Reviewerly/exHarmony huggingface dataset:
A Secure Cloud Storage Based Multi-Server Public Key Encoding for Keyword Word

Accessible catchphrase is of extraordinary enthusiasm for protecting the data protection in secure accessible distributed storage. In this paper, we have an attempted to explore the security of an outstanding logical control crude, in particular, open key encoding with catchphrase search (PEKS) that is incredibly useful in a few uses of distributed storage. Unfortunately, it's been demonstrated that the standard PEKS system experiences partner degree natural uncertainty known as inside watchword gauge assault (KGA) propelled by the malevolent server. To manage this security defenselessness, we tend to propose a shiny new PEKS system named double server PEKS (DS-PEKS). As another primary commitment, we tend to diagram a fresh out of the plastic new variation of the smooth projective hash capacities (SPHFs) expressed as straight and homomorphic SPHF (LH-SPHF). we tend to then demonstrate a conventional development of secure DS-PEKS from LH-SPHF. for example the practicability of our new system, we offer partner degree affordable portrayal of the last structure from a decision Diffie–Hellman-based LH-SPHF and demonstrate that it can do the hearty protection from inside the KGA. Accessible cryptography will be cultivated in either respective or uneven cryptography setting. In, Song et al. anticipated watchword search on ciphertext, alluded to as Searchable reciprocal cryptography (SSE) and later numerous south southeast plans , were intended for upgrades. despite the fact that south southeast plans get joy from high power, they experience the ill effects of modern mystery key dispersion. Unequivocally, clients should immovably share mystery keys that square measure utilized for encryption. Else they're incapable to share the encoded data re-appropriated to the cloud. To determine this drawback, Boneh et al. Presented an extra adaptable crude, explicitly Public Key cryptography with Keyword Search (PEKS) that enables a client to go glimpsing encoded data inside the uneven cryptography setting. in an exceedingly PEKS framework, exploitation the recipient's open key, the sender connects some scrambled watchwords (alluded to as PEKS ciphertexts) with the encoded data. The collector at that point sends the trapdoor of a to-be-scanned catchphrase to the server for data looking. Given the trapdoor and furthermore the PEKS ciphertext, the server will investigate whether the watchword fundamental the PEKS ciphertxt is up to the one choose by the collector. Provided that this is true, the server sends the coordinating encoded data to the collector.
Preparing pure states with lossy beam splitters using quantum coherent absorption of squeezed light

We investigate coherent perfect absorption (CPA) of squeezed coherent states of light by an absorbing beam splitter. First we derive the absorption coefficients for quantum coherence and for intensity, which generally differ. Secondly, we present the remarkable properties of a CPA-gate: two identical but otherwise arbitrary incoming squeezed coherent states can be completely stripped off their coherence, producing a pure entangled squeezed vacuum state at the output. Importantly, this output state of light is not entangled with the absorbing beam splitter by which it was produced. This makes the CPA gate potentially interesting for continuous-variable quantum state preparation.
Research of Differential Privacy Protection on Social Network

Abstract Rapid development has been witnessed in social network. As a consequent, much more people are involved in getting it, sharing their own information to enlarge their social circle. In this paper, the security problem, which exists in privacy preserve in social network and process users’ private information via Differential Privacy Histogram Release is focused on. The method can process user’s privacy information on the premise of guaranteeing the availability of information, making the information released after publication different from the information before publication, thus making the user’s privacy information not leak. Shown by theoretical analysis and research results, safe release of private information can be guaranteed if processed by this method.
New Encryption Method with Adaptable Computational and Memory Complexity Using Selected Hash Function

In this paper, we describe the new method of data encryption/decryption with selectable block and key length and hash function. The block size directly improves the efficiency but also impacts on memory complexity of the presented solution. The choice of hash function length results in computational complexity and complicates attacks using rainbow tables. The algorithm is a modification of SP (Substitution-Permutation) concept with the use of static S-blocks and dynamically indexed block permutation. In further step, usage of chosen operational modes of the method (ECB, CBC, CTR) is presented. In the article there is the example with all the algorithm parameters i.e. input and output data, key value, hash function type and set of all method internal states. The efficiency of the solution was experimentally examined and compared with the most popular bock cipher algorithms e.g. AES, Serpent, BlowFish, TwoFish. The obtained initial results indicate that the new method can be dedicated especially for systems with high security requirements. The paper conclusions contain propositions of algorithm implementation optimizations and further research.
Sonic Virtuality, Environment, and Presence

