Reviewerly/exHarmony huggingface dataset:
The Comparison of Some Version of Linear Vector Quantization (LVQ) for Vitamin and Mineral Deficiency Early Detection

Vitamin and mineral deficiency are often ignored because they do not have a direct impact on body health. However, prolonged deficiency can cause various diseases from mild to serious illness. Some previous research in computer science already conducted to make early detection of vitamin and mineral deficiency, but no one has produced an adaptive model to find out the most dominant type of deficiency. Therefore, the goal of this research is to develop an adaptive model using an artificial neural network (ANN) with Linear Vector Quantization (LVQ) as the learning algorithm to make early detection of vitamin and mineral deficiency. LVQ consists of three layers: an input layer that represents the features, output layer that represent the class label, and the competitive layer. The competitive layer will save the distance between the input vector and the codebook vector from each class. The distance will calculate using Euclidean Distance. LVQ also involves some parameters in the training process, like epsilon value, learning rate, codebook vector, epoch, and window size which obtained by trial and error experiment. This research will also compare the performance of some version of LVQ. The experiment results show that the maximum accuracy level obtained by the system is 85.71% by using LVQ3. The dataset used split into data training and data testing with a ratio 84:16 respectively. From our scenario, the optimum model was achieved by using 20 codebook vectors with the number of epochs is 3400 and the value of the learning rate parameter (α) of 0.4, window size (ō) of 0.3, and epsilon (ε) of 0.2.
Stone Carving Art Image Recognition Approach Based on Artificial Neural Network

Contraposing to the research on technology and application of stone carving art image and computer image recognition, this paper analyses and explores the art and computer aspects, and puts forward corresponding research approach. Firstly, the image preprocessing method based on morphological transformation technology is proposed. Then the specific features of stone carving image are analyzed, and the measures of feature extraction are also put forward. Finally, the strategy of image template matching is studied, and the genetic algorithm and BP network are integrated to solve the problem that BP algorithm is easy to fall into local minimum in image recognition. The automatic recognition system of the stone carving image is preliminarily realized. The experimental results show that the proposed image recognition scheme has high recognition accuracy and fast calculation speed, with good recognition effect which has been achieved through experiments.
Random Forest Algorithm for Learner’s Confusion Detection Using Behavioral Features

This paper presents a random forest algorithm to detect confusion affective states in virtual learning systems. The learning contents and examination questions are based on Bloom’s taxonomy cognitive levels. The proposed methodology captures learner’s behavioral interaction features. The confusion detection accuracy is above 90%.
Design Framework Forensics Readiness as a Service for Automatic Processing

Cloud computing technologies are one of the most developed knowledge fields today. However, the rapid growth of cloud computing has made cybercrime crime grow. This poses new challenges to investigating. Currently, there have been many research models and frameworks that support the forensics investigation process and methodology, but the existing frameworks are not suitable to be implemented automatically in a cloud environment. By using a literature survey, the researcher proposes a model that can be applied to a cloud environment. The proposed model is expected to carry out investigations automatically and aims to maximize the use of digital evidence so that readiness can be achieved.
Real-Time 3D Position-Based Dynamics Simulation for Hydrographic Printing

Hydrographic Printing, also called Hydrographics, is a viable method for coloring objects created with 3D printers. However, executing the hydrographic technique leads to a complex interaction between a thin film and a 3D printed object, in which the image in the film must adhere to the object. To handle the difficulty of predicting the final result of hydrographic printing, we propose a 3D computational simulation that uses Position-Based Dynamics, a popular technique for simulating deformable bodies and widely used in physics engines. We take advantage of this technique running in parallel a GPU-based simulation with suitable performance. We simulate the film behavior and its interaction with the printed object, as an interaction between a soft-body colliding with a rigid one. To evaluate the achieved performance consistency, we varied the number of vertices and voxels in the bodies involved and observed that the simulation kept running in real-time. We also execute the hydrographic technique in different printed models and compare these results with the simulated models.
Ellipse Fitting Demodulation System of Fiber Optical Hydrophones System Based on 3×3 Coupler

