Reviewerly/exHarmony huggingface dataset:
Content relative thresholding technique for key frame extraction

The growth in communication methods have motivated a good number of users to migrate the existing communication methods towards video-based communications. Thus, the use of video-based communications have become the basic communication method for various fields and domains as distance education, bu siness, physical security monitoring and also in the field of news and media. The summarization process demands to extract key components from the video data in order to reduce the size of the data without compromising on any information loss. This processing is called key frame extraction process. Realizing the priority of the key frame extraction process, a few parallel research attempts were executed to match with the bottleneck of information loss and size reduction. Nevertheless, the processes were highly criticised for being time complex and sometimes for information loss. The issue with the standard or parallel methods for extraction of key frames is either high or low rate of key frame extractions, which in turn results into high size or high information loss respectively. Thus, this work aims to provide a novel key frame extraction process using the image meta data and further the adaptive thresholding method. The work demonstrates a nearly 50% reduction in time complexity with 100% accuracy of the key frame extraction process and finally a nearly 30% reduction in the key frame replication control.
An Effective Approach of Extracting Local Documents from the Distributed Representation of Text using Document Embedding and Latent Semantic Analysis

Document retrieval is the process of extracting the relevant documents on the basis of the defined query. The main problem is on finding the related document based on the local representation of the query. Here local representation means, for e.g. if you want to search for documents related to “bank”. “Bank” will have two representation “bank of a river” (distributed representation) or “Saving Bank” (local representation). But we need documents related to “Saving Bank” which belongs to the local representation of the query “bank”. This paper proposes a new model named Latent Semantic Analysis (LSA) and Document Embedding's to find the relevant documents. This is the initial attempt to combine the document embedding's vectors with LSA All neural embedding models learn distributed representation of text and match the results in the latent semantic space on a given query, but searching documents from the distributed representation will lose the relevance of local representation of a given query. We propose a novel information retrieval system, which uses doc2vec model to give top N similar documents with a relevant ranking using Latent Semantic Indexing to give the top K (documents score is greater than a soft threshold) documents which are the local representation of given query. We can use these K documents to find the most similar ones. We can show that this `dual' combination performs better than other traditional information retrieval algorithm or recently developed neural network models.
LizarMong: Excellent Key Encapsulation Mechanism Based on RLWE and RLWR

The RLWE family algorithms submitted to the NIST post-quantum cryptography standardization process have each merit in terms of security, correctness, performance, and bandwidth. However, there is no splendid algorithm in all respects. Besides, various recent studies have been published that affect security and correctness, such as side-channel attacks and error dependencies. To date, though, no algorithm has fully considered all the aspects. We propose a novel Key Encapsulation Mechanism scheme called LizarMong, which is based on RLizard. LizarMong combines the merit of each algorithm and state-of-the-art studies. As a result, it achieves up to 85% smaller bandwidth and 3.3 times faster performance compared to RLizard. Compared to the NIST’s candidate algorithms with a similar security, the bandwidth is about 5–42% smaller, and the performance is about 1.2-4.1 times faster. Also, our scheme resists the known side-channel attacks.
Improvement of Binary and Non Binary Statistical Decoding Algorithm

The security of McEliece’s cryptosystem relies heavily on the hardness of decoding a random linear code. The best known generic decoding algorithms are derived from the Information-Set Decoding (ISD) algorithm. This was first proposed in 1962 by Prange and subsequently improved in 1989 by Stern and later in 1991 by Dumer. In 2001 Al Jabri introduced a new decoding algorithm for general linear block codes which does not belong to this family, called Statistical Decoding (SD). Since then, like for the Information Set Decoding algorithm, there have been numerous work done to improve and generalize the SD algorithm. In this paper, we improve the SD algorithm using the notion of bases lists in binary case. Then, we give a non binary version of this improvement. Finally, we have computed complexity analysis and have made a complexity comparison of our results with that of recent results on SD algorithm and complexity of classic ISD algorithm.
YCbCr Color Space Based Image Segmentation Algorithm with Feedback in the Underground Cable Tunnel

In order to solve the problem that the image segmentation is affected by the intensity of illumination in the underground cable tunnel. An image segmentation algorithm with feedback based on YCbCr color space is proposed in this paper. Firstly, the color distribution characteristics of some equipment cables in the YCbCr color space is analyzed, and a threshold selection constraint for image segmentation is proposed. Oriented FAST and Rotated BRIEF (ORB) are used to feedback the matching results of the segmented images to achieve a fast feedback mechanism. Finally, the segmentation threshold is adjusted according to the feedback results, to improve the robustness of the image segmentation algorithm in the underground cable tunnel environment.
Hybrid Reasoning Configuration Method of Rescue Vehicle Based on Cases and Ruler Study

