Reviewerly/exHarmony huggingface dataset:
The Kronecker-Vahlen theorem fails in real quadratic norm-Euclidean fields

The present paper is devoted to methods of the automatic verifying of the Kronecker-Vahlen theorem on the shortest length of the Euclidean algorithm in algebraic number fields with the infinite group of units. We provide explicit methods to prove that the Kronecker-Vahlen theorem fails in certain algebraic number field. In particular, we give a complete solution of the problem for quadratic norm-Euclidean number fields.
A Universal Protector System for most of the ELF binaries against Vulnerabilities

Over the past few decades, vulnerability mitigation measures for the Linux platform have gradually raised the threshold for exploits. However, attackers can still bypass all current vulnerability mitigations and get a remote shell. In this paper, we implemented a universal protector system consists of a common code execution framework and a universal protection code for most of the ELF binaries against vulnerabilities. The protector system can automatically insert protection code into the ELF file and modify the program execution flow dynamically. The experimental results show that the protector system can successfully defend against the vulnerability and prevent attackers from getting a remote shell on the Linux platform.
Latent Semantic Index: A Microservices Architecture

Nowadays, searching for a topic on the Internet can be a frustrating experience because of all the excessive information. Thus, a strategy for automatically classifying the results can improve user experience and work efficiency. Latent Semantic Indexing (LSI) algorithm is used to classify documents by meaning due to its effectiveness. However, there is a problem with the implementation of this algorithm. LSI is computationally intensive because the cost is directly related to the number of documents. In particular, the Singular Value Decomposition (SVD) that is mainly used in LSI is unscalable in terms of both memory and computation time. One possible solution is to use more powerful computational resources, such as multiple computing nodes. In this paper, a novel distributed architecture for the LSI algorithm is proposed. It is based on the use of microservices in a Google Cloud environment. We evaluated the performances of the proposed Cloud-based LSI, and comparison is made with standalone LSI. The results show the benefits of using distributed systems based on runtime, concurrency, and processing.
IMGCRYPTO-XOR Algorithm using MUSIC Theory for secure medical data storage in cloud

An image is a visual representation of something which consists of several pixels. Pixel is the smallest unit which has 4 values ARGB. Each component can take a value between 0 to 255 which can be manipulated to encrypt an image. In this paper we have suggested the Cryptography algorithm that uses the XOR effect on image pixels followed by an algorithm that uses music theory as its base. The encipher and decipher algorithm based on music theory.
Value Iteration Solver Networks

Value Iteration Algorithm is iterative and can't be parallelized. Computation time grows exponentially when the size of the input maps is increased. We propose UNet-RNN-Skip artificial neural network architecture that can be used to parallelize Value Iteration Algorithm results. The proposed model can solve Value Iteration problem in fewer iterations than the original algorithm and computation time increases by only a small amount when increasing the size of the input map. Fundamental UNet-RNN-Skip architecture can be used also to solve and parallelize other sequential problems. With this paper synthetic dataset of maps and generator has been published to enable further studies in mapping and path planning tasks.
Face Recognition using a greedy based warping algorithm

Recognition of faces is an important and challenging problem of a biometric system. In this paper, a greedy based warping algorithm is proposed for face recognition, which is a variant of conventional dynamic time warping (DTW) algorithm. The algorithm is designed to improve the recognition accuracy and to reduce the time complexity of DTW. Many real-time image processing applications require a solution that is near-optimal. The proposed algorithm provides a near-optimal solution to face recognition applications with linear time and space complexity and gives a better recognition rate. In this, matching of test face image to a set of gallery face images is performed. The performance of the greedy based algorithm has been evaluated by the experimentation on standard face databases such as ORL and FEI. Experimental results show that the proposed algorithm performs well even if the face images have uncontrolled conditions such as expression, lighting, and pose variations.
Mining Comprehensible Classification Rules with Metaheuristic Strategy: AntMiner

In mining, classification rules from knowledge-based discovery and expert systems. Nature-inspired algorithms are gaining popularity these days due to their metaheuristic approach used in discovering classification models in the sphere of data-mining. This paper explores an experimental study on six datasets by implementing AntMiner. Simulation study is carried out by comparing AntMiner with existing rule creation algorithmic strategies (JRIP and PART). NP-test are conducted for evaluating the performance measures of various algorithms in mining rules of classifiers and leads to the fact that cAntMiner-2 as well as cAntminer-PBoutperform over JRIP and PART algorithms.
Artificial Intelligence based Semantic Text Similarity for RAP Lyrics

