Reviewerly/exHarmony huggingface dataset:
Efficient and Precise Secure Generalized Edit Distance and Beyond

Secure string-comparison by some non-linear metrics such as edit-distance and its variations is an important building block of many applications including patient genome matching and text-based intrusion detection. Despite the significance of these string metrics, computing them in a provably secure manner is very expensive. In this paper, we improve the performance of secure computation of these string metrics without sacrificing security, generality, composability, and accuracy. We explore a new design methodology that allows us to reduce the asymptotic cost by a factor of O(log n) (where n denotes the input string length). In our experiments, we observe up to an order-of-magnitude savings in time and bandwidth compared to the best prior results. We extended our semi-honest protocols to work in the malicious model, which is by-far the most efficient actively-secure protocols for computing these string metrics.
Restaurant Recommendation System Based on Novel Approach Using K-Means and Naïve Bayes Classifiers

Human opinion has become the most useful metric for recommendation systems in recent years. Before buying any product, we are looking for ratings. Growth in social media has provided an opportunity to know what interests likeminded people. In this paper, we present a k-means nearest neighbor and Naïve Bayes classifier-based systems for recommendation of restaurants. To make precise predictions and provide proficient recommendations, the data and method are most important factors. By thoroughly analyzing the literature, we came across the fact that the Facebook and Yelp are most successfully used datasets. While working, we realized the issues related to Yelp in searching from outcome of first search and time consumption. We chose Zomato that is most used restaurant-finding application which provides many attributes and users ratings. We have used Zomato data along with k-means and Naïve Bayes and achieved overall accuracy of 93%.
Thesis topic recommendation using simple multi attribute rating technique

Abstract The purpose of this study is to design information systems that can recommend the selection of final project (thesis) topics based on the competencies possessed by students. The analytical model integrated into this system is the Simple Multi Attribute Rating Technique (SMART), with student competency data taken based on the grade score of the subjects relevant to the selection of the final project theme clusters. The design of the information system in this study uses a prototype model, with the main stages consisting of needs analysis, design, testing, and evaluation. The system designed successfully runs well and can provide recommendations on the selection of thesis topics. This recommendation provided an overview of the strength of competencies that are superior to students what have relevance to the cluster of the thesis main topics.
Sign Language Translator using the Back Propagation Algorithm of an MLP

Although technology has been evolving rapidly in this information age, deaf/mute people still use sign language as their only way of communication. Using sign language as a communication tool can be beneficial among those who are familiar with this language, but the problem remains when communicating with the wider community. Sign Language Translator is the appropriate solution that enables deaf/mute people to communication fluently through technology in different languages. It is a combination of two gloves with the required sensors and a smart phone that contains the mobile application that translates the hand gestures into spoken words, is based on the Lebanese Sign Language; Artificial Neural Networks plays a big role in releasing the exact output.
Image stylisation: from predefined to personalised

The authors present a framework for interactive design of new image stylisations using a wide range of predefined filter blocks. Both novel and off‐the‐shelf image filtering and rendering techniques are extended and combined to allow the user to unleash their creativity to intuitively invent, modify, and tune new styles from a given set of filters. In parallel to this manual design, they propose a novel procedural approach that automatically assembles sequences of filters, leading to unique and novel styles. An important aim of the authors’ framework is to allow for interactive exploration and design, as well as to enable videos and camera streams to be stylised on the fly. In order to achieve this real‐time performance, they use the Best Linear Adaptive Enhancement (BLADE) framework – an interpretable shallow machine learning method that simulates complex filter blocks in real time. Their representative results include over a dozen styles designed using their interactive tool, a set of styles created procedurally, and new filters trained with their BLADE approach.
Graph-Based Micro-Seismic Signal Classification with an Optimised Feature Space

Classification of seismic events detected from seismic recordings has been gaining popularity in interpretation of subsurface processes, e.g., volcanic systems, earthquake activity, induced seismicity and slope stability, in particular landslides.However, due to the variability of signal representation for different classes in the temporal and spectral space, a large feature space to characterise the uniqueness of a particular type of event is used for classifying seismic signals.The consequence is additional complexity on the classifier and overfitting.So far, there has been little attempt to address dimensionality reduction via feature selection.In this paper, we propose an iterative, alternating graph feature and classifier learning method for micro-seismic signals via graph Laplacian regularization and normalized graph Laplacian regularization.Using recorded micro-seismic events from an active landslide, we demonstrate improved classification accuracy with a relatively small feature space compared to state of the art.
Classification of Web Applications Using AiFlow Features

