Reviewerly/exHarmony huggingface dataset:
Recognition Pattern of Arca Siwa Prambanan Temple with Canny and Backpropagation Algorithm

The statue temple of recognition pattern used to distinguish the direction the digital image (photos) in order facilitate tourist or user knows the direction of the capture of temple image. In its application used algorithms the detection of edge Canny and artificial neural network Backpropagation and the process of extracting by using the PCA. Canny algorithm applied to the process of image where image processing rgb converted into binary image. While backpropagation algorithms used to recognize patterns of processing that image. The direction of which were recognized by the system which is West, South, Southeast, East, Northeast, and North. The level of accuracy against 36 testing data to the specifications 24 the image of practicing and 12 test image. The level of accuracy testing of the image of trained 100 %, while the image of a test of 83,33 %.
Dam Deformation Prediction Model Based on Combined Gaussian Process

Systematic combat effectiveness evaluation is an important part of system combat research, which is of special significance for our army to adapt to modern war. The scale of systematic combat is large and the structure is complex, so it is difficult to complete the evaluation work with the traditional empirical method or mathematical method. In recent years, the simulation method which is widely used has achieved good results, but there are also problems such as huge computational cost. On the basis of system combat simulation, this paper uses xgboost to build an intelligent evaluation model of system combat effectiveness, which effectively solves the problem of traditional methods in calculating costs. Compared with the SVM method, the good performance of this method is proved.
User Experience, IoMT, and Healthcare

In this paper, we discuss current trends in how health professionals and patients are using wearables, connected devices, and software tools to deliver care and health monitoring purposes.We emphasize the importance of considering users' experience through understanding user workflows, their needs, and their limitations when creating connected health ecosystems (CHES).We discuss both the patient and the provider as "users" in the ecosystem.We note both barriers to using the Internet of medical things (IoMT) to create CHES and efforts to overcome them.The increasing penetration of the Internet and the availability of connected health devices along with changes in reimbursement policies provide an environment for CHES to grow.
Federated-Cloud Based Deep Neural Networks with Privacy Preserving Image Filtering Techniques

Training Deep Neural Network (DNN) models often require significant computational resources due to the large dataset sizes and a huge number of parameters to be optimized. A cloud-based approach may be utilized to accommodate such resource needs with flexibility and efficiency. But, protecting data privacy is a challenge in such approaches. Most of the encryption-based approaches for providing privacy incurs substantial overheads. However, in many instances, only part of data information needs to be protected, and the level of privacy is often dependent on the application requirements. Various types of image filtering techniques are utilized to generate distorted DNN datasets with application-specific privacy requirements satisfied. In general, high distortion level provides strong protection on data privacy but degrades the DNN accuracy. To find the appropriate type and level of image filtering prior to the training process, we identify an image similarity metric that can be used as a DNN accuracy predictor as well as the distortion level indicator. Furthermore, to improve the DNN accuracy of highly distorted datasets, we propose a privacy-preserving federated-cloud DNN training/classification on multiple distorted datasets. Each cloud trains an independent DNN model with a different image filtering algorithm, and then the client combines and utilizes the multiple models to obtain a well-performing model. Experiments were conducted to validate the effectiveness of the proposed schemes.
Optimization of Neural Networks with Response Surface Methodology: Prediction of Cigarette Pressure Drop

Neural network is an artificial intelligence technique providing successful results in solving many prediction problems. There are many factors affecting the predictive performance of a neural network such as learning rate, momentum rate, number of neurons etc. In practice, trial-error method is used in the selection of these parameters. However, this approach is a time-consuming process and can only measure the effect of change in one parameter on performance at a time. In recent years, alternative methods such as experimental design, genetic algorithm, simulated annealing have been used to find the optimum neural network topology. In this study, response surface method, which is one of the most common design of experiment, was used to determine the neural network topology that would provide the highest predictive performance of the pressure drop that is a quality parameter in tobacco industry. The parameters affecting the pressure drop parameter are considered as circumference, total weight and ventilation. In this context, number of hidden neurons, learning rate, momentum rate and stop criteria were identified as the experimental factors and the combination that gives the lowest mean absolute deviation has been proposed as neural network model to predict pressure drop parameter. As a real life application, thousand cigarette samples have been processed by multilayer perceptron. Findings revealed that epoch size, learning rate, number of hidden neurons and stop criteria have significant linear impact on mean absolute deviation of neural network. Optimum neural network design has been obtained to predict pressure drop parameter.
An Improved LBlock-s Key Schedule Algorithm

