Reviewerly/exHarmony huggingface dataset:
A Secured Method of Reversible Data Hiding

In this paper, a new method of secured reversible data hiding that depends on modified pixel expansion has been proposed. The coloured image is divided into three layers (red, green, and blue); then, the blue-layer will be selected as a cover-media for embedding. Next, the difference between two adjacent pixels of the cover-image is calculated, and the result is assigned to the second pixel. After that, a secret logo of size (256x512) will be embedded into each 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> MSB of the second pixel of the cover-image to obtain a stego-image (blue layer only). Finally, the other layers of stego-image; red and green will be encrypted using the Henon chaotic system, and they will be merged to generate the final encrypted image. The receiver has to use the extraction and recovery process to decrypt the ciphered image to obtain the original layers of the image (red, green, and blue). Also, the secret logo will be extracted from 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> MSB of the stego-image then the original cover-image will be ideally recovered via using the recovery process. The red, green, and recovered blue layer has to be combined to obtain the final original image. According to the results, the proposed approach has achieved the best embedding capacity equal to (0.5 bpp) and optimal image quality for the recovered image. The confidentiality of the stego-image and secret logo has been improved via applying Henon chaotic system. Besides, the proposed method is straightforward for implementation, and it requires a little execution time.
Identity-Based Hybrid Format-Preserving Encryption Scheme

The format-preserving encryption has the characteristics of the encrypted data format and the same data length, and does not break the data format constraints, thereby reducing the cost of modifying the data format. The analysis of existing sensitive information format-preserving encryption schemes is based on a symmetric encryption system, which has problems such as low key transmission security and high key management cost. This paper proposes an identity-based format-preserving encryption scheme. Compared with the existing format-preserving encryption scheme, the communication parties do not need to transmit a key, and the encryption key and the decryption key are generated by the key derivation function, and the hybrid encryption is used. The way to improve the security of sensitive information transmission. It is proved that the scheme satisfies the identity-based pseudo-random permutation security, and at the same time, the scheme satisfies the adaptive selection of ciphertext indistinguishability under plaintext attack.
Accurate Object Detection in Smart Transportation Using Multiple Cameras

Recently, more and more attention has been paid to the connected object detection for better performance. One of the most interesting fields is learning from multiple resources in a connected fashion. In this paper, we present a connected object detection method using multiple cameras for the smart transportation system. The proposed architecture consists of three parts: an alignment framework, a deep multi-view fusion network and an object detection network. Experiments are conducted to illustrate the performance of our proposed architecture.
Boost-histogram: High-Performance Histograms as Objects

Unlike arrays and tables, histograms in Python have usually been denied their own object, and have been represented as a single operation producing several arrays.Boost-histogram is a new Python library that provides histograms that can be filled, manipulated, sliced, and projected as objects.Building on top of the Boost libraries' Histogram in C++14 provided interesting distribution and design challenges with useful solutions.This is meant to be a foundation that others can build on; in the Scikit-HEP project 1 , a physicist friendly front-end "Hist" and a conversion package "Aghast" are already being designed around boost-histogram.
DFS Response Time Prediction Using the Techniques of “Deep Learning”

Big data systems are designed to manage and analyze huge amounts of data that can reach multiple yottabytes. Those systems often rely on Distributed File System (DFS) environments to store data and optimize their processing and exploitation such as HDFS (Hadoop File System) or GFS (Google File System). The particularity of a DFS cluster is to allow Big Data systems to store data on a large number of machines, while guaranteeing their accessibility, their security and the optimization of access. DFS often uses data replication and distribution on several machines to ensure their durability, optimize paralleled queries, and enable data recovery in case of machine failure. The data placement on machines of a cluster generally respects a strategy that promotes data security (having replicas on different machines, racks, sites …). However, it often ignores the optimization of response times, which depends on the capacities of storage machines, the network and the intensity of use of this data. In this study, we propose to predict response time of a DFS Cluster, by taking into account a set of criteria such as locations, file sizes …, using the techniques of “Deep Learning”. Once the response time is predicted, this will allow us to re-configure the system in order to modify the proposed storage locations and make the strategy of files placement more effective.
C++ Language Program Implementation of BINEX-Format to RINEX-Format for Trimble Board

