Reviewerly/exHarmony huggingface dataset:
Secure Multiparty Computation and Privacy Preserving scheme using Homomorphic Elliptic Curve Cryptography

In this day and age of cutting edge innovation, it's not sheltered to share the private or classified information over the system .Cryptography is one of the methods in data security through which we are shield our private information from obscure individual .In this paper we center around the elliptic bend cryptography based methodology for secure multiparty calculation .Classical way to deal with SMC is to perform calculation utilizing confided in outsider. be that as it may, when we talk about commonsense circumstance TTPs are difficult to accomplish and it is basic to kill TTP in SMC .moreover ,existing arrangement proposed for SMC utilize traditional homomorphic encryption plan, for example, RSA and Paillier. Yet, such cryptosystem brought about greater expense ,So that resultant SMC conventions are not adaptable .We have proposed ECC based homomorphic encryption conspire for SMC, in light of execution measurements, for example, computational expense and correspondence cost . We prescribe an effective calculation among a few chose one, that offers security and diminishes costing and can be connected in numerous protection requesting application.
Improved Framework for Breast Cancer Prediction Using Frequent Itemsets Mining for Attributes Filtering

Data Mining is applicable for pulling up some new information by analyzing the database. It is also used for prediction based on real and actual current data. Breast cancer is a very harmful disease which effects badly to ones social, physical life and also effects mentally. This paper focuses on the attribute filtering techniques i.e frequent itemsets mining with the intention to find the essential and relevant attribute from the Wisconsin breast cancer dataset and classification algorithmic program like SVM, Naive Bayes, k-NN, Decision Tree comparison is done with attribute filtering. SVM produces beat result among all the classifier with attribute filtering.
AsmDB: Understanding and Mitigating Front-End Stalls in Warehouse-Scale Computers

It is well known that the datacenters hosting today's cloud services waste a significant number of cycles on front-end stalls. However, prior work has provided little insights about the source of these front-end stalls and how to address them. This work analyzes the cause of instruction cache misses at a fleet-wide scale and proposes a new compiler-driven software code prefetching strategy to reduce instruction caches misses by 90%.
Facial Recognition And Verification System For Accessing Patient Health Records

This paper proposes an Image processing based embedded system to access patient health records by face recognition. Nowadays carrying all the medical report during every visit became a tedious process and hence the medical reports are stored in a database and accessed with a unique identification number. In this paper, face recognition is used instead of a unique identification number. In emergency situations, this helps to know the medical history of the patients. The proposed system consists of a Raspberry Pi 3 processor and a webcam which is used to capture the face. Local Binary Pattern(LBP) and HAAR Cascade algorithms are used to detect and recognize the face. The features of the face are extracted using LBP and HAAR cascade algorithm. The extracted features are given to classifier which compares these features with the trained ones and displays the data and reports stored in the database. Both algorithms are implemented in python using OpenCV. Experimental results indicate that LBP algorithm is more efficient than HAAR cascade classifier algorithm in terms of execution time, classification accuracy, confusion matrix, and F1 score.
Classical Equivalent Quantum Unsupervised Learning Algorithms

The paper presents necessity of data pre-processing to process the data set through machine learning algorithms, and it filters the raw data to make the data as in compatible format for analysis purpose. The methods such as mean normalization, feature scaling and dimensionality reduction are used for data smoothing and therefore formatted data set gets processed by learning algorithms to predict best possible outcome as per the analysis. Machine learning became famous for such processing purpose and through unsupervised learning we are capable of processing over various data formats. Such algorithms performance is up-to the mark, but as per the availability of inherent parallel processing through quantum machines, computational speedup can be achieved by designing classical equivalent quantum machine learning algorithms. In this paper, we discussed some of the classical unsupervised learning algorithms, and then we propose the equivalent quantum version of algorithms along with the mathematical justification over the complexity analysis and achieved computational speedup and show the betterment by processing such problems over quantum machines.
Electrical Impedance Of Breast’s Tissue Classification By Using Bootstrap Aggregating

This paper presents on the classification of the electrical impedance of tissue from the breast. To analyze the risk of breast cancer by using techniques Bootstrap Aggregating due to cancer. The motivation of this work is only one important reason of a major public health problem and a common cancers cause of death. They are vigilant in the detection and treatment of breast cancer to be diagnosed in the early stages of breast cancer and treatment prior to the spread of the disease away to get a better therapeutic effect. It developed a method for detecting the operating room, including a variety of tools to detect, with a special understanding of the pathological increase. To make the guidelines for the treatment of breast cancer today have changed enough. The widgets work on this proposed new algorithm. A data set from UCI relating to the identification of the electrical impedance of the tissue box is cut from the breast with effective techniques. Bootstrap Aggregating is tested with 10-folds cross validation with 106 objects, the result is that the accuracy of 74.47%.
Design and Implementation of Intelligent Medical Customer Service Robot Based on Deep Learning