LBlock-s key schedule algorithm is a modified version of LBlock key schedule algorithm that has been claimed to have better diffusion properties. Using correlation test based on Niaz method we conclude that LBlock-s’ key schedule algorithm produced high correlation keys. In other words, their randomness properties are not good enough. In this paper, we improved LBlock-s key schedule algorithm to enhance its randomness properties against high correlation keys. The result show that our improve algorithm has produced lower correlation keys than LBlock-s in subkeys level, bytes level as well as bit level. On the other hand, our comparation of execution time of both key schedule algorithm shows that even our improved key schedule algorithm is more complex than LBlock, but their execution time has not significant gap.
Analysis of Automation Systems and Virtual Reality Applications in Smart Libraries

With the rapid development of information technology, automation systems and virtual reality technology have become important pillars of economic and social development. As a key institution to store books and information materials, develop information resources and provide knowledge support, libraries are constructing and optimizing their systems. Based on the literature review about library automation systems and virtual reality application, this paper analyzes their expectable influence to promote more concern from scholars and technical personnel, and thereby, enhance the overall management and service level of libraries, especially for smart libraries, which represent the development trend of this smart+ era.
A Modified Tiny Encryption Algorithm Using Key Rotation to Enhance Data Security for Internet of Things

The Tiny Encryption Algorithm (TEA) has been proven to be suitable for constrained devices because of its lightness. However, TEA suffers from equivalent key and related key attacks. This paper proposed a modification to TEA to improve its security by rotating its subkeys to every round function as the new key scheduling scheme. Also, the round function of TEA was also modified by shifting the keys before integrating to the functions. The modified TEA was benchmarked with the original TEA with avalanche effect and completeness test. The modified TEA avalanche effect is 54.4% against 47.2% for TEA, and its' completeness test is 51.4% against 49% for TEA. The modified TEA was also tested with equivalent keys and produces different ciphertext. The tests showed that the modified TEA is more secure than the original TEA.
Resampling Errors of Particle Filtering in Correlation-Extreme Navigation of UAV by Relief Field

Resampling techniques for particle filtering in correlation-extreme navigation are investigated. There are several resampling techniques. Among them the following ones are considered in correlation-extreme navigation: multinomial, residual, stratified and systematic Efficiency, computation time and convergence of navigation algorithm by relief field are determined based on circular trajectory of unmanned aerial vehicle. Simulation to get statistical dependencies is conducted for multinomial, residual, stratified and systematic resampling techniques.
Cam-eye: An Affordable Real-Time Video Surveillance Solution

Academia and industry have been attracted around the topic of video surveillance because of its importance to security. Using Video Surveillance, unauthorized activity can be monitored and corrected. Accidental mishaps can be recorded and later evaluated and analyzed. Event happenings can be placed in their correct chronological order. The mere fact of the existence of video surveillance discourages criminals from violent or unauthorized activity such as theft, vandalism, and various illegal felonies. We present Cam-eye, a Video Surveillance solution that is inexpensive to build and deploy. Our solution incorporates color, infrared, and thermal cameras. The live video feed can be accessed via VPN through HTTP or via our freely available iOS app. Our framework has been tested with many general purpose networked cameras as well as cameras that are built based on the Raspberry pi computers. We provide efficient software where high definition video can be transmitted in real time. We also present efficient alarm triggering algorithms that are used with our framework. These algorithms take advantage of the H.264 encoding scheme to implement otherwise computationally expensive motion detection algorithms. These algorithms can now run in real time on one of the most inexpensive single board computers such as the $10 Raspberry Pi Zero.
Improved Five-frame Difference Method and Optimized Update Rate for Codebook Target Detection

Moving object detection is one of the important applications of computer vision. How to accurately realize the separation between foreground and background is the research focus. In this paper, aiming at the problem of poor anti-interference ability of traditional codebook algorithm in complex background and the cavity-prone problem of traditional frame difference method, a pedestrian detection algorithm integrating the optimized five-frame difference method and the optimized update rate codebook is proposed. The algorithm designs a kind of double code word structure, in the process of codebook background modeling and testing process to code the code word in the book to join the update mechanism, and based on the amount of foreground points to automatically change background update rate. This design also implements an improved algorithm of double code word book YUV color space. It is used to realize the function of realtime updating background. Then, Canny edge detection operator is added to the traditional five-frame difference method to remove the noise. At the same time, the extracted rectangular area is used for filling operation and morphological processing to obtain the corresponding foreground image. Finally, the foreground image of codebook model and the foreground image of frame difference method are calculated with “and”, “different or” and “or” of pixels to obtain the precise region of moving target. The experimental results show that this algorithm combines the advantages of the two methods and abandons the disadvantages of each method in the detection prospect. It can accurately detect moving targets and has good robustness. The effectiveness of this algorithm is also verified by experiments.
Quantitative and Ontology-Based Comparison of Explanations for Image Classification

