Reviewerly/exHarmony huggingface dataset:
Suggested Video Steganography Algorithm Based on Power Low Transform (PLT) Using IoTs

Abstract Datum embedding or information hiding is a method that accepted us to secret embedded and extracted data from any file, such as picture file, a video file, and sound file. The base algorithm in steganography is the embedded and extraction algorithms. The objective of this paper is hiding a secret message in video frame to prevent an attacker from accessing the message. This paper suggested new algorithm to hide secret message in video, after convert video to number of frames, it takes each video frame applied the power low transform (PLT) in 3 or 3.5 or 4. In this paper takes power low transform only 3.5 to hide secret message in each video frame uses the secret key, the secret key is curve of cosh-exponential, each point in curve selected the location in frame to hide one bit from frame video. It obtains stego-frame video to Transmitted the for IoTs, and save in database in IoTs, and any authors can be access to this stego-frame video in database to known secret key. The outcome of this algorithm is very good in steganography video, it uses power low transform to obtain the high security, transparency, high capacity in secret message and powerful. These results are obtaining many tests PSNR, MSE, Entropy, Correlation coefficient, Histogram, and Capacity.
Analytical Transform for Image Compression

The need for better quality compressed images with the limited channel bandwidth in real-time data transmissions is the reason for which compression technique plays an important role in multimedia wireless communication and also enhancing the speed of communication. Transform based compressions are widely used owing to their higher compression rate in images. To have a better compression; increasing the probability of finding a character/symbol substantially which results in frequency spread reduction is proposed. Such a lossless transform is called as `Analytical Transform' (AT). Proof behind this transform decreasing the entropy and allowing a simple compression algorithm to work more effectively is explained. `Analytical Transform' is a sequential data transform aiding for superior compression. A comparative analysis of other sequential data compressors like Lempel and Ziv against Huffman coding in the combination of Analytical transform provides 1.3 to 1.8 times better compression.
Procedural Content Generation of Custom Tower Defense Game Using Genetic Algorithms

In the present day, it is difficult to imagine the development of computer games without the use of artificial intelligence. We see it utilized for gameplay, players modeling, playtesting, or content generation. In this paper, we focused on the content generation of a custom Tower Defense game named Save the Sheep. The Tower defense game is a strategic game, which was, in our case, proposed in a non-violent way. We generated key building blocks of the game with a genetic algorithm, i.e., a game map, unit placement, and a waves system. The final Tower Defense inspired game was implemented in the Unity game engine. The results show that by applying genetic algorithms, it is possible not only to generate content that makes the game more complex, but also more challenging and interesting for players.
Research on the Application of Deep Learning Technology in Intelligent Dialogue Robots

With the breakthrough of the application of deep learning technology in computer vision and natural language processing tasks, the application of deep learning to intelligent dialogue robots has become a new research hotspot. As a novel way of human-computer interaction, intelligent dialogue robots are becoming one of the entrances of mobile search and services, and are increasingly being paid attention to by enterprises and society. However, there are still some problems in the current dialogue robot, such as insufficient use of multi-modal information and weak emotional expression ability. In this paper, a multi-modal intelligent reply generation model based on seq2seq + attention is proposed by using deep learning algorithm, which can effectively use multi-modal information such as text, picture and video to interact. At the same time, on the basis of considering the contextual content information, the model further integrates the emotional transfer change information of the dialogue text. Experimental evaluation results show that the combination of emotional intelligence can make text response generation more emotionally expressive and more vivid response generation results.
Single View Learning in Action Recognition

Viewpoint is an essential aspect of how an action is visually perceived, with the motion appearing substantially different for some viewpoint pairs. Data driven action recognition algorithms compensate for this by including a variety of viewpoints in their training data, adding to the cost of data acquisition as well as training. We propose a novel methodology that leverages deeply pretrained features to learn actions from a single viewpoint using domain adaptation for knowledge transfer. We demonstrate the effectiveness of this pipeline on 3 different datasets: IXMAS, MoCA and NTU RGBD+, and compare with both classical and deep learning methods. Our method requires low training data and demonstrates unparalleled cross-view action recognition accuracies for single view learning.
Pronunciation Accuracy Calculator using Machine Learning

Pronouncing words in an error-free and correct way is one of the most relevant skills to acquire. Scientists all over the world are concentrating on identifying pronunciation mistakes with the help of machine learning. This paper describes calculating pronunciation accuracy of a person using machine learning. Pronunciation dictionary data mainly from CMU library is employed to convert the inputted voice data into digital format (ARPABET) and analyzing that data using machine learning models such as LSTM based RNN and then applying HMM. Hence, a neural network model was built to predict pronunciation of different words. Pronunciation accuracy is calculated that can be used by anyone for self assessment of pronunciation accuracy. The results demonstrate acceptable accuracy of the model.
Multi-tier security for cloud environment by HECDSA with IBEM

