Reviewerly/exHarmony huggingface dataset:
Performance Improvement of Parallel Programming Model Based on Parameterized Pipelined Map Reduce Approach

Now a day's processing huge amount of data open challenge in web resources. Map Reduce Programming model is solution to this problem. This framework is useful to compute distributed batch of jobs. Output of Mapper and Reducer simplifies fault tolerance problem. We proposed an improved version of the Map Reduce programming model called as Parameterized Pipelined Map Reduce. This model of parameterized pipelined map reduce is used as solution the problems of information recovery. Parameterized pipelined Map Reduce permits data transfer by pipeline with some timing parameter among the processes, growing the batched Map Reduce programming model. Here important thing is obtaining that parameter from mapper, this is done through different policies named as letter based policy, word length policy, sentence based policy, job based policy and analysis based policy. This technique will improve system utilization rate as well as reduce the completion time of the job. In our proposed work we directly send parameter to mapper and reducer. Our result shows 25% performance of system improved.
A New Global Path Planning Strategy for Mobile Robots Using Hierarchical Topology Map and Safety-Aware Navigation Speed

This paper presents an efficient global path planning method for a mobile robot using a new skeleton representation of a map. For practical consideration of both safety and agility of the robot in narrow corridors, we introduce the concept of allowable speed at each point of free space of the map based on the distance to the closest obstacle. We define a new cost-to-go function corresponding to the allowable speed in a time-relevant way that eventually results in a safe time-minimized path. To realize the main idea, we built a hierarchical graph representation of a map using the skeleton that has the topology of undirected multigraph at the higher level and corresponding metric points at the lower level. The cost-to-go of the metric points are added up to construct the weight values of the edges of the graph. Then, the safe time-minimized path can be efficiently computed by obtaining optimal topological path from the higher level representation and identifying the corresponding metric points from the lower level. Using this approach, a safe and agile path can be found, which provides a good trade-off between the shortest path criterion and the admissible navigation speed based on lateral safety margins. The proposed method offers enhanced performance and less computational cost. We demonstrate the advantages of the proposed method through simulation results using a real map, where we also compared its performance with that of the conventional shortest path strategy.
A Comparative Review on the Agile Tenets in the IT Service Management and the Software Engineering Domains

A rigor-oriented paradigm, in the Information Technology Service Management (ITSM) domain, has permeated in multiple international organizations. In contrast, in the Software Engineering (SwE) domain, the agile paradigm has complemented or replaced the rigor-oriented one in the last two decades. Recently shortened ITSM methods (i.e. FitSM and IT4IT) have emerged. However, due to their novelty, there is a scarce analysis of literature on their main assumed agile tenets. In this study, we identified and compared such tenets (i.e. aims, values, and principles) of the agile SwE and ITSM paradigms as well as the adherence to them from a representative literature on agile SwE and ITSM methods. Our results identified a high and low adherence respectively in the SwE and ITSM methods. Thus, a call for a robust theoretical foundation on agile tenets like those found in the SwE methods is required for the ITSM methods.
Usability Technical Debt in Software Projects: A Multi-Case Study

Background: Over the years, several studies were conducted aiming at understanding the Technical Debt (TD) phenomenon and its implications on software development. Most of these studies focus on source code related TD types. The absence of empirical studies on usability debt motivated our research. Aims: The goal of this paper is to provide an initial usability debt characterization in software projects regarding its occurrence, type, and resolution effort. Method: We conducted a multi-case study, analyzing TD items of five software projects from four different companies. Results: We identified and classified 145 TD items in the projects. The analysis of these items allowed us to observe that the frequency of usability TD items ranged from 10.4% to 20.8% in the projects. The usability debt items cover a range of usability issues, violating eight out of the ten Nielsen usability heuristics. Regarding effort for paying the TD, usability debt items require a relatively low effort, ranging from 5.1% to 6.7% of the total TD resolution effort in the analyzed projects. Conclusions: Usability TD items are frequent, concern relevant usability issues and typically require low effort for their payment. Hence, paying this type of TD should receive high priority in TD management strategies.
ANFIS Technique to Estimate Daily Global Solar Radiation by Day in Southern Algeria

In this paper, the performance and accuracy of the adaptive neuro-fuzzy inference system (ANFIS) technique is assessed for estimating the daily horizontal global solar radiation H in South Algeria. 21 years of experimental data for three cities in South Algeria Adrar, Timimoun and Bordj Badji Mokhtar (BBM) were used to train and test the ANFIS model. The results verified that the ANFIS model provides accurate predictions based on statistical formulas such as: the root mean square error (RMSE), mean absolute percentage error (MAPE and the coefficient of determination (R). In a nutshell, the results highly encouraged the implementation of ANFIS to estimate the daily horizontal global solar radiation using only the number of the day of years nd.
Combining Qualitative and Quantitative Analysis in Football with SportSense