3×3 coupling demodulation has the advantages of no carrier modulation, simple structure and capacity of realizing all-optical fiber hydrophone system. However, the asymmetry of 3×3 coupler will introduce demodulation errors to the usual 3×3 demodulation scheme, and the demodulation scheme using three channels of the 3×3 coupler is not conducive to the formation of large-scaled time division multiplexing system. In view of the above defects, this paper uses the ellipse parameter fitting combined with the digital arctangent algorithm to demodulate signals of two channels of the 3×3 coupler. The principle of this demodulation method is demonstrated in this paper, and simulations to verify the feasibility of the algorithm is carried out. Based on the simulation, the demodulation system in the laboratory was built. The sinusoidal analog signal with amplitude of 4.25v at 1000Hz was demodulated. The demodulation amplitude was 4.2024rad, and the harmonic suppression ratio THD of the demodulated signal frequency spectrum was 39.2014dB. The linear correlation coefficient of demodulated signals with different amplitude at 1000Hz was 0.98. The average demodulation phase difference of 3×3 coupler was 118.8877 °, and the standard deviation was 1.4616 °. Experiments showed that the demodulation system had good consistency and accuracy.
Intelligent calculation method of communication Transmission loss based on hybrid neural network

The traditional calculation method of communication transmission loss has the calculation error of communication loss, and the application scope of the calculation method is small. Therefore, this paper proposes an intelligent calculation method of communication transmission loss based on hybrid neural network. Based on the ideal free space transmission loss model, the transmission loss model of outdoor environment is established; the communication obstacles in the transmission process are determined; and the transmission loss calculation area is divided. The calculation of communication transmission loss is completed by using hybrid neural network combined with the specific parameters of the demarcated area. By comparing with the method based on neural network, it is verified that the method using hybrid neural network can improve the calculation accuracy by about 77.1%, and the method has a wider range of application.
Performance Comparison of Finite Field Adders for SM2 Algorithm Based on FPGA Implementation

An Elliptic curve is one of the significant applications of cryptography, and the 256-bit of crypto that uses a digital signature algorithm based on such a method has the same security level as 3072-bit of RSA. In other words, the elliptic curve algorithm can reach the same security level using 12 times shorter bits than the RSA method. From the computational side, an efficient and high-speed calculation methodology is required in the field of 256 bits' finite field. The most required calculation in the finite field is the addition of arithmetic. This paper discusses our latest results on 256 bits' addition arithmetic implementations, which are implemented on the Virtex6 FPGA board and Xilinx 14.5 IDE. The considered simulation results include time delay propagation, area, and power consumption.
Increasing the correlation level of polarization entangled photon pairs generated by type-II SPDC in BBO

Entangled photon sources introduce phase shift between polarization states. Exceeding coherence length, coincidence rate drops for certain bases. Correlation is restored by phase compensation. Applying anisotropic retarders coincidence rate doubles.
Shape and Contrast Adaptive Non-local Means Filter

In this paper we propose an improvement to the NLM algorithm. It consists of an adaptive threshold parameter used to compute the similarity function of the Non-Local Means (NLM) approach integrated in the NLM-SAP (shape-adaptive-patches) algorithm. To obtain the adaptive threshold parameter we use an algorithm that decomposes the image in a textural and a homogenous part. Additionally, a prefiltering method is used on the input noisy image, that is based on a discrete version for TV filtering. Experimental results show that this allows the obtention of better visual, and objective measures-quantified results than our previous work and other considered NLM approaches. In some cases, better visual results were obtained compared with the state-of-the-art BM3D (Block Matching 3D) algorithm and the results in terms of PSNR/MSSIM are comparable.
B-mode Ultrasound Texture Recognition Algorithm of Liver Based on Random Forests

With the advantages of non ionizing radiation, real-time imaging, multi-directional tomography and dynamic observation of blood flow, B-mode ultrasound has become the preferred method for imaging examination of some organs such as liver, gallbladder, pancreas and spleen. On the one hand, the B-ultrasound diagnostic doctor fixed his eyes on the screen of B- ultrasound monitor, on the other hand, he operated the probe to move on the patient's position to be examined. With dozens of patients are examined every day, so B-mode ultrasound doctors often work at full-load every day and lead to eye fatigue. Eye fatigue easily causes erroneous diagnosis or missed diagnosis. With the development of artificial intelligence technology and computer technology, more and more work done by human can be completed by computer instead. The computer will not feel fatigue for long working hours, and its analysis has objectivity and consistency. Therefore, computer-aided diagnosis is an urgent need in the field of B-mode ultrasound. Random forests algorithm is a machine learning algorithm based on decision tree, which can be used for classification. In this study, a B-mode ultrasound texture recognition algorithm for liver based on random forests is established. Compared with CART decision tree algorithm with sufficient samples, it is found that random forests is superior to CART decision tree in the accuracy of texture recognition, so random forests has a good application prospect in the analysis of B-mode ultrasound texture and computer aided diagnosis of diseases.
AI and Patents and Trade Secrets