Data mining is the primary method of gathering large volumes of knowledge. To make use of such data to implementation requires the use of effective machine learning strategies. Semantic Textual Similarity is one of the primary machine learning strategies. At its core, semantic textual similarity is the identification of words with similar context. Initial work in STS involved text reuse, word search among others. The proposed research work uses a specific method of determining textual similarity using Google's Word2Vec framework and the Continuous-bag-of-words algorithm for identifying word similarity in rap records. A large data set consisting of over 50,000 rap records is used. The key aspect of proposed methodology is to determine the words with similar context and cluster them into different word clusters also called bags. To achieve the desired result, the dataset is first processed to obtain the features. Once the features are selected, a model is generated by passing the data onto the Word2Vec framework. The research work on semantic textual similarity was repeated across three different runs, with the data set size changing in every run. At the end of each the accuracy of similarity obtained by the model was determined. The current research work has achieved average accuracy as 85%.
Deep Learning-Based Automatic Classification of Slide Frames and Presenter Frames

Online learning has become very popular among the current generation. In many of the online learning contents, the only shared content is only the video which has the presenter making the presentation interspersed with slides from the presentation. Presentation slides or lesson notes are sometimes not shared or not available. Our current work explores automatic lesson notes creation from education lecture videos. The final goal is to make presentation slides of the lecture content from the video. As a first step in this regard, we currently explore automatic classification of video frames as either being slide frames or presenter frames. We have used a pre-trained deep neural network—ResNet trained on Imagenet. Our data is frames generated from cs231n Stanford course. We had retrained the fully connected layers of our model on our data. Our experiment results show that we are able to classify slide frames and presenter frames with 98.8% accuracy with as less as 20% of our datasets as training. Our approach is promising in getting to the final goal of automatically creating presentation slides from lecture videos.
Operating System Process Using Message Passing Concept in Military

In Robotics Operating System Process correspondence is the instrument given by the working framework that enables procedures to speak with one another Message passing model enables different procedures to peruse and compose information to the message line without being associated with one another, messages going between Robots. ROS is intended to be an inexactly coupled framework where a procedure is known as a hub and each hub ought to be answerable for one assignment. In the military application robots will go to go about as an officer and going ensure nation. In the referenced idea robot solider will give the message passing idea then the officers will go caution and start assaulting on the foes.
A Robust and Fast Template Matching Algorithm

Template matching is an important method in the target recognition process. In order to improve the matching speed, recognition accuracy and algorithm robustness, a robust and fast template matching algorithm is proposed. The algorithm uses the composite morphological filters to filter out noise in gray scale images, and uses a novel low pass filter to further filter out noise in the image. The improved weighted Hamming Distance is used to detect the similarity of the image to reduce the complexity of the algorithm; the threshold is used to speed up the algorithm. Compared with other fast template matching algorithms, the experimental results show that the algorithm has good recognition accuracy in the target recognition process, which not only speeds up the running speed of the algorithm, but also has good robustness to noise, thus proving the effectiveness of the algorithm in the target recognition.
Three-dimensional Measurement of Skin Displacement

We propose a method of measuring threedimensional skin displacement when a finger traces a texture. We combine the techniques of index matching and stereoscopy; the former makes the textured plate virtually transparent while the latter allows three-dimensional measurement of markers on the skin. We confirm that the proposed method can observe skin displacement in normal and tangential directions when the pitch of texture is larger than 2 mm. Observation results are reasonable in that the progress of a sinusoidal wave is observed on the skin when the texture is sinusoidal, and a displacement with the same period is observed when the texture is a rectangular linear grating. Moreover, we observe that the skin displacement in the skin normal direction may have a higher resolution or amplitude than that in the tangential direction. Limitations of the proposed method are also discussed.
A Hybrid Approach for Detection of Forgery in Video

Due to digitalization, the information which was in structured form and handled by only technical people has now shifted in a larger way to unstructured form which can be used by all. This increased the use of multimedia elements like images and videos in number of applications. Adversely this gave rise to image and video forgery. The image forgery problem was found to be well addressed as a large number of research activities have being found in the literature. Currently video usage has been greatly enhanced in various fields due to multimedia systems. Unfortunately sufficient work was not found in the literature for video forgery detection. Thus this field remained unexposed. Working on these lines, this research work mainly focus on Video forgery detection with an enhanced feature of its elimination. Machine learning is used as the working technology alone with image processing to explore the motion parameters and to extract the required features. This research work also goes one step further to identify the forge object so as to enable its removal. This work is put forward as system design approach in which the methods implemented are evaluated qualitatively and quantitatively so as to provide for a graphical comparative analysis.
On the k-error Linear Complexity of Subsequences of d-ary Sidel’nikov Sequences Over Prime Field 𝔽d

