Reviewerly/exHarmony huggingface dataset:
An Effective Leukemia Prediction Technique Using Supervised Machine Learning Classification Algorithm

Leukemia is not only fatal in nature, the treatment is also extremely expensive. Leukemia's second stage (typically there are four stages) is enough to blow a large hole in a family's savings. In this paper, we have designed a supervised machine learning model that accurately predicts the possibility of Leukemia at an early stage. We mainly focus on regular symptoms and the probabilities of a subject to develop Leukemia later on. The parameters or features are usually information available at regular checkups. Firstly, we have defined 17 parameters in consultation with the specialist doctors and then we have collected primary data through surveys of different Leukemia and Non Leukemia patients from hospitals. We have divided the data into train and test datasets and applied different machine learning algorithms such as Decision Tree, Random Forest, KNN, Linear Regression, Adaboost, Naive Bayesian, etc. to find out the accuracy. We obtained 98% of accuracy using Decision Tree and Random Forest, 97.21% using KNN, 91.24% using Logistic Regression, 94.24% using Adaboost, and 75.03% using Naive Bayesian, respectively. It is observed that the Decision Tree and the Random Forest classifier outperform the rest.
Coordinated Landmark-based Routing for Blockchain Offline Channels

Blockchain offline channels such as the Bitcoin Lightning Network will improve the scalability of blockchains by reducing the number of transactions needed to be recorded in the blockchain. Uncoordinated payment transfer in channel networks including path-based fund transfer may overuse few channels. This will lead to imbalanced channel networks where the channel balance of few channels is too low to remain operational. In this paper, we propose a coordination method for a landmark-based routing algorithm for fund transfer in offline channels. Our procedure allows the landmarks to route funds in complementary and non-overlapping paths which balances channel values in a bi-directional channel network. Using experimental evaluation with Bitcoin Lightning network data we prove that the proposed coordinated landmark-based routing algorithm keeps a better balance of the channels and significantly improves the success rate of fund transfer compared with existing landmark-based routing algorithms.
The Role of Cognition in Radar Sensing

The paper is aimed at discussing the role of cognition in radar sensing providing a system level point of view. In this respect some fundamental issues are considered involving transmitter, receiver, signal and data processing units. Firstly, the definition of the cognitive radar concept is provided and its key aspects are pinpointed. Secondly, the role played by the perception-action cycle and the awareness gleaned by dynamic databases and/or sensing networks is emphasized. Hence the application of the cognitive paradigm for radar operation in spectrally crowded environments is discussed and an example with the use of real hardware is shown. Finally a glimpse on the role of cognition in agile tracking systems is given.
Sentiment Analysis of Amazon Product Reviews

Sentiment analysis allows computer programs to identify subjective information from a document with the help of text analysis, computational linguistics, and machine learning methods. Sentiment analysis models are trained to detect polarity within a text (e.g. positive or negative opinion), whether it's a whole document, paragraph, or a single sentence. In this research, both traditional machine learning methods, such as Naïve Bayes analysis and Support Vector Machines, as well as deep learning models such as Multilayer Perceptron (MLP) and Recurrent Neural Network (RNN) are built, tested and compared in terms of their effectiveness to detect the correlation between Amazon product reviews and the star rating given by customers. By comparing these models, we gain a comprehensive understanding of the role they can play in sentiment analysis. This understanding can be used to better understand people's feelings when making business decision in applications like e-commerce.
Minimizing the operation cost of distributed green data centers with energy storage under carbon capping

The expensive cost and intermittent availability of renewable energy bring great challenges to its efficient utilization in green data centers. In this paper, we propose a new way to achieve an explicit trade-off between operational cost and carbon emission by dynamic storing off-site renewable energy in distributed data centers. We first formulate a constrained stochastic optimization problem for cost minimization of data centers. Then, by leveraging Lyapunov optimization theory, we design an online Carbon Capped Cost Minimization algorithm (CCCM) to achieve a near-optimal cost with rigorous mathematical proof. Specially, the decisions at each time slot are determined with an efficient iterative algorithm based on the Generalized Benders Decomposition (GBD) technique. Finally, extensive simulations are conducted to show the effectiveness of our algorithm. The results show that our algorithm can save about 6% total costs compared with the algorithm without offsite energy storage.
A secure quantum multi-party ranking protocol based on continuous variables

