Reviewerly/exHarmony huggingface dataset:
Online Subjective answer verifying system Using Artificial Intelligence

Every year educational institutes conduct various examinations, which include institutional and non-institutional competitive exams. Now a day's online tests and examinations are becoming popular to reduce the burden of the examination evaluation process. The online exams include either objective or multiple-choice questions. Nevertheless, the exams include only objective or multiple-choice questions. However, subjective-based questions and answers are not involved due to the evaluation process complexity and efficiency of the evaluation process. An automatic answer checker application that checks the written answers and marks the weightage similar to a human being is more helpful in the current modern era is necessary. Hence, the software applications built to check subjective answers may be more useful for allocating marks to the user after verifying the answers for online examination. This type of tool/application/system has the challenge of having an abundant resource database, including questions, corresponding answers, and the marks allocated to the corresponding answers. At the same time system need to verify the answers provided by the users by checking the template answers and the answers provided by the user. However, Artificial Intelligence is required to identify the core element of the answers while allocating marks. Hence, an Artificial Intelligence-based answer verifier is proposed to do the job of examiner/evaluator automatically. As a result of this artificial intelligence-based answer verifier, the evaluator's time and energy can be conserved, with improved work efficiency.
Exploring the software repositories of embedded systems: An industrial experience

Tracing reports for software repositories have attracted many researchers. Most of them have focused on defect analysis and development processes in relation to open source programs. There exists a gap between open source and industrial software projects, which, in particular, relates to different schemes for creating software repositories and development schemes. This is especially true for embedded systems that gain large markets and become more complex. The aim is to explore the software repositories of industrial embedded systems and derive characteristic features in order to evaluate quality and identify problems to do with development processes. In this paper we have proposed a novel approach to software repository analysis based on the fine grained exploration of issue tracking and code control repositories. In particular, we distinguish the various activities of project actors (e.g. creating new functions, correcting defects, improving performance, modifying tests) and analyse them in a context, not only of a single project, but also a set of correlated projects that have been developed in the company. These issues have been neglected in the literature. These analyses needed new holistic schemes for repository exploration, including various statistical metrics, text mining, and machine learning techniques. In exploring selected industrial projects we have identified that only 40–75% of issues relate to defects; the issue reports and commit descriptions included here comprise a lot of data that has been disregarded in the literature. These data allow us to trace diverse types of code changes and identify imperfections in software repositories. We show that fine grained repository analysis gives a broader and more complete view of project development, which may lead to its improvement.
A Novel Privacy Preserving HMAC Algorithm Based on Homomorphic Encryption and Auditing for Cloud

Cloud is the perfect way to hold our data every day. Yet the confidentiality of our data is a big concern in the handling of cloud data. Data integrity, authentication and confidentiality are basic security threats in the cloud. Cryptography techniques and Third Party Auditor (TPA) are very useful to impose the integrity and confidentiality of data. In this paper, a system is proposed Enhancing data protection that is housed in cloud computing. The suggested solution uses the RSA algorithm and the AES algorithm to encrypt user data. The hybridization of these two algorithms allows better data protection before it is stored in the cloud. Secure hash algorithm 512 is used to compute the Hash Message Authentication Code (HMAC). A stable audit program is also introduced for Third Party Auditor (TPA) use. The suggested algorithm is applied in python programming and tested in a simple sample format. It is checked that the proposed algorithm functions well to guarantee greater data protection.
Variational learning of a shifted scaled Dirichlet model with component splitting approach

Mixture models have become arguably one of the most widely used statistical approaches to perform inference on various types of data and have been successfully applied in data mining and numerous real world applications. In this work, we focus on the variational learning of finite shifted scaled Dirichlet mixture models. Component splitting is one of the major assets of our model which prevents over-fitting. Furthermore, the number of components can be estimated automatically and simultaneously along with parameters estimation. The performance and effectiveness of proposed model is verified by experimenting on two real-life applications, namely, occupancy estimation and activities recognition in smart buildings.
CASTLEGUARD: Anonymised Data Streams with Guaranteed Differential Privacy