With the progress of society, China pays special attention to the service quality of hospitals and the upgrading of medical services. In order to meet the medical needs of different patients, and provide patients with more convenient guidance and triage services, it is especially important to create an unmanned customer-service robot which can introduce a guideline for patients to be diagnosed.Based on the important achievements and data accumulated by West China Second University Hospital (Sichuan University, China) in the information construction, this paper uses the scene classification model, the improved deep semantic matching model and the generated business model to create a smart and efficient medical customer-service robot for the hospital. The customer-service robot can fully consider the context, provide patients with accurate answers and intelligent services in all-weather conditions, so that every problem raised by patients can be answered in a timely and effective manner, and human-machine dialogue accuracy and patient experience can be greatly improved.
Mona Lisa's emoji: digital civilization and its discontents

Emojis are presently thus customary in digital communication that they have become part of the communicative habits of most users of social networks. Representing an attitude of hilarity towards a certain statement by ending its verbal transcription with an emoji "smiling to tears" is not the same as smiling or laughing to tears when uttering the same content as an oral statement. The major structural difference between an emoji in digital conversation and a facial expression in an oral, face-to-face dialogue comes down to what could be labeled a "semiotics of the interval." In mainstream face-to-face conversation, the face is a fundamental interface of social interaction. It can be physiologically controlled in order to guide its communicative effect on the interlocutor; yet, the face is never an entirely semiotic surface. Moreover, whereas the face must continuously react impromptu to external solicitations, emojis are a typical expression of mastered, differed communication. They are supposed to bestow on digital dialogue an illusion of immediacy, yet they inevitably turn into devices of mediation, resulting from the structural possibility of chiseling a message before sending it out to the interlocutor. The article reflects on this intrinsic difference so as to develop a more general consideration of the cultural reasons and effects of the disappearing of the body from communication and its replacement through digital simulacra.
Future of bitcoin in India: Issues and challenges

Purpose - The main purpose of the study is to classify the different barriers in adoptions of Bitcoins and provide the appropriate elucidations for the problems faced by Bitcoin users in India.Design/ methodology/ approach - The study reviews researches on the adoption of Bitcoin as a medium of transactions and investments. A review of literature has been carried out to explore various existing Bitcoin. A case study has been conducted on Bitcoin and its adoption by individuals in different countries. AHP has been used to propose the model and for insights.Originality/value - Bitcoin adoption is still an unexplored area of research. This research has attempted to explore the root causes of the non-adoption of Bitcoin in different countries.
Comparison of Video Face Detection methods Using HSV, HSL and HSI Color Spaces

this paper presents, new face detection methods based on HSL and HSI color spaces are presented. A comparison of the new face detection methods and a new HSV skin color range is presented. The three color spaces are based on: H, S, V, L, and I, whose represent Hue, Saturation, Value, Luminance and Intensity respectively. YouTube Celebrities Face Tracking and Recognition Dataset is used. It contains 1910 sequences of 47 subjects. All dataset videos are encoded in MPEG4 at 25fps rate. The proposed methods based on two main steps, at the beginning, the skin like regions is detected by the gradient values of the proposed color space. According to main facial features, such as eyes, mouth and nose the desired faces are determined from the recommended regions. According to experimental results, HSV color space gives good results in lighten_faces, HSL color space gives good results for multi_faces and HSI color space gives good results for single_faces and zoomed_faces videos.
Generative Model for NLP Applications based on Component Extraction

People all around the world speak so many different languages, but a Computer System or any other Computerized Machine only understands a single language i.e. binary language (1s and 0s) This system or a process that converts human language to computer understandable language is known as Natural Language Processing (NLP), though various diversified models have suggested so far, yet the need for a generative predictive model which can optimize depending upon the nature of problem being addressed is still an area of research under work. The paper presents a Generative Model for NLP Applications based on significant components extracted from Case Studies. The generative model is a single platform for diversified areas of NLP that can address specific problems relating to read text, hear speech, interpret it, measure sentiment and determine which parts are important. This is achieved by process of elimination once the relevant components are identified. Single platform provides same model generating and reproducing optimized solutions and addressing different issues.
Aggregatable Certificateless Designated Verifier Signature

