Reviewerly/exHarmony huggingface dataset:
ScrumOntoBDD: Agile software development based on scrum, ontologies and behaviour-driven development

Abstract When developing a Learning Management System (LMS) using Scrum, we noticed that it was quite often necessary to redefine some system behaviour scenarios, due to ambiguities in the requirement specifications, or due to misinterpretations of stories reported by the Product Owners (POs). The definition of test suites was also cumbersome, resulting in test suites that were incomplete or did not at all comply with the system requirements. Based on this experience and to deal with these problems, in this paper, we propose the ScrumOntoBDD approach to agile software development, which combines Scrum, ontologies and Behaviour-Driven Development (BDD). This approach is centred on the concepts and techniques of Scrum and BDD and focuses on the planning and analysis phases of the software life cycle, since the BDD tools currently provide little support to these phases, while most of the problems during the LMS development were found exactly there. We claim that our approach improves the software development practices in this respect. Furthermore, ScrumOntoBDD employs ontologies in order to reduce ambiguities intrinsic to the use of a natural language as a BDD ubiquitous language. In this paper, we illustrate and systematically evaluate our approach, showing that it is beneficial since it improves the communication between members of an agile development team.
A Framework Using Contrastive Learning for Classification with Noisy Labels

We propose a framework using contrastive learning as a pre-training task to perform image classification in the presence of noisy labels. Recent strategies, such as pseudo-labeling, sample selection with Gaussian Mixture models, and weighted supervised contrastive learning have, been combined into a fine-tuning phase following the pre-training. In this paper, we provide an extensive empirical study showing that a preliminary contrastive learning step brings a significant gain in performance when using different loss functions: non robust, robust, and early-learning regularized. Our experiments performed on standard benchmarks and real-world datasets demonstrate that: (i) the contrastive pre-training increases the robustness of any loss function to noisy labels and (ii) the additional fine-tuning phase can further improve accuracy, but at the cost of additional complexity.
EDF-SSD: An Improved Feature Fused SSD for Object Detection

Single Shot Multibox Detector (SSD), which is considered one of the top prominent algorithms for object detection in terms of speed and accuracy. However, in the feature pyramid, conventional SSD uses every layer individualistically and ignores the background information of the objects by taking only the fine-grained details of the objects into account, which drops its accuracy in certain cases especially in heavy occlusion, objects overlapping and small objects. It is a known fact that if the number of feature maps is increased, the efficiency of a deep network is considered to be increased, but just simply rising the number of feature maps will not bring any improvement. Regarding the lack of feature complementarity in conventional SSD feature layers and its low accuracy in certain circumstances, this paper proposed a multi-scale strategy to combine feature maps of a low-level feature with the deconvolutional layers on conventional SSD to improve the efficiency, for simplicity, we called the proposed method EDF-SSD (Enhanced Deconvolution Feature Fused SSD). The proposed EDF-SSD is evaluated on MS COCO and PASCAL VOC 2007 datasets, the experiment results show that EDF-SSD has achieved higher mAP on both datasets by comparing with that of conventional SSD.
Continuous Wavelet Transform Applications In Steganography

Steganography is the art of hiding confidential information. The main purpose of steganography is to hide a message and to make it invisible for an attacker even in the case of receiving an object where the message was hidden. Messages can be, for example, hidden in audio or video signals. Steganography methods can be divided into spatial and frequency. The first group of methods directly changes the components of the original image (pixels), therefore, they visually reduce the quality of the image. One of the most well-known and widespread methods is a discrete cosine transform (DCT). As an alternative to DCT, discrete wavelet transform (DWT) is increasingly being used. Wavelet transform is a relatively new concept. There is a trend towards using wavelets in signal processing and analysis instead of or in addition to discrete cosine transform (DCT). One of the applications of steganography in an image is the use of a watermark. The purpose of a digital watermark is to hide the watermark (a kind of identification data) inside the image so that it is not perceptible to the human eye, but also so that it is stable enough to withstand various kinds of transformations. This paper is focusing on successfully hiding information in an image with the help of a wavelet transform without a visible change to the source file. The proposed algorithm includes a single-level 2D wavelet transform based on various types of transformations such as Haara, Daubeshi and Coiflet.
Towards the benchmarking of question generation: introducing the Monserrate corpus