The task of performance analysts and coaches in football (and other team sports) is manifold: they need to assess the performance of individual players of their team, they need to monitor the interaction between players of their team and their tactical compliance, and they need to analyze other teams. For this, they usually have to consider various sources of information: video footage, tracking data, event data, and aggregated statistics. On the basis of this information, analysts have to generate quantitative summaries of events including their spatial and temporal distribution, and the qualitative assessment of individual events by considering the associated video footage. In this paper, we present SportSense, a system for sports video retrieval, that seamlessly combines quantitative and qualitative analysis. For this, SportSense provides dedicated filters that help analysts in selecting the events they are interested in. Moreover, it supports the comparative analysis of stored queries with respect to specific parameters. Essentially, SportSense allows to easily switch between qualitative and quantitative analyses to support coaches and analysts in a best possible way in their task. Based on a user study, we show the effectiveness of the proposed approach.
ERCNN: Enhanced Recurrent Convolutional Neural Networks for Learning Sentence Similarity

Learning the similarity between sentences is made difficult by the fact that two sentences which are semantically related may not contain any words in common limited to the length. Recently, there have been a variety kind of deep learning models which are used to solve the sentence similarity problem. In this paper we propose a new model which utilizes enhanced recurrent convolutional neural network (ERCNN) to capture more fine-grained features and the interactive effects of keypoints in two sentences to learn sentence similarity. With less computational complexity, our model yields state-of-the-art improvement compared with other baseline models in paraphrase identification task on the Ant Financial competition dataset.
Generating Objective Summaries of Sports Matches Using Social Media

Social media has become a platform where users post their messages about a wide range of topics, making it a useful source of information to summarize events such as sports matches. Previous summaries of sports matches generated using social media tended to be biased towards one of the teams, due to a high proportion of the posts used being from fans of the teams involved. This is problematic because in general people desire summaries that are free from bias and objective. To remedy this problem and generate higher quality summaries, we propose two approaches. The first is a function maximization method which measures the objectivity of each post based on its constituent words. The second is a neural network-based approach, where we use an encoder-decoder architecture. Then, we compare them with an existing approach and show promising results that indicate the effectiveness of our methods.
Separation of Basic Words in Angkola Batak Text Documents using Enhanced Confix Stripping Stemmer Case: Mandailing Ethnic

Abstract Stemming is the process of separating essential words from an affixed word. Stemming works by eliminating morphological variations which attached to a word by removing affixes in a word with a dictionary as a reference to the stemming process. One effective algorithm for resolving stemming in Indonesian is an Enhanced Confix Stripping Stemmer algorithm. In this case, the improved Confix stripping stemmer algorithm is implemented for the separation of essential words in the Angola-Mandailing Batak language document that has different phonologies and morphologies with Indonesian. The documents used are Latin character documents in the Angkola-Mandailing language that goes through the stages of filtering, folding cases, and tokenization before stemming. The stemming process is done by removing particles, ownership, suffixes, and prefixes. After testing this research, it was concluded that this algorithm was able to separate essential words in the Angkola-Mandailing Batak language with accuracy 87,05%.
Fusing Optical Flow and Inertial Data for UAV Motion Estimation in GPS-denied Environment

In this paper, a solution for estimating the motion of unmanned aerial vehicle (UAV) in lacking of GPS environment is proposed. First, interframe difference method is used to detect the point of interesting in adjacent frames. Then Pyramid Lucas-Kanade (Pyr-LK) algorithm is used to calculate optical flow of the point of interesting. Furthermore, mean-shift algorithm is applied to improve the accuracy of optical flow. Finally, the optical flow and Inertial Measurement Unit (IMU) data are fused based on Extended Kalman Filter (EKF) to estimate velocity. Flight test is conducted, and the comparison experiment results of our method with the differential GPS which can provide centimeter-level positioning data show that the proposed method can estimate the motion velocity of UAV precisely.
Avoiding Risky Designs When Using Blockchain Technologies in Cyber-Physical Systems

Blockchain has been hailed as an emerging technology with the potential to cause significant impact in a variety of fields. One domain is an application in cyber-physical systems (CPSs), e.g., as a building block for the (Industrial) Internet of Things (IIoT) and Industry 4.0. In this regard, various use cases and designs have been proposed that seek to leverage the desirable properties blockchain technologies seem to offer. While many of the principles behind blockchain have actually been studied under the veil of Byzantine fault tolerance for decades, some approaches, such as relying on game-theoretic incentives and proof-of-work (PoW), are not yet fully understood. This knowledge gap can leave both practitioners and researchers in a difficult position regarding a possible application of such technologies, as it is often unclear what guarantees and characteristics a particular blockchain design actually achieves. This work-in-progress paper provides an overview of blockchain security research, outlines system designs that are likely to exhibit vulnerabilities, and provides examples of potentially insecure proposals in the field of CPSs that employ such designs.
MILP-based Related-Key Rectangle Attack and Its Application to GIFT, Khudra, MIBS