Security has become the main issue in the current data transmission, whereas it is considered as a significant goal for vendors and providers of cloud services. Hyper elliptic curve cryptography (HECC) technique is the best cryptography approach, whereas digital signature algorithm (DSA) is also an advanced DSA However, an advanced protocol is assigned with authentication of the key agreement by the Parallel Pollard rho (PPρ) algorithm to enhance the security of the cloud environment. Hence, this paper focuses on Hyper HECC along with the technique of DSA signature that has designed together for encryption analysis. HECC technique gets transplanted into the DSA algorithm, which works on the principle of signature-based Hyper Elliptic Curve Digital Signature Algorithm (HECDSA). In order to improve the security level with less consumption of time, Inter bit exchange and merge (IBEM) pattern is considered for data as permutation and feed using substitution as S-Boxes. This proposed hybrid mechanism of enhanced encryption security scheme as HECDSA with PPp algorithm and key exchange using IBEM pattern by blowfish technique. The cryptographic performance of the data may not allow the intruders as quickly as possible to find the key mechanism that has been sending from the user. Thus, the resultant is provided with both Cloud Service Provider (CSP) and owner of the data gain enhanced security. Moreover, the proposed hybrid HECDSA-IBEM is compared with the existing encryption technique for evaluating the performance metrics based on scalability and security.
Local Quality Pattern (LQP): A Quality Aware Local Statistical Feature Descriptor for No-Reference Image Quality Assessment

Assessment of the quality of the no-reference images in real-life applications is of immense importance. The no-reference image quality assessment (NR-IQA) algorithms are designed to measure quantitatively and accurately the perpetual quality of an image without any prior knowledge about its reference. In this work, we have proposed a novel NR-IQA metric to replicate the human visual system (HVS) for the perception of image distortion using the structural details and luminance information of the image. We propose a local descriptor that extracts the perceptual structural features of the image with highlighted reflectance to represent the variation of illuminance in the distorted probe image. We name this quality-aware descriptor as Local Quality Pattern (LQP). The extracted features are trained with support vector regression (SVR) to model the mapping of the feature vectors with the quality measure. Finally, the metric obtained is named Local Quality Pattern Score (LQPS). Extensive experiments conducted on publicly available datasets namely, LIVE, MLIVE and CSIQ databases to show that the proposed LQPS metric outperforms the compared NR-IQA methods with a prominent margin.
Anatomical Planes-Based Representation for Recognizing Two-Person Interactions from Partially Observed Video Sequences: A Feasibility Study

This paper presents a new approach for two-person interaction recognition from partially observed video sequences. The proposed approach employs the 3D joint positions captured by a Microsoft Kinect sensor to construct a view-invariant anatomical planes-based descriptor, called the two-person motion-pose geometric descriptor (TP-MPGD), that quantifies the activities performed by two interacting persons at each video frame. Using the TP-MPGDs extracted from the frames of the input videos, we construct a two-phase classification framework to recognize the class of the interaction performed by two persons. The performance of the proposed approach has been evaluated using a publicly available interaction dataset that comprises the 3D joint positions data recorded using the Kinect sensor for 21 pairs of subjects while performing eight interactions. Moreover, we have developed five different evaluation scenarios, including one evaluation scenario that is based on fully observed video sequences and four other evaluation scenarios that are based on partially observed video sequences. The classification accuracies obtained for each of the five evaluation scenarios demonstrate the feasibility of our proposed approach to recognize two-person interactions from fully observed and partially observed video sequences.
Supervised Feature Embedding for Classification by Learning Rank-based Neighborhoods

In feature embedding, the recovery of associated discriminative information in the reduced subspace is critical for downstream classifiers. In this study, a supervised feature embedding method is proposed inspired by the well-known word embedding technique, word2vec. Proposed embedding method is implemented as representative learning of rank-based neighborhoods. The notion of context words in word2vec is extended into neighboring instances within a given window. Neighborship is defined using ranks of instances rather than their values so that regions with different densities are captured properly. Each sample is represented by a unique one-hot vector whereas its neighbors are encoded by several two-hot vectors. The two-hot vectors are identical for neighboring samples of the same class. A feed-forward neural network with a continuous projection layer, then learns the mapping from one-hot vectors to multiple two-hot vectors. The hidden layer determines the reduced subspace for the train samples. The obtained transformation is then applied on test data to find a lower-dimensional representation. Proposed method is tested in classification problems on 10 UCI data sets. Experimental results confirm that the proposed method is effective in finding a discriminative representation of the features and outperforms several supervised embedding approaches in terms of classification performance.
Dimensionality Prediction for Word Embeddings