We study the [Formula: see text]-error linear complexity of subsequences of the [Formula: see text]-ary Sidel’nikov sequences over the prime field [Formula: see text]. A general lower bound for the [Formula: see text]-error linear complexity is given. For several special periods, we show that these sequences have large [Formula: see text]-error linear complexity.
Preventing fraud victimisation in rural areas

Fraud presents itself as a significant challenge in the twenty-first century. While there is an emerging body of work on fraud (with a focus on how technologies such as the internet are transforming both offending and victimisation), to date little attention has been paid to any differences in how fraud is experienced across rural communities compared to those who live in urban centres. Certainly geography is no longer a protective factor in limiting potential victimisation, given the prevalence of the internet and the ability of those in geographically remote areas to communicate at speed with others globally. This chapter explores what is known about fraud, and provides insights into how a rural context impacts on fraud victimisation, pointing to ways in which responses to fraud victims who live in rural environments can be improved. A serving police officer from a rural Canadian locality provides a practitioner perspective, illustrating the complexities of fraud in a rural setting.
Design of Controllers

The primary purpose of vehicle guidance is to make the vehicles to follow a predefined path. To achieve this, we need to design controllers that are able to steer and propel the vehicles along the predefined paths while minimizing the path tracking errors. Among the errors to be minimized are; lateral path offsets, heading offsets and in articulated vehicles coupled to active trailers, the longitudinal coupling forces.
Application of Neural Networks and Lifting Wavelet Transform for Long Term Solar Radiation Power Prediction

In the past research scholars, have tried to predict solar radiation for a short duration for power prediction. However, with increase in applications requiring predicted data for solar radiation need for long duration prediction has increased. Present work carried out by application of concurrent Neural Networks and lifting wavelet transform for long-term power prediction. A brief review of Neural Networks and second-generation wavelets carried out for their application in long-term solar radiation power generation. Algorithm developed and experiments conducted with results presented for long-term prediction of solar radiation. Results produced present quite accurate prediction for a duration up to three months.
Neural Machine Translation Using Multiple Back-translation Generated by Sampling

Alarge-scaleparallelcorpusisindispensabletotrainencoder-decoderneuralmachinetranslation. Themethod of using synthetic parallel texts, called back-translation, in which target monolingual sentences are automatically translated into the source language, has been proven effective in improving the decoder. However, it does not necessarily help enhance the encoder. In this paper, we propose a method that enhances not only the decoder but also the encoder using target monolingual corpora by generating multiple source sentences via sampling-based sequence generation. The source sentences generated in that way increase their diversity and thus help make the encoder robust. Ourexperimentalresultsshowthatthetranslationqualitywasimprovedbyincreasingthenumberofsynthetic source sentences for each given target sentence. Even though the quality did not reach to the one that realized with a genuine parallel corpus comprising single human translations, our proposed method derived over 50% of the improvementbroughtbytheparallelcorpususingonlyitstargetside, i.e., monolingualdata. Moreover,theproposed samplingmethodresultedinﬁnaltranslationofhigherqualitythann-bestback-translation. Theseresultsindicatethat not only the quality of back-translation but also the diversity of synthetic source sentences is crucial.
Analysis of Parallelism in Deep CNNs

Convolutional Neural Network architectures have undergone a transformation from traditional sequential like models to more complex parallel execution blocks. The design of these modern architectures opens up new opportunities for inter operator parallelization. However, obtaining optimal parallel speedup across a wide range of model types requires several iterations of intense fine tuning and analysis. In this paper, we analyze the role played by model architectures in the overall speedup obtained during parallel execution. Our analysis gives rise to a set of simple generic guidelines that can be followed to achieve optimal speedup across different model architectures.
Spatial–temporal representation for video re‐identification via key images

Video‐based person re‐identification aims to verify the pedestrian identity from image sequences. The sequences are captured by cameras located in different directions at different times. Existing studies have certain limitations in the case of occlusions and pose variations. To solve the aforementioned problems, this study proposes a new two‐stage framework, from which the key‐image‐based fusion spatial–temporal feature (KISTF) of the pedestrian can be extracted from the video. The image‐level features at all timestamps are aggregated into the sequence‐level feature representation of the video by using an long short‐term memory network. Additionally, the concept of key image is defined for the image sequence, and the frame‐level feature of the pedestrian is extracted from these key images. The proposed spatial–temporal feature, KISTF, is obtained by fusing the sequence‐level feature and the frame‐level feature. It aims to solve the problem of pedestrian representation in small video data sets. Experiments are conducted on the iLIDS‐VID and PRID2011 data sets. The results demonstrate that the proposed approach outperforms state‐of‐the‐art video‐based re‐identification methods.
Towards a Domain-Specific Deep Learning Models for Medical Image Analysis