In recent years, the Internet of Things (IoT) devices have become increasingly deployed in many industries and generated a large amount of data that needs to be processed in a timely and efficient manner.Using aggregate signatures, it provides a secure and efficient way to handle large numbers of digital signatures with the same message.Recently, the privacy issue has been concerned about the topic of data sharing on the cloud.To provide the integrity, authenticity, authority, and privacy on the data sharing in the cloud storage, the notion of an aggregatable certificateless designated verifier signature scheme (ACLDVS) was proposed.ACLDVS also is a perfect tool to enable efficient privacy-preserving authentication systems for IoT and or the vehicular ad hoc networks (VANET).Our concrete scheme was proved to be secured underling of the Computational Diffie-Hellman assumption.Compared to other related schemes, our scheme is efficient, and the signature size is considerably short.
An automated and continuous method for the optical measurement of wood pellet size distribution and the gravimetric determination of fines

This paper presents an automated, continuous method to determine wood pellet size distributions of larger and, hence, representative samples based on 2D-digital image analysis. The analysis routine developed combines several image processing steps to detect and separate the respective particles. The test unit designed allows length determination of up to 140 kg of pellets with a rate of 10.2 kg/h without any manual intervention. Pellets with a diameter up to 10 mm and a maximum length of about 50 mm can be analysed. In addition, the amount of fines is determined gravimetrically. Verification tests confirm an average standard deviation of 0.42% for the effective pellet length. First measurements of various pellet types show significant differences in their initial size distributions. The method developed provides a tool, which allows a more precise quality control of wood pellet size distribution.
Asynchronous variance-reduced block schemes for composite non-convex stochastic optimization: block-specific steplengths and adapted batch-sizes

This work considers the minimization of a sum of an expectation-valued coordinate-wise smooth nonconvex function and a nonsmooth block-separable convex regularizer. We propose an asynchronous variance-reduced algorithm, where in each iteration, a single block is randomly chosen to update its estimates by a proximal variable sample-size stochastic gradient scheme, while the remaining blocks are kept invariant. Notably, each block employs a steplength relying on its block-specific Lipschitz constant while batch-sizes are updated as a function of the number of times that block is selected. We show that every limit point is a stationary point and establish the ergodic non-asymptotic rate O(1/K). Iteration and oracle complexity to obtain an ε-stationary point are shown to be O(1/ϵ) and O(1/ϵ2), respectively. Furthermore, under a proximal Polyak–Łojasiewicz condition with batch sizes increasing at a geometric rate, we prove that the suboptimality diminishes at a geometric rate, the optimal deterministic rate while iteration and oracle complexity to obtain an ε-optimal solution are O(ln⁡(1/ϵ)) and O(1/ϵ)1+c with c≥ 0. In the single block setting, we obtain the optimal oracle complexity O(1/ϵ). Finally, preliminary numerics suggest that the schemes compare well with competitors reliant on global Lipschitz constants.
Recommendation system using a deep learning and graph analysis approach

Abstract When a user connects to the Internet to fulfill his needs, he often encounters a huge amount of related information. Recommender systems are the techniques for massively filtering information and offering the items that users find them satisfying and interesting. The advances in machine learning methods, especially deep learning, have led to great achievements in recommender systems, although these systems still suffer from challenges such as cold‐start and sparsity problems. To solve these problems, context information such as user communication network is usually used. In this article, we have proposed a novel recommendation method based on matrix factorization and graph analysis methods, namely Louvain for community detection and HITS for finding the most important node within the trust network. In addition, we leverage deep autoencoders to initialize users and items latent factors, and the Node2vec deep embedding method gathers users' latent factors from the user trust graph. The proposed method is implemented on Ciao and Epinions standard datasets. The experimental results and comparisons demonstrate that the proposed approach is superior to the existing state‐of‐the‐art recommendation methods. Our approach outperforms other comparative methods and achieves great improvements, that is, 15.56% RMSE improvement for Epinions and 18.41% RMSE improvement for Ciao.
Co-Clustering for Object by Variable Data Matrices

Co-clustering means the simultaneous clustering of the rows and columns of a two-dimensional data table (biclustering, two-way clustering), in contrast to separately clustering the rows and the columns. Practical applications may be met, e.g., in economics, social sciences, bioinformatics, etc. Various co-clustering models, criteria, and algorithms have been proposed that differ with respect to the considered data types (real-valued, integers, binary data, contingency tables), and also the meaning of rows and columns (samples, variables, factors, time,...). This paper concentrates on the case where rows correspond to (independent) samples or objects, and columns to (typically dependent) variables. We emphasize that here, in general, different similarity or homogeneity concepts must be used for rows and columns. We propose two probabilistic co-clustering approaches: a situation where clusters of objects and of variables refer to two different distribution parameters, and a situation where clusters of ‘highly correlated’ variables (by regression to a latent class-specific factor) are crossed with object clusters that are distinguished by additive effects only. We emphasize here the classical ‘classification approach’, where maximum likelihood criteria are optimized by generalized alternating k-means type algorithms.
Practical Time-Release Blockchain