Word embeddings are the basic building blocks for many natural language processing tasks. Word embedding models represent both syntax and semantics of a word in a vector space. The vector representations require more space if words are represented using a high dimensionality. The optimal dimensionality of a word embedding partly assists in structure learning of a neural network models for solving natural language processing (NLP) tasks like classification and part-of-speech (PoS) tagging. Embedding dimension chosen by word embedding model has a significant impact on the word vectors generated. high dimensional embeddings have a heavy memory footprint. In this paper, we propose a model to predict the optimal word embedding size for a given corpus so that the word embeddings can be used to learn the structure of a neural network for typical NLP tasks and, the embeddings can be used in memory-constrained devices. A random forest-based regression model is used to predict the word embedding size. Experiments are conducted to identify the features that have a significant effect on the dimensionality of word embeddings.
Compressing CNNs by Exponent Sharing in Weights using IEEE Single Precision Format

The performance of convolutional neural networks for computer vision and other applications has crossed human accuracy levels on high-end systems. The demand for these applications in small, mobile hardware is increasing while expecting the same performance. These devices have considerably smaller memory and power budgets. Prior work on model compression for inference on edge devices has sacrificed some accuracy to compress the models. We propose a novel model compression approach by sharing exponents of weights stored in IEEE floating-point format. This approach does not require any fine-tuning after compression. We demonstrate our technique on different trained models resulting in nearly 10% compression in storage and requiring less than 1.5 times the original execution time.
Research on Word Alignment Based on Embedded Bilingual Words

Word alignment is one of the most important problems in machine translation. Many machine translation technology solutions require parallel corpus to achieve word level alignment. However, the current mainstream word alignment methods and tools need to maintain a large-scale word translation probability table, which will generate huge network transmission overhead in distributed environment, which is not conducive to parallelization, and exists in the face of large-scale data sets large performance issues. The extensive development of deep learning and the deep application of word embedding technology in natural language processing have opened up a new way for word alignment algorithm. With the advent of bilingual word embedding, it is possible to use word embedding to calculate lexical similarity across languages. In this paper, bilingual word embedding is proposed to calculate the probability of lexical translation, and then word alignment is achieved. Compared with the large word translation probability tables in traditional methods, lightweight word vector tables have a significant advantage in transmission costs, and they are more suitable for large-scale corpus scenarios and distributed computing environments.
Region of Interest Coding Based on Convolutional Neural Network

Abstract The traditional region of interest coding method mainly uses low-level features to detect the Region of Interest (ROI). The ROI detected by it is poor in stability and is not easily interfered by noise. In this paper, ROI detection is performed on the image through a deep convolutional network to obtain a stable ROI based on the high-level feature extraction of the image, and then the discrete cosine transform (DCT) is performed on the image and divided into coding units. According to whether the coding unit is an ROI, To determine the quantization matrix used when encoding it. This article uses fine quantization for coding units that belong to ROI, and coarse quantization for non-ROI coding units. In this way, it can be ensured that the compression rate is greatly reduced without affecting the subjective perception of the image. Experiments show that the compression rate of this method can reach about 84%, and the weighted peak signal-to-noise ratio is improved by about 0. 99dB on average compared with JPEG encoding.
A Case Study of Choreography Realizability Checking on Smart Home Application

This paper studies the choreography realization problem that is a problem to synthesize a concrete model from an abstract specification in service-oriented architecture. So far, we have studied the problem on a case where the choreography was defined by one or two scenarios and was expressed by an acyclic relation of events. In our previous study, we proposed the use of event structures to express choreography. In addition, we introduced the re-constructibility of event structures, which is a property of them to be satisfied, and showed a necessary condition for an event structure to be re-constructible. In this paper, we use a simple example of a smart home where the choreography is defined by three scenarios with multiple message sets. We demonstrate the re-constructibility of the event structure created from the choreography.
Non-traditional drawing techniques as a means of developing the imagination of older preschool children with general speech underdevelopment