With the increasing of complexity and volume of network traffic, more advanced methods of traffic analysis are required to continue producing useful results. Conventional methods of solving this problem include lexicographical pattern analysis, where a signature is created manually and then compared with incoming traffic in the hopes of detecting a matching signature. The main issue of such methods is its inability to adapt to even minor changes in the signature of the target application architecture. In this paper we introduce a new flow format AiFlow, designed specifically to assist in the association of traffic with application based on a wider set of criteria. This flow format coupled with a sufficient artificial intelligence (AI), could be capable of identifying both the dynamic and static elements that define the behavior of a network-enabled application. Further, the system would be equipped to adapt to the inevitable variations in application behavior over time.
Onboard Visual Horizon Detection for Unmanned Aerial Systems with Programmable Logic

We introduce and analyze a fast horizon detection algorithm with native radial distortion handling and its implementation on a low power field programmable gate array (FPGA) development board in this paper. The algorithm is suited for visual applications in an airborne environment, that is on board a small unmanned aircraft. The algorithm was designed to have low complexity because of the power consumption requirements. To keep the computational cost low, an initial guess for the horizon is used, which is provided by the attitude heading reference system of the aircraft. The camera model takes radial distortions into account, which is necessary for a wide-angle lens used in most applications. This paper presents formulae for distorted horizon lines and a gradient sampling-based resolution-independent single shot algorithm for finding a horizon with radial distortion without undistortion of the complete image. The implemented algorithm is part of our visual sense-and-avoid system, where it is used for the sky-ground separation, and the performance of the algorithm is tested on real flight data. The FPGA implementation of the horizon detection method makes it possible to add this efficient module to any FPGA-based vision system.
Research of shopping recommendation system based on improved wide-depth network

Abstract With the development of e-commerce, there are more and more commodities. How to recommend the commodities that users are interested in quickly and accurately has become an important research topic in the field of e-commerce.we propose a product recommendation algorithm based on DeepFM network. Firstly, we embed the user’s purchased products, and transform the sparse feature into the low-dimensional dense feature, while the user’s personal attribute features can express the user’s purchase intention to a certain extent, and also use embedded coding to transform the features.DeepFM considers both wide and deep (i.e. low-level and high-level) at the same time to further improve the generalization ability of the model. So we use DeepFM to predict the interest of users in purchasing goods.Learning the expression of user’s interest from user’s purchase and personal preference, so as to accurately predict user’s purchase behavior.Finally, we use the real record data set purchased by online users to evaluate the effect of the model, and compare it with other models to verify the effectiveness of the model.
Classification of Citation Sentence for Filtering Scientific References

Citation sentence is able to inform readers about relation between scientific articles that cite and are cited by finding its purpose against the research. Besides giving credit to other researchers and recommendation to read other related articles, citation can help readers to know what knowledge they have obtained based on the cited scientific articles they have read. In this research, we try to define citation categories for filtering scientific references which will be initial step in guided summarization of scientific articles. Our goal is to classify citation sentence first into `problem', `other', `useModel', `useTool' and `useData'. This category will make it easier to classify scientific articles into more specific topics. Then we use features namely voice, tenses, citation location, meta-discourse and bag of words. Then, we employ SVM Linear for building classification model and sampling technique, namely SMOTE for imbalance dataset. The best result of f-measure for our citation classification is achieved at 61.2% when combining voice& tense, meta-discourse, bag of words and sampling the feature data of UseData category with SMOTE.
A fuzzy based system for target search using top-down visual attention

Top–down influences play a major role in the primate’s visual attention mechanism. Design of top-down influences for target search problems is the recommended approach to develop better computational models. Existing top down computational visual attention models mainly exploit three factors namely the context information, target information and task demands. Here in this paper we propose a Fuzzy based System for Target Search (FSTS) which makes use of target information as the top-down factor. The system uses Fuzzy logic to predict the salient locations in an image based on the prior information about a target object to be detected in a scene or frame. The performance of the system was analysed using multiple evaluation parameters and is found to have a better average hit number, number of first hits and elapsed CPU time than the existing system. The saliency map comparison is performed with human eye fixation map and is found to predict the human fixations with better accuracy than existing systems.
Product of complete, strong, null, and regular fuzzy graphs