A secure quantum multi-party ranking protocol based on continuous variables is proposed. We first propose a secure quantum three-party ranking protocol based the continuous-variable GHZ state, and then extend the protocol to a secure quantum multi-party ranking protocol. The trusted center (TC) can compute the order of the secret input while not knowing the secret input and order of the parties, and each party only knows the order of his own input but doesn’t know the other parties’ inputs and orders. Finally, the security analysis results show that it satisfies the basis security requirement of a quantum secure ranking protocol, such as correctness, anonymity, untraceability and security, where the security requirement of anonymity, untraceability and security are analyzed from two aspects: the outsider attack and the participants attack.
Game-theoretic Modeling and Stability Analysis of Blockchain Channels

The emergence of channel technology reduces the transaction verification time of blockchains. A channel with a stable state is helpful for completing transactions successfully. Under the assumption of node bounded rationality and replication dynamics of an evolutionary process, this paper presents a dynamic evolutionary game model based on node behaviors in blockchain channels. The model considers the cost of attack, attack success rate, defense, cooperation, and noncooperation strategies during the game process. The defense strategy can help nodes resist different attacks. Nodes can dynamically adjust their own strategies according to different behaviors of attackers to achieve effective defense. The experimental results show that the proposed method is better than a lightning network channel in terms of transaction success ratio.
Automatic Calibration of a Two-Axis Rotary Table for 3D Scanning Purposes

Rotary tables are often used to speed up the acquisition time during the 3D scanning of complex geometries. In order to avoid manual registration of the point clouds acquired with different orientations, automatic algorithms to compensate the rotation were developed. Alternatively, a proper calibration of the rotary axis with respect to the camera system is needed. Several methods are available in the literature, but they only consider a single-axis calibration. In this paper, a method for the simultaneous calibration of both axes of the table is proposed. A checkerboard is attached to the table, and several images with different poses are acquired. An optimization algorithm is then setup to determine the orientation and the locations of the two axes. A metric to assess the calibration quality was also defined by computing the average mean reprojection error. This metric is used to investigate the optimal number and distribution of the calibration poses, demonstrating that the optimum calibration results are achieved when a wider dispersion of the calibration poses is adopted.
Extracurricular activities of students - a means of forming professional motivation

The article analyzes the specificities of future teachers` extracurricular activities as a means of forming their professional motivation. The author proves that extracurricular work can be considered as a factor of generating students` primary interest to the future occupation. The variety and complexity of extracurricular activities allow to reveal the diversity of the world, the job, human relationships and lead to increased motivation for getting professional pedagogical knowledge.
NTRU and RSA Cryptosystems for Data Security in IoT Environment

The massive technological revolution that has taken place and that is going on at a massive rate, has led to zillions of digital information traveling in a communication network. Information traveling in the network needs security and privacy. There are various cryptographic algorithms available today that can secure messages and data being communicated. Symmetric cryptography, though simple, does not provide authorization efficiently. Asymmetric cryptography proves to be more secure as it provides all the components of information security.With the breakthrough development on the Internet-of- Things (IOT), any digital device can become an originator of data. The main concern which arises is how to protect these data. With an accelerated usage of IoT environment, the security of data collected and stored by these devices has become more crucial.In this paper, we discuss two asymmetric algorithms NTRU (Nth degree truncated polynomial ring) and RSA (Rivest- Shamir-Adleman) in context of IoT. RSA is widely used but is heavy on key size and computation complexity. On the other hand, NTRU can give the same level of bit security with comparative key size and very less computational complexity. The captivity of NTRU algorithm lies in the fact that it works on simple polynomial operations. It is a NP-hard problem based on shortest vector problem (SVP) and is secure even for future quantum attacks. In a resource constrained environment like IoT environment, the performance of RSA and NTRU has been analyzed on the various security level parameters, viz. key generation, encryption and decryption time. Furthermore, performance of the various parameters has also been analyzed on various bits size plain text.
Improving visible-thermal ReID with structural common space embedding and part models

With the emergence of large-scale datasets and deep learning systems, person re-identification(Re-ID) has made many significant breakthroughs. Meanwhile, Visible-Thermal person re-identification(V-T Re-ID) between visible and thermal images has also received ever-increasing attention. However, most of typical visible-visible person re-identification(V-V Re-ID) algorithms are difficult to be directly applied to the task of V-T Re-ID, due to the large cross-modality intra-class and inter-class variation. In this paper, we build an end-to-end dual-path spatial-structure-preserving common space network to transfer some V-V Re-ID methods to V-T Re-ID domain effectively. The framework mainly consists of two parts: a modility specific feature embedding network and a common feature space. Benefiting from the common space, our framework can abstract attentive common information by learning local feature representations for V-T Re-ID. We conduct extensive experiments on the publicly available RGB-IR re-ID benchmark datasets, SYSUMM01 and RegDB, for demonstration of the effectiveness of bridging the gap between V-V Re-ID and V-T Re-ID. Experimental results achieves the state-of-the-art performance.
CNN-LSTM Based Approach for Parameter Estimation of K-Clutter Plus Noise