The article presents a brief introduction to the concept of sonic virtuality, a view of sound as a multi-modal, emergent perception that provides a framework that has since been used to provide an explanation of the formation of environments. Additionally, the article uses such concepts to explain the phenomenon of presence, not only in virtual worlds but also in actual worlds. The view put forward is that environment is an emergent perception, formed from the hypothetical modelling of salient worlds of sensory things, and it is in the environment that we feel present. The article ends with some thoughts on the use of biofeedback in computer games as part of the immersive technology designed to facilitate presence in such worlds.
Automatic analysis of image quality by ETR-1 for the On-Board Imager

The test-plate image of an image quality test tool is processed. The processing is based on quality assurance with the well-established test device ETR-1. A program is developed to analyze the parameters such as contrast, low contrast and resolution automatically. This results in more accurate patient positioning for the On-Board Imager (OBI) system. The contrast and resolution are measured by means of Bresenham's line algorithm. The low contrast is calculated with the help of binary masking. The modulation transfer function (MTF) is also observed for the system. The developed program imports the Digital Imaging and Communications in Medicine (DICOM) image and returns the image parameters. The program can process the ideal image or the less noisy image. The no-rotation-mode or the slight-rotation-mode of the test-plate can be analyzed.
The role of The University Center for Social Development in the regional innovation ecosystem of social assistance.

The stable innovation system generation is one of the Russian economic policy priorities. Universities have the role of a central hub in the regional innovation systems formation. This article presents a study of factors influencing the formation and development of the university’s innovation environment and examples of innovation activities of the Krasnoyarsk State Pedagogical University V.P. Astafyev (KSPU) in the regional innovation ecosystem of social assistance. The second section of the article is devoted to the exchange of experience and the results of the university becoming the center of social development in the regional innovation ecosystem of social assistance. An important aspect of this part is a positive result in several key areas: Globalization - mobility and increased competition between universities in China, South Korea, Japan, Poland, Germany, France, Lithuania, Belarus, Ukraine, Kazakhstan and the United States; Multidisciplinary and Interdisciplinary - the integration of science, technology and design, teams from different faculties and universities; and Corporatization - specialized institutes of applied research, and extension of stakeholders. The final section presents the Transformation Program of the Krasnoyarsk State Pedagogical University at the University Center for Social Development of the Krasnoyarsk Territory for current and future operations. The program includes both initiatives and ongoing projects. Today, many successful examples prove that the Center for Social Development in the field of social assistance of the Krasnoyarsk State Pedagogical University plays an important role in the development of the region. Conclusion. Krasnoyarsk State Pedagogical University really stands on the route to the social entrepreneurship development and influx of new technologies, introduction of innovative approaches, and becomes the center of social and project competencies of the Krasnoyarsk Territory, one of the leading drivers of social development and of social assistance of the region.
A Study on Strengthening Privacy and Cyber Security using Block Chain

Objectives: The work is to analyze the block chain technology to improve the privacy and cyber Security. Methods/ Statistical Analysis: The blockchain can be used in cloud to protect data stored in them and also block chain-based identity and access management system address the key challenges visage in IoT security. Findings: The findings show that the blockchain lends itself well to Internet of Things (IoT) applications. It provides more security in the exponentially growing networks and holds each part involved accountable in transaction. Finally conclude that the combination of block chain and IoT can provide a powerful approach which can significantly pave the way for new business models and distributed applications. Application/Improvements: The blockchain achieves safety and secrecy of distinctiveness and other private data in health care industry. Keywords: Blockchain, Cyber Security and Health Care Industry, Internet of Things (IoT)
Investigation of moving objects through atmospheric turbulence from a non-stationary platform

In this work, we extract the optical flow field corresponding to moving objects from an image sequence of a scene impacted by atmospheric turbulence and captured from a moving camera. Our procedure first computes the optical flow field and creates a motion model to compensate for the flow field induced by camera motion. After subtracting the motion model from the optical flow, we proceed with our previous work, Gilles et al,<sup>1</sup> where a spatial-temporal cartoon+texture inspired decomposition is performed on the motion-compensated flow field in order to separate flows corresponding to atmospheric turbulence and object motion. Finally, the geometric component is processed with the detection and tracking method and is compared against a ground truth. All of the sequences and code used in this work are open source and are available by contacting the authors.
FPGA-based Acceleration of Binary Neural Network Training with Minimized Off-Chip Memory Access