This chapter considers some implications of artificial intelligence (AI) for the disclosure of information about inventions and decisions. Patent law promotes the disclosure of information about inventions and can reward such disclosure with a patent. But, if AI or AI-generated artifacts are not patentable for the various reasons discussed in this chapter, then disclosure of information about them might not occur. Trade secret law currently protects secret AI algorithms that are used to make decisions from unauthorized disclosure, potentially hindering the accountability of such decision-making. This chapter describes how both patent law and trade secret law could adapt to ensure that sufficient information about inventions and automated decisions is disclosed to further knowledge and accountability.
Multi Data Driven Validation of E-Document Using Concern Authentic Multi-signature Combinations

The idea raises a novel data security protocol for ensuring reliable online affirmation of e-documents with stronger compliance of authentication, integrity confidentiality, and non-repudiation scenarios. Initially the parent authority shares four secret signatures to the authorized client and stores them on server database. The protocol is initiated by server with hiding of four parent body signatures on the e-document image with each one hosted on a separate region. Critically, this hiding is dictated by self-derived hash functions on client id-number for precisely locating the concern region hosting the particular applicable signature. Importantly, this idea is implemented through hash values tracing the start indexes for circular sequencing of regions as well as signatures both. Next, this certified e-document is endorsed at the client end with segmented concealment of each shared signature on each separate region. Interestingly, here also similar region based circular sequencing of signatures are adopted with self-designed hash operations on client name, client date-of-birth and server issued session random challenge. Finally, this authenticated e-document is ratified by server with validation of all embedded signatures through those same hash functions to establish data authenticity cum legality of both parties. Additional enhancement is also addressed by fabricating signature bits on distinctly transformed pixel byte elements of an image block and adopting standard deviation based block transformations to promote excellent robustness. Overall, this protocol clearly confirms superiority over other works both in terms of security principals and standardized evaluation of exhaustive simulation results from different angles.
Research on a controllable hybrid encryption algorithm for privacy image

According to the security performance of image encryption, a hybrid image encryption algorithm is proposed. The algorithm can use different chaotic systems and pseudorandom generators to scramble and spread according to the needs of human control. The security of the algorithm is improved by dividing the plaintext into equal length sequence segments and encrypting them into inverse operations. Through the analysis of the sensitivity of the algorithm, the ability to resist differential attack and the encryption rate of the algorithm, it shows that the algorithm is safe and reliable.
Re-Training and Parameter Sharing with the Hash Trick for Compressing Convolutional Neural Networks

As an ubiquitous technology for improving machine intelligence, deep learning has largely taken the dominant position among nowadays most advanced computer vision systems. To achieve superior performance on large-scale data sets, convolutional neural networks (CNNs) are often designed as complex models with millions of parameters. This limits the deployment of CNNs in embedded intelligent computer vision systems, such as intelligent robots that are resource-constrained with real-time computing requirement. This paper proposes a simple and effective model compression scheme to improve the real-time sensing of the surrounding objects. In the proposed framework, the Hash trick is first applied to a modified convolutional layer, and the compression of the convolutional layer is realized via weight sharing. Subsequently, the Hash index matrix is introduced to represent the Hash function, and its relaxation regularization is introduced into the fine-tuned loss function. Through the dynamic retraining of the index matrix, the Hash function can be updated. We evaluate our method using several state-of-the-art CNNs. Experimental results showed that the proposed method can reduce the number of parameters in AlexNet by 24 × with no accuracy loss. In addition, the compressed VGG16 and ResNet50 can achieve a more than 60 × increased speed, which is significant.
STATISTICAL MODELING AS A TOOL FOR DEVELOPING INFORMATION AND MATHEMATICAL CULTURE OF STUDENTS

The use of statistical modeling for the development of information and mathematical culture of schoolchildren is considered in the article. A comparative analysis of various statistical modeling tools: spreadsheets, programming languages, interactive mathematical environments is carried out. Examples of the simplest statistical models and their implementation using these tools are given: experiments with coins and cubes, random selection without returning, Bernoulli tests before the first success. The most interesting theoretical issues that arise during statistical experiments and are available for study in the school mathematics course are highlighted. Several games with probabilistic content are considered, demonstrating well-known statistical paradoxes. It is shown how the skills accumulated in the process of statistical modeling can be used to study real situations and perform independent research and design work on this basis.
An Effective Trespasser Detection System using Video Surveillance Data

Today video surveillance applications are not only enough for object monitoring and recording but also necessary for object detection and decision making to prevent and alert unauthorized cases. The main objective of the paper is to detect a person who is passing through into a restricted area. In this paper, there are two main stages, the first stages is human detection and classification. The second stage is the detection of the passing across in a restricted area. In this proposed technique, human motion detection and tracking is implemented using Gaussian Mixture Model. Arbitrary restricted region can be drawn as polygon shape on input surveillance video clip. By comparing the foreground motion object region and the restricted region area, the pass cross event of the motion object can be detected. The accuracy and performance of the proposed technique is proved with various experimental results.
A Compact Digital Signature Scheme Based on the Module-LWR Problem