Despite the growing interest in Question Generation, evaluating these systems remains notably difficult. Many authors rely on metrics like BLEU or ROUGE instead of relying on manual evaluations, as their computation is mostly free. However, corpora generally used as reference is very incomplete, containing just a couple of hypotheses per source sentence. In this paper, we propose monserrate corpus, a dataset specifically built to evaluate Question Generation systems, with, on average, 26 questions associated to each source sentence, attempting to be an “exhaustive” reference. With monserrate we study the impact of the reference size in evaluating Question Generation systems. Several evaluation metrics are used, from more traditional lexical ones to metrics based on word embeddings, and we conclude that these are still a limiting evaluation factor, as they lead to different outcomes. Finally, with monserrate, we benchmark three different Question Generation systems, representing different approaches to this task.
A Model-Driven Framework for Security Labs using Blockchain Methodology

Blockchain technology is the need of an hour for ensuring security and data privacy. However, very limited tools and documentation are available, therefore, the traditional code-centric implementation of Blockchain is challenging for programmers and developers due to inherent complexities. To overcome these challenges, in this article, a novel and efficient framework is proposed that is based on the Model-Driven Architecture. Particularly, a Meta-model (M2 level Ecore Model) is defined that contains the concepts of Blockchain technology. As a part of tool support, a tree editor (developed using Eclipse Modeling Framework) and a Sirius based graphical modeling tool with a drag-drop palette have been provided to allow modeling and visualization of simple and complex Blockchain-based scenarios for security labs in a very user-friendly manner. A Model to Text (M2T) transformation code has also been written using Acceleo language that transforms the modeled scenarios into java code for Blockchain application in the security lab. The validity of the proposed framework has been demonstrated via a case study. The results prove that our framework can be reliably used and further extended for automation and development of Blockchain-based application for security labs with simplicity.
Semi-supervised Text Classification with Temporal Ensembling

Supervised learning algorithms require a lot of labeled data to train the model while obtaining labeled data for large datasets is costly and time consuming. Semi-supervised learning algorithms can jointly learning from labeled and unlabeled data. Thus, it is expected to solve the difficulty of labeling large data. This paper aims to propose a method which can train in a supervised fashion with labeled and unlabeled data simultaneously. To achieve the goal, we extend temporal ensembling which give a pseudo label for each example in unlabeled data into text classification. We propose a semi-supervised model which combine Bi-GRU and temporal ensembling. We use Bi-GRU as the deep neural network and use labeled data and unlabeled data to train Bi-GRU simultaneously. During training, pseudo-label is an ensemble prediction which aggregating the predictions of multiple previous network evaluations given by trained Bi-GRU. The experimental results demonstrate that our approach provides improvements when compared to the state of the art methods.
Surficial geochemical and mineralogical signatures of Ni-Cu-PGE deposits in glaciated terrain: Examples from the South Range of the Sudbury Igneous Complex, Ontario, Canada

A till geochemistry and indicator mineral (IM) study was carried out in the South Range of the Sudbury Igneous Complex (SIC) to develop surficial mineral exploration techniques that are suitable for low sulfide, high precious metal (LSHPM) and massive sulfide Ni-Cu-PGE mineralization. Surficial exploration in this region is problematic because of heterogeneous till deposits overlain by glaciolacustrine sediments, a naturally high surficial geochemical background, and a paucity of undisturbed till for geochemical sampling due to mining-related operations. The Vermilion and Crean Hill deposits were selected for this case study because their geology and mineralogy have been extensively studied. Pathfinder elements that show a strong, positive (r2 > 0.6, n = 120) correlation with ore elements Ni, Cu, Au and PGEs (Pt, Pd, Ru, Rh, and Ir), which are derived from mineralization and present in elevated concentrations in till samples overlying mineralization are As, Bi, Co, Cr, Pb, Sb and Te. Ore minerals that survive glacial transport and post-glacial weathering and are the best indicators of Ni-Cu-PGE-Au mineralization are grains of gold, chalcopyrite, and sperrylite, predominantly recovered from the < 0.25 mm pan concentrate fraction of till. Despite challenges, this study has identified new exploration targets that warrant further investigation and demonstrates that till geochemistry and IM methods can be used to explore for Ni-Cu-PGE mineralization in established mining camps.
Design and Implementation of Smart Real-Time Billing, GSM, and GPS-Based Theft Monitoring and Accident Notification Systems