This paper concerns the problem of estimating the parameters of the K plus noise distribution. In a previous work, it has been shown that, in the multilook scenario, the modified fractional order moment estimator (MFOME) has about the same estimation accuracy as the [zlog(z)] method, but lower computational complexity. However, significant estimation errors have been observed in the single look scenario, low sample size, and large values of the K-distribution shape parameter. Moreover, the computational complexity of the [zlog(z)] estimator discourages its implementation in practical applications. The aim of this work is to estimate the shape parameter of the K-distribution with reduced computational complexity. The problem can be formulated as a supervised many-to-one sequence prediction. We propose here a hybrid model including convolutional and long-short-term-memory (LSTM) neural networks (NN). Estimation performance is investigated by processing both simulated and real clutter data.
Research on classification and identification of library based on artificial intelligence

With the popularization of open borrowing, it is urgent for major libraries to explore new collection classification and identification systems to assist or replace librarians in their daily book cataloguing work. The messy books can be marked according to invisible colors, which puts forward a new method of library classification. In the algorithm used to detect image edges, by looking for the traditional sensitivity measurement system, the best parameters are applied to the wrong book image edges, and the test results are verified to find the best parameter range. A two-dimensional algorithm is used to segment the information element matrix to reduce sound. The results show that the image edge test is effective and reduces the influence of noise on the image test. The self-adjustment control algorithm of a single neuron is used to control the robot orbit, which makes the robot not only react quickly, but also reduce a small part of the error in meeting the actual needs of the robot. The effect on the result is getting smaller and smaller.
Application of Machine Learning in Air Hockey Interactive Control System

In recent years, chip design technology and AI (artificial intelligence) have made significant progress. This forces all of fields to investigate how to increase the competitiveness of products with machine learning technology. In this work, we mainly use deep learning coupled with motor control to realize the real-time interactive system of air hockey, and to verify the feasibility of machine learning in the real-time interactive system. In particular, we use the convolutional neural network YOLO (“you only look once”) to capture the hockey current position. At the same time, the law of reflection and neural networking are applied to predict the end position of the puck Based on the predicted location, the system will control the stepping motor to move the linear slide to realize the real-time interactive air hockey system. Finally, we discuss and verify the accuracy of the prediction of the puck end position and improve the system response time to meet the system requirements.
Dynamic speckle deflectometry based on backward digital image correlation

Traditional phase measuring deflectometry locates the screen source point (SSP) using the sinusoidal fringe based phase extraction and unwrapping methods, which cannot realize dynamic measurement. And the digital image correlation based deflectometry needs at least 2 shots for surface reconstruction. We propose a single-shot dynamic speckle deflectometry (DSD) that locates the SSP by directly matching the captured single-shot speckle pattern with a computer synthesized speckle pattern. The theoretical basis is detailed. Numerical simulation is conducted to evaluate the validity and robustness of DSD with comparison to the fringe based approaches. The accuracy performance of DSD is quantified by measuring a glass piece, the surface result differs from that of interferometer 46.0 nm in root mean square. The dynamic motion of a wafer deformed by squeezing force is recorded by DSD, the squeezing force brings a shape deformation mainly in the form of astigmatism to the wafer surface.
A Reinforcement Learning Approach to Design Verification Strategies of Engineered Systems

System verification is a critical process in the development of engineered systems. Engineers gain confidence in the correct functionality of the system before it is deployed into operation by executing verification activities. Choosing the right set of verification activities at the right system development stage, that is, designing a verification strategy (VS), is essential to balancing information discovery and verification cost. Only recently, quantitative methods have been proposed to support the design of verification strategies. However, their applicability in real-life scenarios is impractical due to their limited computational efficiency in the high dimensional solution space of the VS selection problem. This paper presents a reinforcement learning (RL) approach to search for a near-optimal VS. Specifically, the VS design problem is formulated as a Markov decision process (MDP) in which a value function is required. Then we combine tree search and a neural network (NN) to design a RL algorithm. In the RL algorithm, the value function is approximated as a NN that is trained in an iterative way. The near-optimal VS can be generated from the trained NN. A case study is presented to show the superiority of the proposed method.
Wearable Glove Based Approach for Human-UAV Interaction