Abstract The rectangle attack is the extension of the traditional differential attack and is evolved from the boomerange attack. It has been widely used to attack several existing ciphers. In this article, we study the security of lightweight block ciphers GIFT, Khudra and MIBS against related-key rectangle attack. We use Mixed-Integer Linear Programming-aided cryptanalysis to search rectangle distinguishers by taking into account the effect of the ladder switch technique. For GIFT, we build a 19-round related-key rectangle distinguisher and attack on 23-round GIFT-64, which requires 260 chosen plaintexts and 2107 encryptions. For Khudra, a 14-round related-key rectangle distinguisher can be built, which leads us to a 17-round rectangle attack. Our attack on 17-round Khudra requires a data complexity of 262.9 chosen plaintexts and a time complexity of 273.9 encryptions. For MIBS, we construct a 13-round related-key rectangle distinguisher and propose an attack on 15-round MIBS-64 with time complexity of 259 and data complexity of 245. Compared to the previous best related-key rectangle attack, we can attack one more round on Khudra and MIBS-64 than before.
Retrieval of Similar Scenes Based on Multimodal Distance Metric Learning in Soccer Videos

This paper presents a new method for retrieval of similar scenes based on multimodal distance metric learning in far-view soccer videos that broadly capture soccer fields and are not edited. We extract visual features and audio features from soccer video clips, and we extract text features from text data corresponding to these soccer video clips. In addition, distance metric learning based on Laplacian Regularized Metric Learning is performed to calculate the distances for each kind of features. Finally, by determining the final rank by integrating these distances, we realize successful multimodal retrieval of similar scenes from query scenes of soccer video clips. Experimental results show the effectiveness of our retrieval method.
A New Hybrid Architecture for Real-Time Detection of Emergency Vehicles

VANET is a vital part of wireless networking. Vehicular movement is expanding indefinitely everywhere and is causing terrible problems to daily life. Almost all of the traffic lights now feature a fixed green light sequence and so green light sequence is determined without taking the existence of emergency vehicles into consideration. Consequently emergency vehicles such as ambulances, fire engines, police vehicles etc. are struck in traffic which might cause loss of valuable life and property. In this paper we present a new hybrid architecture for detection of emergency vehicles in real time. This hybrid architecture is based on the mixed features of image processing and machine learning. We also show the percentage decrease in the search space for the processing which results in faster detection of emergency vehicles.
Bilingual text detection in natural scene images using invariant moments

In today’s world, there have been lots of unique optical character recognition systems. One drawback of these systems is that they cannot work effectively on natural scene images where the text is not only subject to different orientations, lightning, and background but can be of multiple scripts a s well. The paper, proposes a state of the art algorithm to detect texts of different dialects and orientations in an image. The whole text detection pipeline is divided into two parts. First, extraction of probable text regions in an image is performed based on a combination of statistical filters, which results in a high recall. These regions are then fed to an Artificial Neural Networks (ANN) based classifier which classifies whether the proposed regions are text or non-text, which increases the overall precision. The validity of the algorithm is verified on the most challenging bilingual text detection dataset MSRA-TD500 and a promising F1 score of 0.67 is reported.
Photo-Realistic and Robust Inpainting of Faces Using Refinement GANs

Image inpainting faces many different challenges when filling holes or restoring damaged pixels. One of the most difficult tasks is inpainting central and eye-catching parts, where already small artifacts are fatal. Such a challenge is inpainting missing parts of human faces in portrait photographs. In this paper we propose, design and evaluate a refinement generative adversarial network (GAN), that we apply within an inpainting pipeline to robustly generate photo-realistic restorations. The key aspects of our refiner are the global consensus and the inpainting loss, that ensure close generated and damaged images. Furthermore, the content losses lead to a reliable prediction of the perception score, that examines the generated images upon their authenticity. We show typical problems of state-of-the-art semantic inpainting that omits such a global consensus loss and suffers from visually disruptive artifacts such as inpainting a mustache in a female face or inpainting disagreeing skin or eye color. We validate our method on the CelebA dataset and report four different traditional quality metrics using six different hole patterns. We conclude, that refinement considerably improves for each setting the numerical and visual quality and preserves already good restorations.
High Performance SM2 Elliptic Curve Cryptographic Processor over GF(p)