The rapid expansion of technology and architecture has improved our lives while increasing the traffic hazards, vehicular robberies, and road accidents, which in turn cause a tremendous death toll and property because of poor crisis facilities. As per the government demonstration, everybody ought to observe the traffic rules on the off chance that an individual disrupts any guidelines, at that point the approved individual will charge the fine (Inigo in IEEE Trans Veh Technol 38(3):112–122, [1]). As thefts increasing day today, to control this problem, the security of the system should be increased. If the car is stolen, the owner is allowed to stop ignition via GSM network communications by sending a ‘stop’ message. Then, the parked position can be easily identified by using the methods of the global positioning system. When an accident happens, the family members, friends, nearby police station, and hospitals also will get the notification, along with the location where it happens (Lotufo and Morgan inAutomatic numberplate recognition (6):1–6, [2]). After that, the location of an incident can be easily identified using the GSM network and the global positioning system (GPS) technique (Collins et al. in A system for video surveillance and monitoring. Carnegie Mellon University, Carnegie, [3, Dickinson and Wan in Road traffic monitoring using the trip system, pp. 56–60,4]). We use ARM 7 for this technology as the centerpiece of our new smart vehicle tracking system with types of equipment like RFID, GPS, and GSM. The equipment/programming co-structure of another smart vehicle tracking framework incorporates automatic billing, RFID identification, programmed accident detection, and GPS location.
Handwritten Gujarati Script Recognition

Their are numerous languages in this world and they are spoken and written every day. In modern times everyone has recognized the need for the recognition of handwritten scripts by machines for various purposes. Handwriting recognition has been one of the most demanding and interesting research areas in the field of image processing and machine learning since a long time. It finds its use in various applications which include conversion of handwritten documents into structural text form, reading aid for the blind, bank cheques, etc. Handwritten Script recognition can also play a significant role in preserving the culture, famous works, and literature from old times that were handwritten. For this reason, it is particularly important to have a handwritten script recognition system that can be used irrespective of the document type.The project aims at developing an offline handwritten Gujarati script recognition system which can be effectively used for recognizing handwritten Gujarati scripts. There is a significant difference in the handwriting of different people and the contours formed by characters of the Gujarati Script and these factors poses a stiff challenge in this process. This process involves the creation of a neural network that will take input as an image (.jpg,.jpeg,.png, etc.) and then the features will be extracted from it, the neural network will recognize this text and output it on the machine. This will help in the recognition of Gujarati scripts of different handwriting and will help reduce human error and save the time of individuals.
Practical and comprehensive formalisms for modelling contemporary graph query languages

The industry-wide adoption of graph databases has been hindered due to the fragmentation in syntax and semantics of available graph query languages. As a result, several projects have been proposed by industry and academia to develop a standard query language by integrating features from existing practical graph query languages. A significant factor that can impact query language integration is the lack of common theoretical language formalisms. We propose common formalisms by extending conjunctive queries and union of conjunctive queries with Tarski's relation algebra (CQT/UCQT). We use common graph query patterns to compare the expressive power of (CQT/UCQT) with two practical graph query languages - Cypher and PGQL. The query languages are analysed on the core features of graph pattern matching and graph navigation, revealing the common and exclusive characteristics for these languages. Overall, our study serves as a formal basis for comparing existing graph query languages and assists the move towards query language integration and interoperability between available graph database technologies.
Video Object Detection Method Using Single-Frame Detection and Motion Vector Tracking

Video traffic on the Internet has been increasing rapidly and accounts for a large percentage of the total traffic. To process the increasing number of videos, edge computing is preferable for load balancing and bandwidth reduction. However, edge areas have less computational resources than cloud areas, and high-performance GPUs for processing videos at high speed are not always present. Therefore, a memory-saving and high-throughput video analysis method is necessary for analyzing videos in edge areas. In this paper, a video object detection method using single-frame detection and motion vector tracking is proposed. This method is classified as a pixel and compressed domain analysis method and is realized by compensating motion using the motion vectors that already exist in the compressed domain. This method is divided into two processes: CNN-based object detection and motion vector-based object detection. In addition, a network-transparent platform for video reconstruction in edge areas is constructed. The network-transparent service can be installed without modifying the existing end-device network settings, network configuration, and routing. The platform enables video object detection services to be added on without modification of these settings.
Perfect Control for Continuous-Time LTI State-Space Systems: The Nonzero Reference Case Study

In this paper an innovative study concerning the perfect control algorithm is presented. This particular control strategy has already been developed for continuous- and discrete-time transfer-function-originated plants, as well as discrete-time state-space-related systems, however the approach devoted to continuous-time state-space objects is still an unexplored research area. With the application of the time-dependent system correction joined with nonunique matrix inverses, a new maximum-speed and maximum-accuracy control paradigm is established within this research. Simulation cases performed in the Matlab/Simulink environment show, that the new results can successfully be applied in the field of control theory and practice.
Resource Allocation in URLLC with Online Learning for Mobile Users