To obtain real-time ephemeris data from the Trimble BD930 board, we created an ephemeris receiving system based on Trimble BD930 board. Since the ephemeris data obtained is in the BINEX format, it cannot be directly adapted to the RINEX ephemeris format required by most receivers, and can only be used after transcoding. Based on an in-depth analysis of the differences between the two formats, we improved the traditional RINEX encoding algorithm and implemented the conversion from BINEX to RINEX using C++programs. After multiple sets of data comparison tests, the algorithm program shows that we designed can achieve a stable conversion of BINEX to RINEX with greater accuracy and speed.
Argus: Multi-Level Service Visibility Scoping for Internet-of-Things in Enterprise Environments

In IoT, what services from which nearby devices are available, must be discovered by a user's device (e.g., smartphone) before she can issue commands to access them. Service visibility scoping in large scale, heterogeneous enterprise environments has multiple unique features, e.g., proximity based interactions, differentiated visibility according to device natures and user attributes, frequent user churns thus revocation. They render existing solutions completely insufficient. We propose Argus, a distributed algorithm offering three-level, fine-grained visibility scoping in parallel: i) Level 1 public visibility where services are identically visible to everyone; ii) Level 2 differentiated visibility where service visibility depends on users' non-sensitive attributes; iii) Level 3 covert visibility where service visibility depends on users' sensitive attributes that are never explicitly disclosed. Extensive analysis and experiments show that: i) Argus is secure; ii) its Level 2 is 10x as scalable and computationally efficient as work using Attribute-based Encryption, Level 3 is 10x as efficient as work using Paring-based Cryptography; iii) it is fast and agile for satisfactory user experience, costing 0.25 s to discover 20 Level 1 devices, and 0.63 s for Level 2 or Level 3 devices.
Demystifying Computational Techniques Used to Diagnose Tuberculosis

Tuberculosis is one of the most deathful diseases in the entire world and remains a major reason for death worldwide. In 2017, around 10.0 million people infected with tuberculosis. For detecting disease in the medical field using the computational technique, MRCNN & UNET Model has achieved impressive accuracy across multiple datasets like brain tumor (Multimodal Brain Tumor Image Segmentation (BRATS 2015) datasets), glaucoma and other based on data collected. This Research is based on the detection of WBC (White Blood Cell) form the stained Microscopy image, by collecting past data form patients. In India, medical patient data is not stored anywhere systematically, we made extra effort to find hidden patterns from data. The article deeply discusses the various approaches to diagnose tuberculosis. It summarizes the advantages and disadvantages of the existing techniques and why deep learning technology use in the various medical diagnosis process. In this study, we propose a fully automatic method for WBC segmentation, which is developed using MRCNN based deep convolutional networks. Proposed technique was evaluated on a dataset that contains 2500 stained microscopy images are used to train the system and 540 images are used to test the system. We have achieved nearly 92% accuracy with a low false-positive type error.
A Method for Preprocessing State Data of Power Transmission and Transformation Equipment

Abstract In the era of big data, transportation and inspection data of power transmission and transformation equipment are characterized by diversity and richness. Massive data provide data support for state assessment of power transmission and transformation equipment, but at the same time, higher requirements are put forward for traditional data management and data quality model. How to clean the errors and invalid data in the transportation and inspection data, how to effectively repair the missing data, establish the data quality evaluation model, and improve the quality of the transportation and inspection data of the transmission and transformation equipment are of great significance to the equipment status evaluation.. For the acquired data, this paper carries out preliminary cleaning work on the data, and carries out preliminary cleaning on the data through time series analysis and other technologies to ensure the validity, consistency and integrity of the data.
A Collaborative Filtering Recommendation Method based on Knowledge Points and Learning Ability Evaluation Model

Personalized recommendation is an effective way for users to select useful information from massive information. Based on application background of online learning, this paper proposes a new idea that constructs user interest model with knowledge points and learning ability evaluation model by extracting users' mastery of knowledge as their characteristics, and also on basis of using the idea of collaborative filtering algorithm, this paper also recommends personal preference knowledge to users based on knowledge points and learning ability evaluation model. Experiment results show that the collaborative filtering recommendation method is effective; it also uses the proposed method for two courses recommendation in junior middle school as an application with good effects.
Traffic Optimization at the Application Level Proof of concept, development and usefulness evaluation of the ALTO solution

This paper presents an overview, proof of concept, and a preliminary demonstrative benchmarking study of the Application-Layer Traffic Optimization (ALTO) architecture proposed by the IETF ALTO Working Group. The main ALTO system purpose is to allow applications to get a more complete view of the underlying network infrastructure, allowing for wellreasoned connection decisions in situations of service redundancy. This paper first begins with a technical description of the ALTO project, and afterwards evaluates how P2P applications, guided by our proposed prototype implementation of the ALTO architecture, perform in comparison to traditional peer selection algorithms on the task of downloading a file in a typical file-sharing P2P network environment. The obtained results from our developed ALTO prototype system state that an application guided by the ALTO solution reduced overall network usage by around 40% with no significant impact in the application performance.
A New Handwritten Number Recognition Method Using HMM Based on MNIST