Time-release cryptography is a special encryption technique that allows a message to be hidden for some time. The previous schemes have shortcomings in that the encryptor should predict the decryptor’s computing power precisely or the trusted agent should be always available. In this paper, we propose a new, practical time-release blockchain, and find the key to decrypt the content after a certain time. In order to verify the effectiveness of the blockchain system automatically, which uses the proof-of-work (PoW) and the consensus algorithm in the the proposed technique, we have implemented a prototype version of our blockchain system using Python. The proposed method has the following advantages. First, the decryption time is automatically adjusted, even if the miner’s computing power changes over time. Second, unlike previous time-lock puzzle schemes, our algorithm does not require additional computation work for solving the puzzle. Third, our scheme does not need any trusted agents (third parties). Fourth, the proposed method uses standard cryptographic algorithms.
Statistical and Time Series Analysis of Accelerometer Signals for Human Activity Recognition

Sensor-based human activity recognition HAR has become increasingly more important in our daily lives for a number of reasons. Advances in the sensing capabilities of personal devices have seen unprecedented growth over the past decade. HAR systems have many applications especially in health monitoring, intelligent environments, and smart spaces. Wearable sensors are particularly suited in these areas. This is due to the fact that they have small size, their cost has been steadily decreasing, and they are currently embedded in almost all commodity mobile devices such as smart phones, smart watches, sensory gloves, hand straps, and shoes. In this paper we focus on analyzing sensory accelerometer data collected from wearable devices. And in particular, we study activities of daily living (ADL) which are the activities ordinary people have the ability for doing on a daily basis like eating, moving, individual hygiene, and dressing. To the best of our knowledge most HAR systems are based on supervised machine learning techniques and algorithms, In this paper we widens the scope of techniques that can be used for the automatic analysis of human activities and provide a valuation of the relative effectiveness and efficiency of a potentially myriad pool of techniques. Specifically, we apply two approaches. The first approach is time-aware treating the incoming data in its natural form as a sequential temporal sequence of measurements. The techniques we used are based on time series analysis. The other approach is time-neglectful. It is based on using statistical methods based on goodness-of-fit tests. Our comparative assessment shows that the latter approach has some potential in classification accuracy, though needs further investigation. The time-aware approach gives much better results, though the computational resources required can be prohibitive, so also needs further investigation from that perspective.
Pixel Indicator Steganography Technique with Enhanced Capacity for RGB Images

In the modern era, a lot of information is transferred over the internet. This information has to be transferred securely to retain privacy. Many techniques have been developed so that this information is transferred securely without being intercepted by the third party. Steganography is also such techniques in which information can be transferred securely. In steganography, the existence of secure information is completely hidden from the third party. Only sender and receiver know that about the secret information being transferred. In steganography, information is embedded into some cover medium like text, image, audio or video etc. In image steganography, the image is used as a cover medium to hide the secret data. Most common technique for image steganography involves least significant bits (LSB) of image pixels which are altered with the secret message bits. There are a lot of limitations in the LSB techniques, due to which many new image steganography techniques are developed. Pixel Indicator Technique is such a technique. In this paper, an altered version of the PIT is presented in which advantage of 24 bits of each pixel is taken to hide the data in three data channels of RGB. In this method, no separate key is used for encryption and decryption, thus avoiding any overhead of managing key. This technique provides enhanced capacity and security over any other image steganography techniques.
Shadow-Cuts Minimization/Maximization and Complex Hopfield Neural Networks

In this article, we continue our very recent work by extending it to the complex case. Having been inspired by the real Hopfield neural network (HNN) results, our investigations here yield various novel results, some of which are as follows. First, extending the “biased pseudo-cut” concept to the complex HNN (CHNN) case, we introduce a “shadow-cut” that is defined as the sum of intercluster phased edges. Second, while the discrete-time real HNN strictly minimizes the “biased pseudo-cut” in each neuron state change, the CHNN “tends” to minimize the shadow-cut (as the CHNN energy function is minimized). Third, these definitions pose a novel L-phased graph clustering (partitioning) problem in which the sum of the shadow-cuts is minimized (or maximized) for the Hermitian complex and the directed graphs whose edges are (possibly arbitrary positive/negative) complex numbers. Finally, combining the CHNN and the pioneering algorithm GADIA of Babadi and Tarokh and their modified versions, we propose simple indirect algorithms to solve the defined shadow-cuts minimization/maximization problem. The proposed algorithms naturally include the CHNN as well as the GADIA as its special cases. The computer simulations confirm the findings.