In this paper, we examine the feasibility of FPGA as a platform for training a convolutional binary-weight neural network. Training a neural network requires more data movement compared to inference. Acceleration of training on an FPGA is, therefore, a challenge because the data movement increases off-chip memory accesses. We try to address this problem by storing most of the data in the on-chip memory and adopting batch renormalization. This allows for training a large network by reducing the required intermediate data and its movement. For the case where all data except the input images can be stored on an FPGA chip, we present an accelerator for training CNNs to classify the CIFAR-10 dataset. Further, we study the impact of network size on performance and energy of FPGA and GPU. Our accelerator mapped in the Arria 10 FPGA chip obtains up-to 9.33X higher energy efficiency compared to the Nvidia Geforce GTX 1080 Ti GPU at similar performance.
Is Information Density a Reliable Universal Predictor of Eye Movement Patterns in Silent Reading?

The role of information density as a reliable universal predictor of eye movement patterns in silent reading is considered. Density differences between Russian and English are taken to explain the difference in eye movement patterns for readers with Russian as a native language compared to English-speaking readers. An empirical eye tracking study shows that only one of four expectations got confirmed. Supposedly, the eye-movement pattern observed for Russian could be influenced by some additional language-specific properties of Russian other than information density. We conclude that a universal algorithm that allows to predict eye movement patterns during silent reading based on language density only hardly ever exists.
Digit recognition based on programmable nanophotonic processor

Artificial neural networks are computational models enlightened by biological neural networks, playing a significant role in image recognition, language translation and computer vision fields, etc. In this paper, we propose a fully optical neural network based on programmable nanophotonic processor (PNP) to realize digit recognition. The architecture includes 4 layers cascaded Mach–Zehnder interferometers (MZIs), which could theoretically execute matrix functions corresponding to a two-layer fully connected ANN with four inputs. We simulate cascaded MZIs and adjust phase shifters to match weight matrices calculated by ANN in computer beforehand. The accuracy of 4-class handwritten digits in ONN is 80.29% due to the compressed input data. The accuracy of 10-class digits could achieve 99.23% when the input node merely increases to 36. The results demonstrate the handwritten digits could be recognized effectively through PNP in ONN and the construction of PNP could be extended for more complex recognition systems.
Extension Method in Case-Based Reasoning and its Application in Artillery System Design

Abstract Case-Based Reasoning (CBR) is a method of designing new product configurations based on previous design case matching. Over the last couple of decades, CBR has grown from a relatively isolated research area to a comprehensive and multi-field one. Extenics is one of fields combined with it. The representation model and extension distance theory in Extenics are surveyed. Based on dependence function, an improved comprehensive evaluation of configuration schemes of a complex modularized product is proposed, considering the interface matching problem that may occur between different components. To illustrate the validity and feasibility of our method, a design case of artillery system is described. The result shows that our evaluation method can better adapt to engineering practice and provide effective and reasonable matching results.
Open Set Recognition for Unique Person Counting via Virtual Gates

Retail shops or restaurants are interested in real-time profiling analysis of customer visit patterns, which could enable efficient management and target marketing. They need to know not only how many people entered but also if they are visiting for the first time and keep track of their exact number. As a result, in this paper we define the new variant of unique counting for videos, that is counting new persons who have not already been counted in the past. To this end, we propose a complete real-time system which is able to perform detection, tracking and unique counting in the wild with user drawn gates. A fine-tuned network on persons body is used to extract descriptors which are more privacy-oriented. Experiments of the system on the challenging DukeMTMC dataset show that our method is able to effectively count people in real time and discern between the persons which do multiple passages through the gates.
Exploring Multi-label Stacking in Natural Language Processing

The task of classification with multi-label data is an important research field in Natural Language Processing (NLP). While there have been studies using one-stage multi-label approaches for automatic text classification, there are not many that use two-stages stacking models. In this paper we explored Binary Relevance (BR) classifiers, with J48 and probabilistic Support Vector Machine (SVM), in a two-stage stacking model. We have evaluated our proposal in three textual data sets: The Movie Database (TMDB), Enron email, and EURLEX European legal text. The results showed that by using a two-stage stacking model, we can obtain better results than by using one-stage classifiers.
Chaos in Series to Avoid Serial Attacks on Image: A Secure Image Communication Perspective