In order to respond quickly to customer needs, minimize the workload of designers, improve product design efficiency, and configure different personalized rescue vehicles according to different customer needs, a hybrid product configuration method based on case-based reasoning and rule-based reasoning is proposed. According to the design method and basic theory of the rescue vehicle, many types of multi-level rescue vehicle configuration rules were proposed. Finally, the feasibility and rationality of the method are verified by taking the demand target of a type of rescue vehicle.
Implementing Material Changes in Augmented Environments

Augmented and virtual reality technologies are becoming mature, and headsets prices are continuing to come down. Having the functionality to improve people’s live and make them more productive could help create a demand. This paper looks at one such practical application that could help designers, architects, and home buyers to better visualize home setting. This application’s focus is how to change the material of an object from one appearance to another from within the application.
CMS R-CNN: An Efficient Cascade Multi-Scale Region-based Convolutional Neural Network for Accurate 2D Small Vehicle Detection

In recent years, 2D vehicle detection has experienced impressive progress. Despite of the outstanding performance achieved by the state-of-the-art methods, it is still very challenging to detect vehicles in real scene due to the existence of small targets in images. In this work, we propose a Cascade Multi-Scale Region-based Convolutional Neural Network method (CMS R-CNN) for accurate small vehicles detection. Our method combines two key insights: (1) we propose a efficient multi-scale feature fusion network, which combines spatial information of feature maps with all scales, to enhance the ability to detect small vehicles; (2) we introduce a novel cascade framework which consists of a sequence of detectors trained with the increasing IoU thresholds strategy to further achieve better detection performance. Experiments show that our method achieves a high accuracy on the benchmark of KITTI dataset (77.0% in difficult data of KITTI) and outperforms previous work by a remarkable margin (6.2% higher than Faster R-CNN in difficult data of KITTI).
Saliency Estimation via Integration of PDDL &amp; MRF for Video Encoding (SEIPM-Video Encoding)

Recently, there are many applications that broadcast multimedia data through internet and many other ways, as the multimedia data are captured with better quality, the size of the data is getting huge. Thus, because of increase in size there are many issues occurring while broadcasting the media data. To resolve this issue, there are many compression techniques implemented for compressing the size of video by maintaining the quality of the video. The state-of-art techniques are not able to maintain the quality of the video after compressing the video as the quality of the video must be compromised when the size of the video is minimized. Thus, we are implementing a new technique naming as “Saliency Estimation using Integration of Processing-Division-of-Description-Layer and Markov-Random-Field for Video Encoding” in short known as “SEIPM-Video Encoding”. In here, we are compressing the size of the video without compromising the quality of the video. We are implementing this technique based on spatial and temporal saliency prediction by combining Processing-Division-of-Description-Layer (PDDL) and Markov-Random-Field (MRF), where PDDL is used to recognize the features that are attracting the eyes very much and MRF is used analyze the saliency frames of the video to recognize the region that has motions and movements within the frames. By recognizing the region of movement and regions that attracts eye, we only encode that region and remaining pixel is taken from the previous frame. Because of this, we get compressed size with better quality of video.
Pattern Recognition Image Color for Premature Rupture of Membranes Diagnosis Using Euclidean Algorithm

Premature rupture of membranes must be detected accurately. Inaccurate diagnosis increases the risk of caesarean section and infection for both mother and fetus. At present, diagnosis for premature rupture of membranes is performed using litmus paper. Measurement is made based on changes of color on this paper. Unfortunately, the method is subjective so the diagnosis is not accurate. It is a subjective visualization method by midwives. This research proposes a system of digital detection for premature rupture of membranes using digital image processing developed with Euclidean algorithm. The algorithm has been widely used in medical services, one of which is to help diagnose related to images. Euclidean Algorithm shows much better performance on all of the two- and three-dimensional images with variant image contents. Data processing is performed by characterizing alteration in litmus paper color into the color elements of red, green, and blue. Measurement is accurate if the device can clearly differentiate concentrations of urine, vaginal discharge, and amniotic fluid based on color resemblance pattern. Results show that accuracy of litmus paper for detection of premature rupture of membranes using Euclidean algorithm is 95%.
Performance Comparison of Two Algorithms for Arbitrary Shapes Clustering