We propose a new lattice-based digital signature scheme MLWRSign by modifying Dilithium, which is one of the second-round candidates of NIST's call for post-quantum cryptographic standards. To the best of our knowledge, our scheme MLWRSign is the first signature scheme whose security is based on the (module) learning with rounding (LWR) problem. Due to the simplicity of the LWR, the secret key size is reduced by approximately 30% in our scheme compared to Dilithium, while achieving the same level of security. Moreover, we implemented MLWRSign and observed that the running time of our scheme is comparable to that of Dilithium.
Resolving Efficiency Bottleneck of the Bellman Equation in Adaptive Teaching

An intelligent tutoring system (ITS) performs adaptive teaching, which is essentially a process of making decisions. In each teaching step, the system chooses an optimal action based on information of the student’s current knowledge state. The partially observable Markov decision process (POMDP) model is a useful tool for building an ITS. The model enables the system to choose optimal teaching actions even when information of student knowledge states is incomplete and/or unclear. In a POMDP, the agent makes decisions by evaluating a Bellman equation, which has exponential complexity. The computing cost for evaluating the Bellman equation has been a major bottleneck in building POMDP-based ITSs. In this paper, we report our techniques for improving computing efficiency in evaluating the Bellman equation in an ITS. The techniques include partitioning and reducing the state space and solution space.
A Genetic Feature Selection Based Two-Stream Neural Network for Anger Veracity Recognition

People can manipulate emotion expressions when interacting with others. For example, acted anger can be expressed when the stimulus is not genuinely angry with an aim to manipulate the observer. In this paper, we aim to examine if the veracity of anger can be recognized from observers' pupillary data with computational approaches. We use Genetic-based Feature Selection (GFS) methods to select time-series pupillary features of observers who see acted and genuine anger as video stimuli. We then use the selected features to train a simple fully connected neural network and a two-stream neural network. Our results show that the two-stream architecture is able to achieve a promising recognition result with an accuracy of 93.6% when the pupillary responses from both eyes are available. It also shows that genetic algorithm based feature selection method can effectively improve the classification accuracy by 3.1%. We hope our work could help current research such as human machine interaction and psychology studies that require emotion recognition.
Methods of Federated Filtering in the Problems of Navigation Data Processing

Abstract The paper gives an overview of federated filtering methods used in navigation data processing. The principles of designing federated filters are considered and their man properties are discussed.
Global-Local Feature Fusion Mechanism for Sentiment Classification

Short text is a common form of information and opinions in social life. The sentiment analysis of short-text is helpful to obtain the user's attitude towards items, events and services. However, because of sparse data problem of the shorttext, sentiment analysis for short-text is still in face of great challenges. To address this problem, this paper proposes a novel sentiment classification model, Cap_ONCNNA, which includes word embedding module, sentence representation module and capsule classification. Specially, in the sentence representation module, we proposed a new text representation mechanism ONCNNA. This method can generate global structure and local feature semantic representation by attention-based ON-LSTM and CNN respectively and then fuse them to enhance the sentence representation. In classification module, we are able to get more hidden information by introducing capsule network. The experiments on MR, SST1, and SST2 datasets prove our model is superior to other models. Compared with the best model of the three datasets, the accuracy of our model is improved by 0.2% (MR), 1.84% (SST1), 0.03% (SST2) respectively.
TOPIC FLEXIBLE SENTIMENT CLASSIFICATION USING MULTI-CLASS SVM

Classifications of sentiments is a subject-receptive feature, to be even more clear a classifier used for one act different for the other. This may emerge a greater trouble in tweet sentimental evaluation. There are varieties of subjects in twitter consequently it is very onerous to broaden a regular classifier. When evaluated to product overview, Twitter dearth facts about mechanism that labels and rates which can be further used to collect sentiments. There's very confined textual content in the tweets and subsequently it diminishes the overall performance of classification. Right here, we recommend a semi-supervised SVM (guide vector machine) that initiates a classifier which is which starts integrates on not unusual specification and mixed records that are labeled with different subjects. Text and non-textual content specification are taken and divided into different view for co-education. The SVM studying algorithm updates topic-adaptive specification primarily based on the collaborative choice of unlabelled statistics, which in turn allows choosing more dependable tweets to enhance the performance. Meanwhile, SVM can also reap dazzling accuracy and F-rating..