Abstract Taking MNIST data-set as research object, the HMM is introduced into the Handwritten Number Recognition for the first time. After the implementation of the classical HMM training algorithm, some optimization methods are proposed for the problems existing in the training. The general random initialization parameters lead to long training time and unstable data. The training of initialization parameters based on observations can speed up the training and avoid data overflow. The number of iterations in the training process is not positively related to the output probability. In order to obtain an optimal model, after the algorithm converges when the cross entropy loss function of the output probability of two adjacent iterations is the minimum, the training ends. Finally, a comparative experiment between the classical method and the optimization method is carried out in the two stages of training and recognition. In the training stage, compared with the classical method, the optimization method has the advantages of short average training time, high average output probability, fast iteration speed and high accuracy. In the test stage, the accuracy of the optimization method is higher than that of the classical method. The experimental results show that the HMM can be effectively applied in the field of Handwritten Number Recognition. And through the optimization method, to a certain extent, the recognition accuracy is improved. In a word, the method in this paper is an effective and feasible method.
Detecting the Burned Area in Volcanic Region by Using Multitemporal Landsat-8 OLI (Case Study: Mt. Sumbing, Central Java)

Abstract Biomass burning is one of the natural agents that most change terrestrial ecosystems and has a key ecological role in a huge piece of the Earth’s surface. Biomass burning has numerous socio-economic implications, particularly in developed countries where the developing urbanization of forested regions tends to increase accidents related with extraordinary fire occasions. Wildland fire mapping has been a subject vital for the international community over the recent two decades. Since fire is a major threat to forests and wooded areas in the tropical environment of Kalimantan, methodical regional fire observing is a need. Remote sensing methods are recognized as the efficient source of information for mapping burned areas from regional-national up to global scale. This study investigated the use of multitemporal Landsat-8 OLI (Operational Land Imager) data, to identify the burned area, in the tropical region of Indonesia, during 2019 fire season. A pair of Landsat-8 OLI, collected before and after fires, has been used to delineate the boundaries of sample location of burned area. Then, the difference of reflectance and Normalized Burn Ratio were analyzed. Fire incident causes landcover changes from vegetated land to bareland. This changes can affect the reflectance detected from Landsat-8 OLI. The result showed that biomass burning on volcanic region has caused high decreasing values on band 5 and there were high increasing on band 7. The NBR was better used to extracted burned area rather than NDVI or single spectral band. This method based on multitemporal optical data, creates a valuable tool for identifying and interpreting burned area following a fire event on volcanic region.
Entanglement Concentration Protocols for GHZ-type Entangled Coherent State Based on Linear Optics

We proposed two entanglement concentration protocols (ECPs) to obtain maximally entangled Greenberger-Horne-Zeilinger (GHZ)-type entangled coherent state (ECS) from the corresponding partially entangled GHZ-type ECSs. We obtained the first ECP using a partially entangled GHZ-type ECS assisted with a superposition of single-mode coherent state, however the second ECP is designed using two copies of partially entangled GHZ-type ECSs. The success probabilities have also been calculated and discussed for both the ECPs. We have further compared the success probabilities of our first ECP for 3-mode GHZ-type ECS with an ECP of 3-mode W-type ECS and found that our ECP is more efficient (maximal success probabilities) for larger value (\beta=0.7) of state parameter. For the physical realization, two optical circuits (for two ECPs) using linear optical elements, viz 50:50 beam splitter, phase shifter, and photon detectors are provided, which support the future experimental implementation possible with the present technology.
Application Research of Virtual Reality Technology in Design

Abstract With the improvement of people’s living standards, virtual reality technology has been applied to different industries, its own interactive feature, real-time feature and scene feature, which make it widely used in the field of architecture. This paper applies virtual reality technology to interior design, users can experience immersive-artistic conception in the virtual scene. We introduce the concept of virtual reality, and analyze the relationship between virtual reality technology and interior design; we study several main technologies of virtual reality, which include VRML modeling for installing interior virtual scene, using X3D technology for scene expansion of virtual interior design, Web technology is used to share virtual scenes. In the process of interior design, we put VRML technology into realizing the initialization of virtual scene, then adopt X3D technology to simulate the virtual scene, and proofread the generated virtual scene based on VRML technology, in order to realize online experience of the virtual scene, we display the effect of virtual interior design in the web environment, so that users can interact with the virtual interior design scene. By a large number of experiments, we prove that X3D technology can be used for displaying the virtual scene space in many dimensions, and the virtual environment has good maintainability; with the help of online-virtual reality technology, users can modify the scene of interior design, move the objects of scene, so that users can optimize the design of space according to their own wishes.
Multi-Tier Data Encryption Algorithm