Discovering clusters with arbitrary shapes has attracted a large number of researchers, due to its importance in analyzing real-world datasets. DENCLUE is an efficient density-based algorithm that provides a compact mathematical definition of clusters with arbitrary shapes. Several variants of DENCLUE have been proposed to enhance its performance, including DENCLUE 2.0. This study aims to discuss the difference between the two variants, DENCLUE 1.0 and DENCLUE 2.0. This will allow for future improvements on both variants and determining the best variant for specific types of datasets. The Adjusted Rand Index measure is used to evaluate the difference between the clustering results of both algorithms. The experimental results conclude that DECNLUE 1.0 outperforms DENCLUE 2.0 in discovering clusters with arbitrary shapes. Specifically, in datasets that contain clusters with multiple modes.
Using QR Code Labels to Enhance OCR for Capturing Legacy Machines' Data

Despite the maturity of optical character recognition (OCR) technology, reading data from screens such as 7 segments and LCD screens possess some challenges such as identification, position and orientation for conventional OCR to be used directly for recognizing values from screens belongs to legacy manufacturing machines. Very often, these legacy machines do not have programmable interfaces and network connectivity thus the only way to capture its data is through optical means. This work, introduces the use of QR code (Quick Respond Code) labels as marker to guide the pre-processing steps of the capture image before using conventional OCR such as Tesseract to recognize the value in the image. From the QR code image, the system can detect if the capture image is tilted or slanted. This work shows that with such information the image can be processed and attain higher accuracy.
Kamov - an Enthusiast and an Eminent Personality of Practice-Oriented Education in Irkutsk

The article reconstructs some little-known pages of life and activities of Ilya M. Kamov (1873–1942), an outstanding practice-oriented educator of Irkutsk of the first quarter of the 20th century. The authors focus their attention on «the works and days» of the educator related to Irkutsk Industrial College and also to the City Commercial College and Irkutsk Economic and Industry Institute founded on its basis, the director of which he was. The article reconstructs the prominent figure of a real enthusiast of public education in Russia, oriented at the practice of professional education. The content of the article goes far beyond the scope of the local historical issues developed in it. The historical events of secondary and higher professional school of the region are considered in a broader context of the epoch of the so called «pragmatic» turn of the pre- and post-revolutionary Russian education which consisted in adapting the educational policy for the needs of modernization and, more precisely, for those of the country’s industrialization. Even the professional career of the main character of the article reflects the main institutional logic of the practice oriented education development in that period, from the technical education to the commercial one with their subsequent amalgamation into industrial and economic education, on the one hand, and from secondary professional to higher education, on the other hand.
A Framework for implementing the interoperability of semantic web services: Case study

We propose a Semantic Web Services oriented architecture for the implementation of interoperability mechanisms of enterprise information systems. Our architecture is based on three layers. The first layer contains a service-oriented architecture. The second one concerns the architecture to capture the semantics of the services obtained from the first layer. It is based on the concept of ontologies and contexts, and represents a reference model for annotating Web services descriptions. The last layer is dedicated to architecture for semantic interoperability exploiting the first two layers. In this paper, we give more details of information systems interoperability via Semantic Web Services at data level by using and implementing a real case study. It is about trip planning to Japan, which consists of booking airline tickets and hotel rooms. It requires the creation and interaction of several Web services belonging to different information systems and platforms. We first compose these services, and then we show that a failure of this composition can occur because of heterogeneities in the data exchanged. This leads us to semantically describe the data of Web services to be able to solve them by means of mediation.
An immersive VR experience mode design

Vertigo is the biggest problem in the current VR experience. Research shows that the alpha and focal length properties of the panorama are the most important software factors affecting vertigo. A transformation circle mode is proposed in this paper. Setting the alpha and focal length values of the image with a computer formula, and increasing the hysteresis appropriately, reducing the mismatch between the visual input and the head movement, can greatly reduce the vertigo.
Information Compression for Intelligible Multi-Layered Neural Networks

The paper aims to propose a new type of information-theoretic method to interpret the final results of multi-layered neural networks by compressing multi-layered neural networks into ones without hidden layers, keeping good generalization performance. First, information on input patterns is augmented as much as possible to cope with the natural tendency of losing information in the process of compression. Then, multi-layered neural networks with much information content are compressed into simple networks without hidden layers by compressing connection weights or collectively treating all connection weights. With those collective weights, simple networks without hidden layers can be trained, incorporating information acquired by multi-layered neural networks with better generalization performance. The method was applied to two business data sets, where the interpretation of relations between inputs and outputs is much more important. In both cases, improved generalization performance was obtained by simple networks without hidden layers, trained with collective weights of multi-layered networks. The final connection weights tended to choose simpler and clearer linear correlations between inputs and outputs, which were easily and naturally interpreted.
Smart and Efficient Health Home System