Due to the rapid growth in communication, the transmittal of multimedia contents over the open unsecured channel became vital. Hence the mechanism which provides confidentiality of these contents is needed and that can be achieved with the help of Image Encryption. In this paper, the secured grayscale image encryption was performed using combined chaotic maps namely Logistic Sine-Sine (LS-S) and Logistic Logistic-Sine (LL-S) in transform domain Integer Wavelet Transform (IWT)approach. To validate the proposed algorithm, certain analyses such as Statistical (Correlation, Entropy, Histogram), Error metric (MSE, PSNR), Chosen plain text and cropping were executed. Additionally, the strength of the key was evaluated using key sensitivity and keyspace analyses. The proposed algorithm provides the better near zero correlation and average entropy of 7.9916. The average PSNR of 11.4 dB has been achieved and the keyspace is 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">68</sup> so as immune to the brute force attack.
An Empiric Model of Face Detection based on RGB Skin Tone Color

Abstract Modern smartphone has been society’s lifestyle where every smartphone has a high quality digital camera with all the digital image processing feature. One of those features are face detection. Face detection is the most basic process of any face processing operations. Most digital images are stored in the form of RGB (Red, Green, Blue) data. In this research, detection of human face features is done by using different RGB values in digital images. After applying skin tone color segmentation on digital images, detection area will be optimized using human head properties by eliminating non-human face skin tone area. The experiment shows that the detection is mostly accurate for images but there is issue on low light face skin color captured by digital cameras. From the experiment of our model using 10 sample face images, face can be detected on 9 of them, while in 1 image, the face cannot be detected at all because of low light condition.
Sentiment Analysis Based on Background Knowledge Attention

Sentiment analysis, which is a fundamental research in the field of natural language processing and artificial intelligence field, has received much attention these years because of its practical applicability and the challenges. However, existing methods only focus on local text information and ignore the background knowledge (such as the director of a movie, the producer of a product). In this paper, we propose a novel LSTM with Background Knowledge Attention Model (LSTM-BKAM) for sentiment analysis. Our model incorporates background knowledge based attentions over different semantic parts of a sentence. The experiment results show that our model achieves state-of-the-art, and substantially better than other approaches.
The Effect of Plaintext Length on Round Trip Time With Client Server Based Advanced Encryption Standard (AES) Algorithm

Abstract The length of the plaintext affects the round trip time, where the longer the plaintext, the longer round trip time. In an attempt of 35 plaintext characters obtained 1.171 ms round trip time. Whereas on an attempt of 113 plaintext characters obtained 2.355 ms round trip time. In Figure 2 can be seen the graph increase of the round trip time. The author concludes that the length of the plaintext affects the round trip time in the client server based AES algorithm. The longer the plaintext, the longer the round trip time process will run.
Analysis of Decision Tree and Smooth Support Vector Machine Methods on Data Mining

Abstract This research is about smooth support vector machine (SSVM) and Decision Tree in data mining. Many researchers conduct and develop methods to improve the accuracy and classification of data on good results. This research was conducted by conducting an experiment on STMIK Neumann Medan student data. In this study, it was concluded that Decision Tree performance is better than SSVM, Decision Tree gets very good results that promise to help find the best students to get scholarships. This study is better than SSVM. The training process has a difference of 11.04% and the testing process is 10.08% with each Accuracy.
Increase of flight operations effectiveness using automated control over a fleet of various UAS

Following paper presents a research aiming to increase the efficiency of an Unmanned Aerial System (UAS) fleet during mission execution. The developed system allows to reduce the mission time by considering the fleet diversity. System is based on a multilevel optimization, applying an innovative trajectory generation method. The Mixed Integer Linear Programming (MILP) algorithm was adopted, so the trajectories for individual platform were optimal (shortest). The developed solution considers not only the area but also the UASs performance and sensors capabilities. The mission operator with the dedicated GUI could control and designate the platforms for various tasks. The method was tested on various platforms with different performance. The method proved its efficiency reducing the mission time up to 15%. Finally, the system was successfully tested in real environment with the use of three various platforms.
Vehicle detection in aerial image based on deep learning

Abstract At present, urban traffic congestion is becoming more and more serious. Traditional vehicle detection methods generally have problems of low precision and low recognition rate. In order to solve these problems, a vehicle detection method in aerial image based on deep learning is proposed. The method uses the YOLOv3 algorithm and optimizes the algorithm network, increase the number of rasters of the final predicted output, improves the detection ability of the network for dense groups, and verifies the effect by training a new model. The experimental results show that the new training model has a good effect on vehicle detection, which solves the problem of low detection and recognition rate of urban traffic vehicle detection, and makes good predictions and judgments on urban traffic congestion.