Deep learning models are most promising computational networks for medical image analysis. This is because traditional machine learning algorithms becomes inefficient as the volume of medical images increases. Transfer learning - a deep neural network models techniques is used due to the unavailability of ground truth annotated medical images. Features transferred from non-medical data are not very compatible with medical image descriptive features because medical images contain structural contents that are different from natural objects. Hence, there is need for an alternative to transfer learning in developing medical image analysis deep learning models.
Automatic Flat-Level Circuit Generation with Genetic Algorithms

This paper describes a novel methodology to generate analog and digital circuits, autonomously, using the transistor (or other elementary device, e.g. resistor) as the basic elementary block – flat-level. A genetic algorithm is employed as the generation engine and variable length chromosomes are used to describe the circuit topology that evolves during the search. The circuit devices type and sizing are described by each gene of genetic algorithm. The automatic process starts with the circuit input and output specifications, and proceeds with the circuit topology and sizing evolution to meet those specifications, eventually, ending up with a novel topology. During the evolution, each generated circuit is electrically evaluated by a spice-like circuit simulator, i.e. Ngspice, using full model specifications - like BSIM3 for transistors - in a highly parallelized architecture built over a multi-thread model.
Filter-wise Pruning Approach to FPGA Implementation of Fully Convolutional Network for Semantic Segmentation

This paper presents a hardware-aware sparse fully convolutional network (SFCN) for semantic segmentation on an FPGA. Semantic segmentation attracts interest since for self-driving car it is important to recognize road and obstacles in pixel level. However, it is hard to implement the system on embedded systems since the number of weights for the SFCN is so large that embedded systems cannot store them using limited on-chip memory. To realize good a trade-off between speed and accuracy, we construct an AlexNet-based SFCN which has no skip connections and deconvolution layers to reduce the computation costs and the latency. Furthermore, we propose a filter-wise pruning technique that sorts the weights of each filter by their absolute values and prunes them by a preset percent filter-by-filter from a small order. It is more suitable for the hardware implementation since the number of computation of each filter becomes equal. We trained the AlexNet-based SFCN by using Camvid image dataset and implemented on Xilinx zcu102 evaluation board. The results show that the FPGA system is 10.14 times faster than a mobile GPU one, and its performance per power consumption is 24.49 times higher than the GPU counterpart.
Generating Functional Responses Based on Interpretable Variable Space

How to generate responses with different sentence functions is the most challenging task in the area of dialogue systems. Conventional Sequence-to-Sequence (Seq2seq) model cannot generate sentences with different functions based on the same context by using beam search. In this paper, we design a new model to solve this problem. Our model combines an Autoencoder (AE) with a Seq2seq model by sharing the same decoder. It encodes posts and responses into different space variables and restructures the responses respectively. Besides, we introduce a latent variable to extract sentence function and Triplet loss to make variable space interpretable. The results show that our model has the ability to generate different sentences based on the target functional factor and reach a high degree of fluency and diversity in responses.
Detection Of A Target Track Loss Based On The Shiryaev Rule Using Plots Amplitude Information

An important task of the radar data processing is target track loss detection, after which it is reset. It acquires special significance at small signal-to-noise ratio. The presence of plots amplitude information allows to increase the efficiency of target detection and target tracking in the presence of interference. In the work a sequential algorithm for target track loss detection using the plots amplitude information based on the Shiryaev rule is obtained. Moreover, the method of probabilistic data association is used to estimate target's movement parameters at small signal-to-noise ratio. The analysis of the obtained algorithm efficiency is performed on a model example of target tracking due to the survey radar which measures range and range rate using statistical modeling.
FACE RECOGNITION WITH HYBRID TECHNIQUES

Face recognition framework is still in test by numerous applications particularly in close perception and in security frameworks. Generally all utilizations of face recognition utilize enormous information sets, making challenges in present time preparing and effectiveness. This paper contains a structure to enhance face recognition framework which have a few phases. For good result in face recognition framework a few upgrades are critical at each stage. A novel plan is displayed in this paper which gives the better execution for face recognition framework. This plan incorporates expanding in datasets, particularly huge datasets which are required for profound learning. Changing the picture differentiate proportion and pivoting the picture at a few edges which can enhance the recognition precision. At that point, trimming the proper territory of face for highlight extraction and getting the best element vector for face recognition finally. The last after effect of this plan will demonstrate that the given structure is able for distinguishing and perceiving faces with various postures, foundations, and appearance in genuine or present time.