In this article, we extend the work of Dogra [1]. The extension work is related to types of fuzzy graphs that be used. The fuzzy graphs complete, strong, null, and regular and consider to homomorphic, box dot, and star products. The exciting result of the study is regular fuzzy graphs constructed from various products from fuzzy graphs. Homomorphic product of two fuzzy graphs complete with specific condition results to a fuzzy graph regular. Box dot product and star product of fuzzy graph complete and fuzzy graph null with specific condition results to a fuzzy graph regular. For star products, fuzzy graphs regular produced are a union of smaller regular fuzzy graphs.
Development of Curriculum Design Support System Based on Word Embedding and Terminology Extraction

The principles of computer skills have been included in primary and secondary educated since the early 2000s, and the reform of curricula is related to the development of IT. Therefore, curricula should reflect the latest technological trends and needs of society. The development of a curriculum involves the subjective judgment of a few experts or professors to extract knowledge from several similar documents. More objective extraction needs to be based on standardized terminology, and professional terminology can help build content frames for organizing curricula. The purpose of this study is to develop a smart system for extracting terms from the body of computer science (CS) knowledge and organizing knowledge areas. The extracted terms are composed of semantically similar knowledge areas, using the word2vec model. We analyzed a higher-education CS standards document and compiled a dictionary of technical terms with a hierarchical clustering structure. Based on the developed terminology dictionary, a specialized system is proposed to enhance the efficiency and objectivity of terminology extraction. The analysis of high school education courses in India and Israel using the technical term extraction system found that (1) technical terms for Software Development Fundamentals were extracted at a high rate in entry-level courses, (2) in advanced courses, the ratio of technical terms in the areas of Architecture and Organization, Programming Languages, and Software Engineering areas was high, and (3) electives that deal with advanced content had a high percentage of technical terms related to information systems.
On finding time-efficient trajectories for fixed-wing aircraft using dubins paths with multiple radii

Trajectory generation for fixed-wing aircraft can be based on Dubins vehicle model that constrains the vehicle to move forward with a limited turning radius and a constant speed. However, such a vehicle model cannot benefit from the physical capabilities of a real fixed-wing aircraft that can adjust its speed. Therefore, we propose to address the limitation of Dubins vehicle model by a generalized model that combines various turning radii, and thus allows increasing the cruise speed whenever possible. The proposed method provides faster trajectories in comparison to the trajectory generated by Dubins vehicle with a single turning radius and a constant cruise speed. The benefit of the proposed method is demonstrated on point-to-point trajectories, for which the parameters are inspired by Cessna 172 aircraft.
An effective hybrid pruning architecture of dynamic convolution for surveillance videos

The large-scale surveillance videos analysis becomes important as the development of the intelligent city; however, the heavy computational resources necessary for the state-of-the-art deep learning model makes real-time processing hard to be implemented. As the characteristic of high scene similarity generally existing in surveillance videos, we propose an effective compression architecture called dynamic convolution, which can reuse the previous feature maps to reduce the calculation amount; and combine with filter pruning to further speed up the performance. In this paper, we tested the presented method on 45 surveillance videos with various scenes. The experimental results show that the hybrid pruning architecture can reduce up to 80.4% of FLOPs while preserving the precision within 1.34% mAP; furthermore, the method can improve the processing speed up to 2.8 times compared to the traditional Single Shot MultiBox Detection.
Semi-Supervised Multi-Granularity CNNs for Text Classification: An Application in Human-Car Interaction

With the rapid growth of advanced driving assistance technology, how to implement humancar interaction humanely and personally becomes increasingly important.Existing methods typically rely on hand-crafted data to conduct training, which cannot fully extract information hidden in text.In this paper, we tackle the problem of label data acquisition difficulties and inadequate feature extraction via semi-supervised learning and multi-granularity learning.To fulfill the goal of label data acquisition and feature extraction, we present a novel semi-supervised multi-granularity convolutional neural networks (CNNs)-based (SSMGCNNs-based) model for an application in human-car interaction, which consists of the two-view-embedding (TVE) module and the multi-granularity CNNs (MGCNNs) module.The TVE module learns embeddings of text regions from the unlabeled user command data set and then integrate the learned tv-embeddings into MGCNNs, so that the learned tv-embedded regions are used as an additional input into MGCNNs' convolution layer to solve the problem of data annotation.MGCNNs can fully extract information hidden in text by multiple convolution kernels of the same convolution layer.We compared our model with some state-of-the-art machine learning models.On the car operation command data set, the simulation results demonstrated that, compared with CNNs, our method respectively improved 5.13%, 5.64%, 3.60% and 5.34% on precision, recall, F-1 and training loss.INDEX TERMS Convolutional neural networks (CNNs), human-car interaction, two
Parameters of the strategy for managing the economic growth of agricultural production in Russia