Neural networks (NNs) have been applied to solve various problems in ultra-reliable and low-latency communications (URLLC). Facing the stringent quality of service requirement, the time for training and running NNs is not negligible, and how to ensure the reliability with learning-based solutions is challenging, especially in a dynamic environment. In this paper, we propose an online learning method, which fine-tunes the NNs trained without supervision to ensure the reliability of URLLC for mobile users. A joint power and bandwidth allocation problem, aiming to minimize the bandwidth required for satisfying the quality of service of each user, is considered as an example. A "learning-to-optimize" method with offline training is provided for comparison. Simulation results show that the proposed online learning method can achieve comparable system performance as the offline training method, where the time consumed for online training and inference is about 25% of the 1 ms latency bound for the considered setup. Besides, the online learning method adapts to the abrupt change of average packet arrival rate quickly and can ensure reliability by setting the required overall packet loss probability conservative slightly. By contrast, the offline training method yields much worse reliability when the arrival rate varies.
Advanced Video Surveillance System

Video surveillance has shown signs of growing more and more prevalent in today's world, with video surveillance market statistics predicting an 11 percent growth between 2019 and 2021. Video surveillance is evolving past security into intelligent video applications. Everywhere, right from airports, cities, casinos, retail stores and workplaces across the world to surveillance of pets and elderly in homes, there is a surge in the use of this technology with high quality expectations. 4K cameras that provide a level of detail equivalent to a big-screen movie presentation are coming into the mainstream. The ability to discern small visual elements at long distances, security teams can avert or alert to keep people and property safe. With this in mind, reducing the storage by recording only when motion is detected and storing frames which has face, for ease of access is designed.
Construction of Student Personal Information Management System Relying on Computer

Abstract With the development of computer technology, the application of computer software technology is very extensive. Software systems has also improved the development of all walks of life rapidly. The emergence of software systems has greatly improved the management of schools and students. This paper mainly analyzes and summarizes the significance and the main design of the student information management system.
A novel handwritten Turkish letter recognition model based on convolutional neural network

Abstract Convolutional neural networks have provided state‐of‐the‐art solutions for many subfields of computer vision. While there exist many studies in the literature for several languages, studies for handwritten Turkish character recognition lack in the research field. To this end, we propose a novel handwritten Turkish letter recognition model based on a convolutional neural network. Since, to the best of our knowledge, there do not exist any publicly available handwritten Turkish letters datasets, we constructed a handwritten Turkish letters dataset that consists of 25,875 samples. To compare the performance of the proposed model with the related work, three state‐of‐the‐art models, namely, VGG 19, InceptionV 3, and Xception , were utilized through the transfer learning technique. When these models were evaluated on the handwritten Turkish letter dataset, the proposed model's accuracy was calculated as high as 96.07% which was higher than the benchmark models. To measure the generalization ability of the proposed model, it was evaluated on a gold standard dataset, namely, EMNIST , and has achieved an accuracy of 80.54% which was higher than the benchmark models. Finally, the proposed model was trained and evaluated on the EMNIST dataset and it has achieved an accuracy of 94.61% which outperformed the related work.
Blockchain Based Multi-Authority Fine-Grained Access Control System With Flexible Revocation

Traditional data sharing systems are facing new challenges when implementing access control with more and more complex data sharing requirements. Flexibility of user revocation in completely decentralized environments needs to be taken into account. In this article, we propose a Key-Policy Attribute-Based Encryption scheme with Multiple and Flexible Revocation (MAFR-KP-ABE) to achieve the features of decentralized authorization and flexible revocation. We prove the security of our MAFR-KP-ABE scheme in the standard model and provide the comparison with relevant schemes to demonstrate its efficiency. Then we propose a fine-grained access control system based on MAFR-KP-ABE scheme and blockchain that matches the need of paid data sharing services with several security properties enhanced. Security analysis and system implementation are given subsequently to demonstrate our system efficient and secure.
Single-component gradient rules for variational quantum algorithms

Many near-term quantum computing algorithms are conceived as variational quantum algorithms, in which parameterized quantum circuits are optimized in a hybrid quantum-classical setup. Examples are variational quantum eigensolvers, quantum approximate optimization algorithms as well as various algorithms in the context of quantum-assisted machine learning. A common bottleneck of any such algorithm is constituted by the optimization of the variational parameters. A popular set of optimization methods work on the estimate of the gradient, obtained by means of circuit evaluations. We will refer to the way in which one can combine these circuit evaluations as gradient rules. This work provides a comprehensive picture of the family of gradient rules that vary parameters of quantum gates individually. The most prominent known members of this family are the parameter shift rule and the finite differences method. To unite this family, we propose a generalized parameter shift rule that expresses all members of the aforementioned family as special cases, and discuss how all of these can be seen as providing access to a linear combination of exact first- and second-order derivatives. We further prove that a parameter shift rule with one non-shifted evaluation and only one shifted circuit evaluation can not exist does not exist, and introduce a novel perspective for approaching new gradient rules.