Deep Learning models have recently achieved incredible performances in the Computer Vision field and are being deployed in an ever-growing range of real-life scenarios. Since they do not intrinsically provide insights of their inner decision processes, the field of eXplainable Artificial Intelligence emerged. Different XAI techniques have already been proposed, but the existing literature lacks methods to quantitatively compare different explanations, and in particular the semantic component is systematically overlooked. In this paper we introduce quantitative and ontology-based techniques and metrics in order to enrich and compare different explanations and XAI algorithms.
Speaker Recognition Based on Lightweight Neural Network for Smart Home Solutions

With the technological advancement of smart home devices, the lifestyles of people have been gradually changed. Meanwhile, speaker recognition is available in almost all smart home devices. Currently, the mainstream speaker recognition service is provided by a very deep neural network which trained on the cloud server. However, these deep neural networks are not suitable for deployment and operation on smart home devices. Aiming at this problem, in this paper, we propose a packet bottleneck method to improve SqueezeNet which has been widely used in the speaker recognition task. In the meantime, a lightweight structure named TrimNet has been designed. Besides, a model updating strategy based on transfer learning has been adopted to avoid model deteriorates due to the cold speech. The experimental results demonstrate that the proposed lightweight structure TrimNet is superior to SqueezeNet in classification accuracy, structural parameter quantity, and calculation amount. Moreover, the model updating method can increase the recognition rate of cold speech without damaging the recognition rate of other speakers.
Linguistic Knowledge Based on Attention Neural Network for Targeted Sentiment Classification

Deep learning approaches for targeted sentiment classification do not fully exploit linguistic knowledge. In this paper, we propose a Linguistic Knowledge based on Attention Neural Network (LKAN) to employ linguistic knowledge (e.g. sentiment lexicon, negation words, intensity words) to benefit targeted sentiment classification. Firstly, we extract linguistic knowledge words (e.g. sentiment lexicon, negation words, intensity words) in sentences by HowNet vocabulary. Then, we design an attention mechanism which drives the model to concentrate on such words and get a weighted combination of word embeddings as the final representation for the sentences. We evaluate our proposed approach on SemEval 2014 Task 4, whose performance as shown reaches the most advanced level.
Semantic Specialization of Distributional Word Vectors.

Distributional word vectors have become an indispensable component of most state-of-art NLP models. As a major artefact of the underlying distributional hypothesis, distributional word vector spaces conflate various paradigmatic and syntagmatic lexico-semantic relations. For example, relations such as synonymy/similarity (e.g., car-automobile) or lexical entailment (e.g., car-vehicle) often cannot be distinguished from antonymy (e.g., black-white), meronymy (e.g., car-wheel) or broader thematic relatedness (e.g., car-driver) based on the distances in the distributional vector space. This inherent property of distributional spaces often harms performance in downstream applications, since different lexico-semantic relations support different classes of NLP applications. For instance, Semantic Similarity provides guidance for Paraphrasing, Dialogue State Tracking, and Text Simplification, Lexical Entailment supports Natural Language Inference and Taxonomy Induction, whereas broader thematic relatedness yields gains for Named Entity Recognition, Parsing, and Text Classification and Retrieval.A plethora of methods have been proposed to emphasize specific lexico-semantic relations in a reshaped (i.e., specialized) vector space. A common solution is to move beyond purely unsupervised word representation learning and include external lexico-semantic knowledge, in a process commonly referred to as semantic specialization. In this tutorial, we provide a thorough overview of specialization methods, covering: 1) joint specialization methods, which augment distributional learning objectives with external linguistic constraints, 2) post-processing retrofitting models, which fine-tune pre-trained distributional vectors to better reflect external linguistic constraints, and 3) the most recently proposed post-specialization methods that generalize the perturbations of the post-processing methods to the whole distributional space. In addition to providing a comprehensive overview of specialization methods, we will introduce the most recent developments, such as (among others): handling asymmetric relations (e.g., hypernymy-hyponymy) in Euclidean and hyperbolic spaces by accounting for vector magnitude as well as for vector distance; cross-lingual transfer of semantic specialization for languages without external lexico-semantic resources; downstream effects of specializing distributional vector spaces; injecting external knowledge into unsupervised pretraining architectures such as ELMo or BERT.
Measurement of Traffic Volume by Time Series Images Created from Horizontal Edge Segments

