-optical devices based on the nature of the dressed photon

A number of novel nanometer-sized optical devices, named DP devices, are reviewed: logic gates, a nano-optical condenser, an energy transmitter, an optical buffer memory, a digital-to-analog converter, a frequency upconverter, and an optical pulse generator. Novel phenomena, demonstrated by the operation of these DP devices, are presented. As examples of the superior performance levels and unique functionality of DP devices, single-photon operation and extremely low-energy consumption are demonstrated.
Designing an Experience Process for Digital Fabrication to Motivate Newcomers

The purpose of this study is to design the stages where newcomers in digital fabrication can learn. Although many newcomers are interested in digital fabrication, few of them can learn continuously. To overcome the problem, this study assumes that maintaining motivation will contribute to the solution and proposes a step-by -step experiential process that will enable the acquisition of basic skills without losing their motivation.
A Framework for GIS-Based Site Selection and Technical Potential Evaluation of PV Solar Farm Using Fuzzy-Boolean Logic and AHP Multi-Criteria Decision-Making Approach

Electricity demand is increasing mainly due to population expansion and the continuous supply of electricity in the residential, industrial, and service sectors. This energy is a critical factor of economic growth, and clean energy resources must be utilized and substituted to reduce the effects of climate change and move toward sustainable development. Site selection and construction of renewable power plants help to diversify the country's energy basket. This study aims to locate and evaluate a photovoltaic solar power plant's potential in Khuzestan province, using Fuzzy-Boolean logic and Analytical Hierarchy Process (AHP) decision analysis based on GIS. The main criteria for selecting the optimal locations include climatic (annual horizontal solar irradiance, sunshine hours, air temperature, relative humidity, windspeed), economic (the proximity to roads, transmission lines, substations, urban areas, and villages), orography (slope, orientation, and altitude) and the environment. According to the results, 0.12% of the province's area has excellent suitability, 25.66% has suitable suitability, and 59.46% lacks any suitability for installing a photovoltaic system. The annual electric power generation potential using single-crystalline silicon panels and polycrystalline silicon panels is calculated as 3189.54 and 2453.49TWh, respectively. Izeh, Bandar-e-Mahshahr, and Bagh-eMalek cities are the most suitable areas for photovoltaic solar farms in province.
Application of BP neural network technology on dynamic financial risk prediction

Abstract This work mainly studied the application of BP neural network technology. Based on the crisis early warning of listed real estate companies at home and abroad, this study used BP neural network method to determine the corporate governance indicators of crisis enterprises from eight aspects, such as profitability, solvency, operation ability, development ability, cash flow, risk, etc. This work took the real estate listed companies as the research object and put forward eight early warning indicators. Traditional early warning models have low fault tolerance, and most of them are static early warning. In this study, the innovation point is that the BP neural network model has the ability of self-learning and adjustment, strong risk identification and fault tolerance, and can cope with the changeable financial risk environment of real estate enterprises. BP neural network model can analyze problems through self-learning and training, and has strong nonlinear mapping ability. It is feasible to construct financial risk early warning model of real estate listed enterprises.
Combining BERT and Multiple Embedding Methods with the Deep Neural Network for Humor Detection

Humor detection from written sentences has been an interesting and challenging task in the last few years. Most of the prior studies have been explored the traditional approaches of embedding, e.g., Word2Vec or Glove. Recently Bidirectional Encoder Representations from Transformers (BERT) sentence embedding has also been used for this task. In this paper, we propose a framework for humor detection in short texts taken from news headlines. Our proposed framework attempts to extract information from written text via the use of different layers of BERT. After several trials, weights were assigned to different layers of the BERT model. The extracted information was then sent to a Bi-GRU neural network as an embedding matrix. We utilized the properties of some external embedding models. A multi-kernel convolution in our neural network was also employed to extract higher-level sentence representations. This framework performed very well on the task of humor detection.
CADBAIG: Context-Aware Dictionary-Based Automated Insight Generator

The backbone of the growth of any field and its contribution toward the society depends on the research and development in that domain. To conduct comprehensive research, the state of art plays an important role but captures a great amount of human hours. In order to reduce this effort, we have proposed an approach to generate insights of research papers. It is done with the help of local context derived from the inherent section level structure of the paper. The local context consists of the title, abstract, and keywords of a research paper. The proposed approach, namely, CADBAIG (Context-Aware Dictionary-Based Automated Insight Generator) consists of six stages. The input to CADBAIG can be the distinct components of local context or the different combinations and the output is the respective extractive summary. The extractive summary is produced by matching multi-word ranked phrases extracted using PyTextRank. The CADBAIG is tested on various research papers. To prove the work, the results are shown by applying the proposed approach on a sample research paper. In addition, to know more about the working of the proposed approach and its respective conclusion, proposed work and conclusion sections of the research paper are also given as input to CADBAIG. The summaries, so generated by the local context, proposed work, and conclusion are compared. It has been observed that the summary generated from local context is more promising than the individual section summaries. The proposed approach can be used to extract the insights from any research paper, irrespective of the domain.
SCIENCE AS A SOCIAL PHENOMENON AND TRENDS IN THE DEVELOPMENT OF SCIENCE IN THE MODERN WORLD