Doing regular medicalAzibek, Balzhan checkupZhigerova, Shynar is important forObaidat, Mohamamd S. elderly population in order to stay healthy and avoid unexpected fatal consequences. Some elderly people could find health monitoring as problematical due to aging reason, therefore it is essential to develop a smart home health system. This system aims to be an affordable, cost-effective, and easy-to-use. The proposed smart health system has combined remote medical monitoring, reminding and fall detecting features. The proposed system prototype allows to observe health state of elderly people at a distance, and call an emergency by sending email notifications in case of detecting a fall or abnormal health rate vitals. Another advantage of the smart health system is safety, the design includes gas sensors and a camera that can detect occurrence of fire and unusual events (thieves entering). Moreover, the smart system includes the feature to send reminding notifications about daily routine tasks, which is crucial for elderly citizens with amnesia/memory loss. It is expected that the system saves money and time compared with in-clinic care services.
Features and Regression Techniques for Crowd Density Estimation: A Comparison

Crowd density estimation is an important problem for the security applications and it is a regression task consisting of feature extraction and regression steps. In this paper, we compare different features and regression techniques for crowd density estimation. To this end 200 images randomly selected from UCSD pedestrian dataset is used in the experiments. Experimental results show that features extracted from gray level co-occurance matrix (GLCM) gives the best performance however the selection of the regression technique depends on the performance criterion. Applying perspective normalization as a pre-processing step and feature elimination as a post-processing step considerably improve the performance.
DeepNet: A Deep Learning Architecture for Network-Based Anomaly Detection

Anomaly detection has been one of the most interesting research areas in the field of cybersecurity. Supervised anomaly detection systems have not been practical and effective enough in real-world scenarios. As a result, different unsupervised anomaly detection pipelines have gained more attention due to their effectiveness. Autoencoders are one of the most powerful unsupervised approaches which can be used to analyze complex and large-scale datasets. This study proposes a method called DeepNet, which investigates the potential of adopting an unsupervised deep learning approach by proposing an autoencoder architecture to detect network intrusion. An autoencoder approach is implemented on network-based data while taking different architectures into account. We provide a comprehensive comparison of the effectiveness of different schemes. Due to the unique methodology of autoencoders, specific methods have been suggested to evaluate the performance of proposed models. The results of this study can be used as a foundation to build a robust anomaly detection system with an unsupervised approach.
An Automatic Blind Syllable Segmentation Model Based on Bi-directional LSTM

Most of the languages in the Sino-Tibetan language family use syllables as the basic unit of speech recognition. Recently most methods of syllable segmentation extract the features of speech signals as the basis of segmentation, and some predefined thresholds are introduced to determine the boundaries. Considering that the location of a syllable boundary is not only related to its previous speech signal sequence, but also to its later speech signals, a blind syllable segmentation method based on bi-directional recurrent neural network (BRNN) is proposed, in which the pure speech signals are used as original input. In order to alleviate the gradient vanishing problem, Long Short-Term Memory (LSTM) is used as the basic neuron unit in the BRNN model. In order to calculate the loss of the model efficiently, a linear distance factor and an auxiliary label vector are introduced. The two vectors are only related to the speech signals, and can be calculated at the same time as the training set is generated. With the help of these two vectors, a loss function based on cross-entropy is designed, and the model is trained and evaluated under the speech data with a total length of 2.8 hours coming from a Bai Language speech corpora. The experimental results show that the proposed model based on bi-directional LSTM (BiLSTM) can be effectively applied to syllable segmentation without additional threshold parameters, and the detection accuracy of syllable boundaries is close to 85% with the evaluating dataset.
Setting Parameters of Inter-frame Differencing for Compressed Moving Images

Security cameras are installed in cities and facilities to detect intruders, and some of them have moving-object detection. The cameras are installed in various places for various purposes, resulting in cases where moving images may have substantial coding distortion. In this paper, we show experimental results on the setting of appropriate parameters and their conditions in inter-frame differencing and in median filtering of moving images degraded by compression such as H.264.
Correctness Evaluation of Handwritten Chinese Characters Based on Relaxation Matching

To realize intelligent teaching of Chinese characters handwriting, an ex-post evaluation technique about the correctness of handwritten Chinese characters is proposed. The basic idea contains two steps: firstly, the font matching is performed between the handwritten characters and template characters; then, feedback on the writing errors in the handwritten characters is given according to the matching results. Regarding the aspect of font matching, the relaxation matching algorithm is adopted and improved in this paper, which can obtain an accurate font correspondence at the level of stroke segment. Regarding the aspect of error feedback, we integrate the local errors accumulated in the handwritten characters, and present an appropriate feedback scale. Experiments indicate that this technique has excellent stability and wide applicability.