This paper presents the method to measure the number and speed of passing vehicles from the traffic surveillance camera. In the bird's-eye view image obtained by the inverse perspective mapping, vehicles traveling at constant speed move at constant speed which depends on the height from the road surface. Using this feature, the proposed method detects individual vehicles and calculates the vehicle speed. In the image taken from the back of the vehicle, the vehicle appears from the roof and the rear view of the vehicle appears gradually from the top to the bottom. For this reason, the proposed method detects the position of the horizontal edge segment in the bird's-eye view image and creates the time series image in which it's arranged in order of frame numbers. The trajectory of the position of the horizontal edge segment draws a straight line in the time series image when the vehicle moves at a constant speed in the measurement area. Therefore, the slope of the straight line, that is, the speed of the horizontal edge segment is calculated to separate vehicles. When the trajectory of the horizontal edge segment with higher speed appears, it's determined that a new vehicle has entered the measurement area. At this point, the number of vehicles is incremented and the speed of the vehicle is calculated from the slop of the previous trajectory. The proposed method is robust to overlap between vehicles and the sudden change in brightness. The processing speed is also lower than the video rate.
Multimodal Information Access and Retrieval Notable Work and Milestones

There is information explosion in this era; documents of all genres are available at the tip of our hands. The proliferation of digital data like images, audios and videos both on the Internet and on user's personal computers, have led the researchers across the globe to develop an efficient access and retrieval technique that could meet the real time need of the user. Since information exists in divergent modalities, therefore we are in need of a model where different modalities could be used to get access of information present in single modality. The explosion of data together with user's instantaneous need of accurate information signify that Multimodal Information Access and Retrieval techniques have emerged as a big research challenge in all domains including medical, defense, and aircraft etc. This paper introduces important hurdles encountered by MIAR systems and prominent research work which have been undertaken to address these challenges. Further it highlights problems in MIAR systems which are still not addressed. The objective of this work is to lay foundation for researchers willing to work in this area so that they could have deep knowledge of this challenging and exciting research field.
Software Defect Prediction Model Based on GA-BP Algorithm

The novel software defect prediction model based on GA-BP algorithm was proposed in the paper considering the disadvantage of traditional BP (abbreviated for Back Propagation) neural network, which has the problem of easy to fall into local optimization when constructing software defect prediction model, and finally affects the prediction accuracy. Firstly, the optimization ability of GA (abbreviated for Genetic Algorithms) is introduced to optimize the weights and thresholds of Back Propagation neural network. Then the prediction model was constructed based on the GA-BP. Meanwhile the public dataset MDP from NASA was selected and the tool WEKA was used to clean the data and format conversion and as the result, four datasets is available. In the end, experimental results show that the proposed method in the paper is effective for software defect prediction.
Quantum logic gate synthesis as a Markov decision process

Abstract Reinforcement learning has witnessed recent applications to a variety of tasks in quantum programming. The underlying assumption is that those tasks could be modeled as Markov decision processes (MDPs). Here, we investigate the feasibility of this assumption by exploring its consequences for single-qubit quantum state preparation and gate compilation. By forming discrete MDPs, we solve for the optimal policy exactly through policy iteration. We find optimal paths that correspond to the shortest possible sequence of gates to prepare a state or compile a gate, up to some target accuracy. Our method works in both the absence and presence of noise and compares favorably to other quantum compilation methods, such as the Ross–Selinger algorithm. This work provides theoretical insight into why reinforcement learning may be successfully used to find optimally short gate sequences in quantum programming.
Image Watermarking Scheme Using Cuckoo Search Algorithm

The work presented in this paper proposes a robust and optimized algorithm to be used in image watermarking. It makes use of cuckoo search algorithm (CSA) for optimization. A binary watermark is embedded within the host images in transform domain using a fitness function. The locations used for inserting the watermark bits are selected using CSA. The fitness function is a linear summation of similarity correlation coefficients SIM(W, Wʹ) obtained for four different operations performed on the watermarked image. The simulation reports indicate that the PSNR values are high enough. As a result, the watermarked images have high visual quality. The values of similarity correlation coefficient show that the scheme presented in this paper is also robust against the said operations. It can be concluded that this CSA-based scheme shows good result and also reports better results than other similar works.
Multi-task Learning by Pareto Optimality

Deep Neural Networks (DNNs) are often criticized because they lack the ability to learn more than one task at a time: Multitask Learning is an emerging research area whose aim is to overcome this issue. In this work, we introduce the Pareto Multitask Learning framework as a tool that can show how effectively a DNN is learning a shared representation common to a set of tasks. We also experimentally show that it is possible to extend the optimization process so that a single DNN simultaneously learns how to master two or more Atari games: using a single weight parameter vector, our network is able to obtain sub-optimal results for up to four games.