The article is devoted to understanding the place, role and functions of science in the modern world. The author analyzes the process of development of science from the initial individual forms of organization of scientific activity to its transformation into a social institution. The specifics of the process of cognitive and social institutionalization of science are considered. The main conditions for the development of science in the status of a social institution are revealed. The close connection of science with political, economic and social institutions and also the possibility of its development with the state regulation are shown.
Parallelization of the Poisson-Binomial Radius Distance for Comparing Histograms of n-grams

Text documents are typically represented as bag-of-words in order to facilitate subsequent steps in their analysis and classification. Such a representation tends to be high-dimensional and sparse since, for each document, a histogram of its n-grams must be created by considering a global—and thereby large—vocabulary that is common to the whole collection of texts under consideration. A straightforward and powerful way to further process the documents is computing pairwise distances between their bag-of-words representations. A proper distance to compare histograms must be chosen, for instance the recently proposed Poisson-Binomial radius (PBR) distance which has shown to be very competitive in terms of accuracy but somehow computationally costly in contrast with other classic alternatives. We present a GPU-based parallelization of the PBR distance for alleviating the cost of comparing large histograms of n-grams. Our experiments were performed with publicly available datasets of n-grams and showed that speed-ups between 12 and 17 times can be achieved with respect to the sequential implementation.
Calibration and Measurement of Large Distortion Binocular Camera Based on Fully Connected Neural Network

Camera calibration is of great significance in computer vision technology. Large distortion binocular camera is notoriously hard to calibrate accurately using traditional calibration methods. In this paper, we present a new calibration method for large distortion binocular camera based on fully connected neural network. Similarly to planar calibration, it relies on multiple images taken by binocular camera. Compared with traditional calibration methods, the number of images do not increase significantly. Instead of building complex analytical models, we only take some images to train quickly and efficiently the fully connected neural network for different binocular camera systems. The neural network fit accurately the nonlinear mapping relationship of binocular vision imaging model. Experiments show that, compared with traditional calibration methods, this approach calibrate large distortion binocular camera with higher accuracy and stability.
Synthesizing New Expertise via Collaboration

Consider a set of classes and an uncertain input. Suppose, we do not have access to data and only have knowledge of perfect experts between a few classes in the set. What constitutes a consistent set of opinions? How can we use this to predict the opinions of experts on missing sub-domains? In this paper, we define a framework to analyze this problem. In particular, we define an expert graph where vertices represent classes and edges represent binary experts on the topics of their vertices. We derive necessary conditions for an expert graph to be valid. Further, we show that these conditions are also sufficient if the graph is a cycle, which can yield unintuitive results. Using these conditions, we provide an algorithm to obtain upper and lower bounds on the weights of unknown edges in an expert graph.
Toward the Set of Principles for Enhancing Open Cultural Data Reusability in Educational Context

Open cultural data is comprised of digitized heritage artifacts, released by heritage institutions into open access. It constitutes a powerful tool to transform current educational practices, with applications ranging from textbook augmentation to digital games and data-based services. That transformation is achieved via data reuse, allowing reconceptualization of available datasets in creative ways. However, successful implementation of data reuse initiatives is possible only if technical prerequisites are met. Review of existing open data governance principles has revealed that aspects crucial for reuse are not covered, therefore hindering the wide adoption of reuse practices. In this paper, we present a set of principles to act as guidelines for enhancing the reusability potential of open cultural data. Proposed set of principles was utilized to analyze Estonian open cultural data landscape and formulate improvement suggestions. Our analysis has shown substantial gaps which should be resolved before wide-scale educational reuse of Estonian open cultural data that could be initiated.
Improved JPEG image watermarking in the data compression domain

The joint photographic experts group (JPEG) is the most popular format among various digital image formats used in our daily life. Therefore, watermarking JPEG images is important and useful for many applications such as archive management and image authentication. Along with the embedding capacity and imperceptibility that have to be considered for uncompressed images, the storage size of the watermarked JPEG image should be considered. In this paper, based on the philosophy behind the JPEG encoder and the statistical properties of discrete cosine transform (DCT) coefficients, we propose a new JPEG image watermarking in the data compression domain, in which only coefficients with values 1 and -1 are expanded to carry message bits, coefficients with values 2 and -2 are shifted, and all the others coefficients remain unchanged. Moreover, a block selection strategy based on the number of 1 and -1 coefficients in each $8 \times 8$ block is proposed, which can be utilized to adaptively choose DCT coefficients for data hiding. Experimental results demonstrate that by using the proposed method, we can easily realize high embedding capacity and good visual quality. Furthermore, the storage size increase for the watermarked JPEG image is well lessened.
Improving Autoencoder Training with novel Goal Functions based on Multivariable Control Concepts

Autoencoders are becoming more representative in all fields of knowledge, due to their ability to classify, compress, and identify data patterns. This study objective was to propose entirely new objective functions using multivariable process control concepts as the gain matrix and Relative Gain Array to improve the quality of prediction and classification of an autoencoder. The advantages of the proposed approach are illustrated through a pulp-and-paper industry. The new function results show an improvement in the detection, leading to savings of up to 22 to 38 thousand dollars per month compared to a model using only MSE.