Gentry, Sahai, and Waters created the first identity-based encryption (IBE) and lattice-based cryptography in CRYPTO 2013. This scheme is improved in this paper by exploiting the US National Security Agency's (NSA) hashing function. POSIX is used to create a small and portable algorithm to assimilate multiple keys under a single identity-key that enables protection under different identities to be evaluated as one. The encryption renders the original file transformed by splitting it and encrypting the resulting files before encrypting the combination as a whole. Focus has been converged on modulating and modifying available encryption techniques to be processed as multiple segments of a single algorithm for more robust and powerful application. Both, the passkey as well as the data is encrypted using sets of pre-defined encryption techniques and the algorithm is processed via legacy libraries to be more versatile and provide an easy to implement scheme. The algorithm is developed to be applied in the instances of standalone dataset or in cases of virtualization where the algorithm can be inherited by the subsystems as modules to be used for security purposes.
Large-Scale Instance Selection Using a Heterogeneous Value Difference Matrix

Data classification of a large-scale dataset is a common problem nowadays because the classifier model takes an overwhelming amount of time to completely learn all the data. The instance selection algorithm is a well-known technique that addresses this issue by reducing the size of the training set. Instance selection methods decrease the difficulty of data classification and improve the quality of the training data. This paper proposed a novel instance selection method using a heterogeneous value difference matrix (HVDM) distance function. The proposed method selected a set of median HVDM values in each partition as a reduced training set. We compared the proposed method with the condensed nearest neighbor (CNN) and instance-based learning (IB3) methods. Five large-scale datasets from the UCI data repository were tested with three classifier models (decision tree, neural net, and support vector machine). The accuracy and kappa of the proposed method were better than those of the other two methods, and the proposed method had a moderate reduction rate. However, the accuracy and kappa of the proposed method were nearly equal to those of the original training set.
Diagnosis of colorectal cancer based on imperialist competitive algorithm

The Colorectal cancer leads to more number of death in recent years. The diagnosis of Colorectal cancer as early is safe to treat the patient. To identify and treat this type of cancer, Colonoscopy is applied commonly. The feature selection based methods are proposed which helps to choose the subset variables and to attain better prediction. An Imperialist Competitive Algorithm (ICA) is proposed which helps to select features in identification of colon cancer and its treatment. Also K-Nearest Neighbor (KNN) classifier is used to retain a minimal Euclidean distance between the feature of query vector and all the data in the nature of prototype training. Experimental results have proved that the proposed method is superior when compared to other methods in its metrics of performance. Better accuracy is achieved by the proposed method.
Kernelized Correlation Filter with Scale Estimation and Feedback Mechanism for Visual Tracking

The previous target tracking algorithms based on correlation filters have excellent tracking performance. However, when encountering some challenging problems such as fast motion, occlusion, scale variations, motion blur, etc., tracking drift or even tracking failure occurs during the tracking process. Aiming at the above problems, we propose a novel tracking method. On the basis of kernelized correlation filter, a scale adaptive filter is added to adapt to the scale variations of the target during the tracking process. In addition, a feedback mechanism using the average peak-to-correlation energy (APCE) as the judgment criterion is introduced to enable the model to be updated under the premise of high-confidence and avoid tracking model corruption. Experimental results show that our algorithm performs better than traditional correlation filtering algorithms on challenging sequences.
ERF: An Empirical Recommender Framework for Ascertaining Appropriate Learning Materials from Stack Overflow Discussions

Computer programmers require various instructive information during coding and development. Such information is dispersed in different sources like language documentation, wikis, and forums. As an information exchange platform, programmers broadly utilize Stack Overflow, a Web-based Question Answering site. In this paper, we propose a recommender system which uses a supervised machine learning approach to investigate Stack Overflow posts to present instructive information for the programmers. This might be helpful for the programmers to solve programming problems that they confront with in their daily life. We analyzed posts related to two most popular programming languages—Python and PHP. We performed a few trials and found that the supervised approach could effectively manifold valuable information from our corpus. We validated the performance of our system from human perception which showed an accuracy of 71%. We also presented an interactive interface for the users that satisfied the users’ query with the matching sentences with most instructive information.