With the developing technology in recent years, studies towards increasing UAV (Unmanned Aerial Vehicles) usage and meeting the needs in every field are rapidly increasing. Research and development studies on UAV are increasing and Human-UAV interaction is an important factor in terms of being used in many new areas. In this study, wearable smart gloves are designed for human-UAV interaction. Hand motion recognition system is created by using cheap circuit elements such as Flex sensors, imu sensor and Arduino uno in glove design. Thanks to the developed application framework, it has made it easier to determine which axis-oriented movement of the values that will correspond to the movements and to detect the signals against the movements. Standard deviation is applied to the read signal values for motion detection. When the studies conducted in the literature are examined, the biggest plus of our study is that this application, which we realize in real time, provides rapid return thanks to our simple mathematical calculation method. These motion signals obtained are used under the control of the unmanned aerial vehicle we designed in the Unity program. The data obtained from the sensors and the motion data obtained as a result of calculations with the framework we developed were sent directly to the UAV simulation and the correct controller was provided.
Intelligent English writing system based on fusion of herding effect and artificial intelligence

The use of intelligent college English writing training system will certainly promote the traditional teaching structure and realize a new and efficient English writing teaching mode. On the basis of machine learning and the herd effect algorithm, this article constructs an artificial intelligence-based English intelligent writing system. Moreover, in view of the shortcomings of traditional models and the characteristics of intelligent English writing, this paper proposes an improved algorithm for optimization of swarm particle walking paths. In addition, this article proposes a relative attractiveness to initialize the formation of small-scale groups based on the herd effect. Then, in the process of intelligent writing, by establishing an information sharing mechanism between groups, each group is continuously updated and reorganized according to the relative attractiveness of the group, so that the writing process can be simulated more realistically. From the experimental research, it can be seen that the model constructed in this paper has a certain degree of intelligence.
Identity-Based Unidirectional Proxy Re-encryption in Standard Model: A Lattice-Based Construction

Proxy re-encryption (PRE) securely enables the re-encryption of ciphertexts from one key to another, without relying on trusted parties, i.e., it offers delegation of decryption rights. PRE allows a semi-trusted third party termed as a "proxy" to securely divert encrypted files of user A (delegator) to user B (delegatee) without revealing any information about the underlying files to the proxy. To eliminate the necessity of having a costly certificate verification process, Green and Ateniese introduced an identity-based PRE (IB-PRE). The potential applicability of IB-PRE leads to intensive research from its first instantiation. Unfortunately, till today, there is no unidirectional IB-PRE secure in the standard model, which can withstand quantum attack. In this paper, we provide, for the first time, a concrete construction of unidirectional IB-PRE which is secure in standard model based on the hardness of learning with error problem. Our technique is to use the novel trapdoor delegation technique of Micciancio and Peikert. The way we use trapdoor delegation technique may prove useful for functionalities other than proxy re-encryption as well.
Autonomous Navigation via Deep Imitation and Transfer Learning: A Comparative Study

End to end learning for autonomous navigation and driving has become a growing research trend in both industry and academia in recent years. Its promise is in treating the whole driving pipeline as the development of a deep neural network (DNN). Its Achilles' heel is access to thousands of images required for training of the DNN. This paper comprehensively investigates the applicability of the deep transfer learning for the specific task of end to end learning of autonomous navigation. Five state of the art DNNs including ResNet, AlexNet, and Densenet are applied here for extracting features from images taken by the front-facing camera of a mobile robot. Extracted features have different information values as DNNs have different architectures and learning capabilities. These features are then processed by a multilayer fully connected neural network to estimate the robot angular velocity. Obtained results for different DNNs indicate that the transfer learning-based models show a promising performance for accurately estimating the angular velocity purely using visual information. According to obtained results, AlexNet-base model outperforms others in terms of the estimation accuracy and the performance consistency.
Competent multi-level encryption methods for implementing cloud security

Cloud space to yourself is a one of the most considerable tentative issue in cloud computing as some of the clients are satisfied with existing policies or protocols where as rest of them are quite concerned with the aspects of corresponding security. In order to enhance the security levels in this paper we have proposed a multilevel security scheme that provides more security than that of any type of the existing single level encryption based process. In particular the proposed technique ensures that only pre authorized users can only access the cloud data and the other advantage of our algorithm is faster and safer in multiple directions such as while performing uploading and downloading a specific file.