Elliptic Curve cryptography (ECC) is becoming more and more popular in practice due to their bit size of the operands is much smaller at the same level of security compared to other public-key cryptosystems. The performance of ECC is determined by modular multiplication (MM) and point multiplication (PM). For recommended prime filed, modular multiplication consists of multiplication and fast reduction operations. This paper presents a high performance ECC processor of SM2. A two-stage fast-reduction (TSFR) algorithm over SCA-256 prime field is proposed, which can obtain the result of 0 ≤ Z <; 2ρ instead of 0 ≤ Z <; 14ρ of traditional algorithm. In order to enhance the performance, the operational schedule of PM operation is designed to increase the parallelism of multiplication and fast-reduction operations. Synthesized with a 0.13 um CMOS standard cell library at the frequency of 250MHz, the proposed processor needs 276.54k gates area and PM operation takes 0.057 ms. The processor has make a tradeoff between area and performance, soit is better than other methods.
Ramp secret sharing with cheater identification in presence of rushing cheaters

Abstract Secret sharing allows one to share a piece of information among n participants in a way that only qualified subsets of participants can recover the secret whereas others cannot. Some of these participants involved may, however, want to forge their shares of the secret(s) in order to cheat other participants. Various cheater identifiable techniques have been devised in order to identify such cheaters in secret sharing schemes. On the other hand, Ramp secret sharing schemes are a practically efficient variant of usual secret sharing schemes with reduced share size and some loss in security. Ramp secret sharing schemes have many applications in secure information storage, information-theoretic private information retrieval and secret image sharing due to producing relatively smaller shares. However, to the best of our knowledge, there does not exist any cheater identifiable ramp secret sharing scheme. In this paper we define the security model for cheater identifiable ramp secret sharing schemes and provide two constructions for cheater identifiable ramp secret sharing schemes. In addition, the second construction is secure against rushing cheaters who are allowed to submit their shares during secret reconstruction after observing other participants’ responses in one round. Also, we do not make any computational assumptions for the cheaters, i.e., cheaters may be equipped with unlimited time and resources, yet, the cheating probability would be bounded above by a very small positive number.
Optimisation for image salient object detection based on semantic‐aware clustering and CRF

State‐of‐the‐art optimisation methods for salient object detection neglect that saliency maps of different images usually show different imperfections. Therefore, the saliency maps of some images cannot achieve effective optimisation. Based on the observation that the saliency maps of semantically similar images usually show similar imperfections, the authors propose an optimisation method for salient object detection based on semantic‐aware clustering and conditional random field (CRF), named CCRF. They first cluster the training images into some clusters using the image semantic features extracted by using a deep convolutional neural network model for image classification. Then for each cluster, they use a CRF to optimise the saliency maps generated by existing salient object detection methods. A grid search method is used to compute the optimal weights of the kernels of the CRF. The saliency maps of the testing images are optimised by the corresponding CRFs with the optimal weights. The experimental results with 13 typical salient object detection methods on four datasets show that the proposed CCRF algorithm can effectively improve the results of a variety of image salient object detection methods and outperforms the compared optimisation methods.
A Fast Covariance Intersection Tracking Algorithm Based on CDKF

With the enhancement of the anti-jamming ability of the target, it is difficult to track and strike the target stably and accurately by single guidance. It is necessary to use a variety of detectors as sensors to provide a variety of observation data to track the target stably and achieve accurate strike. In this paper, we mainly research the distributed multi-sensor estimation problem. First, the target motion model and observation model are established. Secondly, the central differential Kalman filter (CDKF) transformation is applied to address the non-linear filtering problem without the need for computation of Jacobian matrix. Then, a FCI fusion algorithm for the multi-sensor target tracking problem is proposed. Compared to the traditional covariance intersection fusion method, FCI is more efficient in computation without the need for complex optimization process. Finally, simulations are designed and implemented and the joint CDKF-FCI fusion estimation algorithm is validated.
Aircraft Segmentation from Remote Sensing Image by Transferring Natual Image Trained Forground Extraction CNN Model

With the rapid development of remote sensing technology, remote sensing image processing has been widely used in the field of national defense and the people's livelihood. However, due to the high cost of manually collecting and annotating data, the number of currently public remote sensing datasets are limited, and the datasets of remote sensing images for foreground segmentation and detection is also confined. In this paper, because of the difficulty of small-sized datasets, the foreground segmentation of aircraft remote sensing image is realized by using the idea of transfer learning. We implemented three kinds of different aircraft segmentation networks for small-sized aircraft remote sensing images by transferring three kinds of different foreground segmentation networks trained on large-scaled datasets. The best aircraft segmentation accuracy is up to 79.201% under custom evaluation criteria. Experiment results showed that transferring a foreground extraction CNN model trained on public datasets to cope with the task of aircraft segmentation on small aircraft remote sensing images is significantly effective.