Agricultural economic growth requires management due to poor structurization.The study aimed to determine the parameters of the management strategy for the economic growth of agriculture in Russia.The research methodology relies on cognitive technologies of modelling the strategic alternatives of the economic development of the industrial complex using fuzzy cognitive logic.Static and dynamic analysis of the fuzzy cognitive maps on structural and dynamic indicators of agricultural economic growth in Russia allowed the forecast of the industry trends, influenced by various management factors.The option of an integrated management strategy for the economic growth of agriculture in Russia is proposed together with strategic maps, justified as a tool for its implementation.The created strategic alternative will allow the Russian agricultural and industrial complex to use the existing agricultural potential to achieve the target growth indicators and ensure sustainability.
Fall detection for elderly-people monitoring using learned features and recurrent neural networks

Abstract Elderly care is becoming a relevant issue with the increase of population ageing. Fall injuries, with their impact on social and healthcare cost, represent one of the biggest concerns over the years. Researchers are focusing their attention on several fall-detection algorithms. In this paper, we present a deep-learning solution for automatic fall detection from RGB videos. The proposed approach achieved a mean recall of 0.916, prompting the possibility of translating this approach in the actual monitoring practice. Moreover to enable the scientific community making research on the topic the dataset used for our experiments will be released. This could enhance elderly people safety and quality of life, attenuating risks during elderly activities of daily living with reduced healthcare costs as a final result.
LMDAPNet: A Novel Manifold-Based Deep Learning Network

In this paper, we propose a novel deep learning network, called Local Manifold Discriminant Analysis Projection Network (LMDAPNet).Different from most existing face recognition based on Deep Neural Networks (DNNs) methods that learn complex feature representations of data itself, our method aims to exploit the discriminant and geometrical structure of data manifold by optimally preserving the local neighborhood information.Firstly, it learns the local discriminant embedding for the submanifold of each class by solving an optimization problem.Secondly, we propose a Local Manifold Discriminant Analysis Projection (LMDAP) algorithm to learn convolutional kernel based on the local discriminant embedding space.Meanwhile, we define a new subspace-to-subspace distance metric to measure the dissimilarity between manifold pair.Finally, the learned convolutional kernel is used to extract feature maps efficiently.After a series of feature extraction, multiscale feature analysis is to encode the feature maps before feeding into a classifier.Experimental results on four benchmark datasets show that our proposed network is more robust and competitive performance for face recognition.
A Multi-objective Bat Algorithm for Software Defect Prediction

Both the class imbalance of datasets and parameter selection of support vector machine (SVM) play an important role in the process of software defect prediction. To solve these two problems synchronously, the false positive rate (pf) and the probability of detection (pd) are considered as two objective functions to construct the multi-objective software defect prediction model in this paper. Meanwhile, a multi-objective bat algorithm (MOBA) is designed to solve this model. The individual update strategy in the population is performed using the individual update method in the fast triangle flip bat algorithm, and the non-dominated solution set is used to save the better individuals of the non-defective module and the support vector machine parameters. The simulation results show that MOBA can effectively save resource consumption and improve the quality of software compared with other commonly used algorithms.
On the Relation Between Loss Functions and T-Norms

Deep learning has been shown to achieve impressive results in several domains like computer vision and natural language processing. A key element of this success has been the development of new loss functions, like the popular cross-entropy loss, which has been shown to provide faster convergence and to reduce the vanishing gradient problem in very deep structures. While the cross-entropy loss is usually justified from a probabilistic perspective, this paper shows an alternative and more direct interpretation of this loss in terms of t-norms and their associated generator functions, and derives a general relation between loss functions and t-norms. In particular, the presented work shows intriguing results leading to the development of a novel class of loss functions. These losses can be exploited in any supervised learning task and which could lead to faster convergence rates that the commonly employed cross-entropy loss.