The basis of the creative process is imagination, which determines the creative development of the child's personality, is a component of any activity, an integral part of socialisation. The use of non-traditional drawing techniques in working with children with general speech underdevelopment for correctional and developmental purposes can improve imagination, develop creativity, and hand-eye coordination. As a result of the study, we identified the levels of imagination of children with general speech underdevelopment, identified its criteria and qualitative characteristics, described and tested diagnostic tools based on preschool educational institutions; formative correctional and pedagogic work was carried out, which covered not only the children of the experimental group, but also parents, kindergarten teachers and it was aimed at increasing the selected indicator. As a result, after carrying out the formative work, higher indicators of the level of development of the imagination of children of the studied category were obtained in comparison with the initial ones, which confirms the assumption about the effectiveness of using complex correctional and pedagogic assistance with the use of non-traditional techniques of visual activity. In general, we can say that the proposed correctional work will contribute not only to the development of imagination, but also to improve the basic mental processes, creative abilities, intellectual-volitional qualities, which will allow the child to prepare for schooling.
Optimal Strategy Selecting Method for Mimic Defense Based on Incomplete Information Dynamic Game

Mimic defense (MD) is a novel architecture which takes the dynamic and heterogeneous security of redundant executors, and prevents further attacks through multi-mode decision feedback mechanism. However, there is a lack of effective analysis and evaluation on the defense cost and risk of multi-mode decision output. In this paper, we proposed the idea of Bayesian decision-making with minimum risk combined with the failure probability of the executor and the output of multi-mode decision. After considering the impact of failed defense and defensive cost on strategy generation, the optimal cleaning strategy is designed to ensuring the validity of optimal strategy. Finally, simulation experiments confirm the effectiveness of the model.
Point in: Counting Trees with Weakly Supervised Segmentation Network

For tree counting tasks, since traditional image processing methods require expensive feature engineering and are not end-to-end frameworks, this will cause additional noise and cannot be optimized overall, so this method has not been widely used in recent trends of tree counting application. Recently, many deep learning based approaches are designed for this task because of the powerful feature extracting ability. The representative way is bounding box based supervised method, but time-consuming annotations are indispensable for them. Moreover, these methods are difficult to overcome the occlusion or overlap. To solve this problem, we propose a weakly tree counting network (WTCNet) based on deep segmentation network with only point supervision. It can simultaneously complete tree counting with localization and output mask of each tree at the same time. We first adopt a novel feature extractor network (FENet) to get features of input images, and then an effective strategy is introduced to deal with different mask predictions. In the end, we propose a basic localization guidance accompany with rectification guidance to train the network. We create two different datasets and select an existing challenging plant dataset to evaluate our method on three different tasks. Experimental results show the good performance improvement of our method compared with other existing methods. Further study shows that our method has great potential to reduce human labor and provide effective ground-truth masks and the results show the superiority of our method over the advanced methods.
Optimal Sensor Position: Exploring the Interface Between the User and Sensor in Activity Recognition System

Human activity recognition systems combined with machine learning normally serve users based on the fixed sensor position. Uniform sensor position normally cannot satisfy the user’s demand according to different conditions. In this paper, we recognized the sensor position as an interface between the user and sensor system. We designed the optimization scheme to generate the best sensor position for activity recognition system. The user can indicate his/her preferred or disliked position and sensor numbers and the proposed optimization evaluates which position or positions combination can generate best accuracy under user’s preference. With the experiment, the proposed scheme can be employed to discover the optimal position to help the HAR system in a simple and customized way.
Image Super-Resolution Reconstruction Based on Online dictionary learning Algorithm

For the image super-resolution reconstruction method based on case-learning shows that a fast and efficient dictionary learning algorithm is very important to solve the problem of mapping inconsistency between low-resolution and high-resolution images. This paper adopts the online dictionary learning algorithm for the image super-resolution. In the learning stage, the algorithm constructs the high-resolution and the corresponding low-resolution feature training sets, then by using the online dictionary learning algorithm, obtains a sparse coding matrix of the low-resolution training sets, and computers the high-resolution dictionary by sharing the sparse coding coefficients; in the reconstruction stage, the input low-resolution image firstly is interpolated to the size of the desired high-resolution image, and obtains the sparse coding matrix through OMP ( Orthogonal Matching Pursuit ) method in the low-resolution test sets, then computers the high-resolution image blocks based on the above high-resolution dictionary and the later sparse coding matrix, finally reorders and averages the blocks to achieve the reconstructed high-resolution image. The experimental results show that the proposed method can achieve better quality for image super-resolution reconstruction than the traditional sparse coding method, the detail and texture of the reconstructed image are reconstructed well, and the algorithm can effectively inhibit the artifact of image edge phenomenon.