Data streams are commonly used by data controllers to outsource the processing of real-time data to third-party data processors. Data protection legislation and best practice in data management support the view that data controllers are responsible for providing a guarantee of privacy for user data contained within published data streams. Continuously Anonymising STreaming data via adaptive cLustEring (CASTLE) is an established method for anonymising data streams with a guarantee of k-anonymity. However, k-anonymity has been shown to be a weak privacy guarantee that has vulnerabilities in practical applications. In this paper we propose Continuously Anonymising STreaming data via adaptive cLustEring with GUAR-anteed Differential privacy (CASTLEGUARD), a data stream anonymisation algorithm that provides a reliable guarantee of k-anonymity, l-diversity and differential privacy to data subjects. We analyse CASTLEGUARD to show that, through safe k-anonymisation and β-sampling, the proposed approach satisfies differentially private k-anonymity. Further, we demonstrate the efficacy of the approach in the context of machine learning, presenting experimental analysis to demonstrate that it can be used to protect the individual privacy of users whilst maintaining the utility of a data stream.
Rethinking Defeasible Reasoning: A Scalable Approach

Recent technological advances have led to unprecedented amounts of generated data that originate from the Web, sensor networks and social media. Analytics in terms of defeasible reasoning - for example for decision making - could provide richer knowledge of the underlying domain. Traditionally, defeasible reasoning has focused on complex knowledge structures over small to medium amounts of data, but recent research efforts have attempted to parallelize the reasoning process over theories with large numbers of facts. Such work has shown that traditional defeasible logics come with overheads that limit scalability. In this work, we design a new logic for defeasible reasoning, thus ensuring scalability by design. We establish several properties of the logic, including its relation to existing defeasible logics. Our experimental results indicate that our approach is indeed scalable and defeasible reasoning can be applied to billions of facts.
Spelling correction for the Arabic language space deletion errors-

The automatic correction of spelling errors is one of the most used disciplines in different automatic language processing systems, therefore we are dealing in this article the space deletion errors. This type of error was not taken into consideration in the classical Levenshtein algorithm. In this paper, we propose a new approach for the correction of this spelling errors type. This new approach uses an adapted version of the Levenshtein distance to correct this error. For the evaluation of our new approach we used a lexicon extracted from Al Watane newspaper articles. The results showed the importance of our approach.
Machine Learning and Statistical Analysis applied on Mechanical Engineering CAD course: A Case Study During ERTE Pahse in the Context of Higher Education

As the covid-19 pandemic stroke the humanity on March 2020, the Higher Education Institutions had to confront rapidly the emergency to readapt the organization of their courses in order to respond to those extenuating circumstances.An Emergency Remote Teaching Environment (ERTE) [1] [10] was created by the teaching team, in order to get as close as a 6 months' pre-planned Intentional Online Teaching Environment (IOTE) module can get. To achieve the above, new technologies and features have been used for the online lectures; Social media were also used [7] in order to surpass the lack of the physical environment and face-to-face instruction.This study focuses on predicting the students' satisfaction on the ERTE, by defining constructs and variables that are taken into consideration at an online survey of evaluating students' satisfaction on IOTE. The students' satisfaction rate is evaluated, focusing on the transition difficulties from a face-to face Teaching environment (FFTE) to an ERTE.The present study consists of a comparative analysis, regarding the efficiency of online mechanical Computer Aided Design courses at the University of West Attica, Athens, Greece, filled in by the students attending the remote teaching-online class courses.A dataset has been generated, considering their attendance, behavioral and comprehension parameters described in a questionnaire examining 40 variables, grouped in 9 constructs.A statistical analysis takes place, including univariate analysis (bar charts, pie charts, and frequency tables), correlation analysis, clustering and principal component analysis.The outcomes of this research aim in establishing the sustainability of these new technology features.
Ensemble Regression Models for Software Development Effort Estimation: A Comparative Study

As demand for computer software continually increases, software scope and complexity become higher than ever. The software industry is in real need of accurate estimates of the project under development. Software development effort estimation is one of the main processes in software project management. However, overestimation and underestimation may cause the software industry loses. This study determines which technique has better effort prediction accuracy and propose combined techniques that could provide better estimates. Eight different ensemble models to estimate effort with Ensemble Models were compared with each other base on the predictive accuracy on the Mean Absolute Residual (MAR) criterion and statistical tests. The results have indicated that the proposed ensemble models, besides delivering high efficiency in contrast to its counterparts, and produces the best responses for software project effort estimation. Therefore, the proposed ensemble models in this study will help the project managers working with development quality software.
Towards Verification of Neural Networks for Small Unmanned Aircraft Collision Avoidance

The ACAS X family of aircraft collision avoidance systems uses large numeric lookup tables to make decisions. Recent work used a deep neural network to approximate and compress a collision avoidance table, and simulations showed that the neural network performance was comparable to the original table. Consequently, neural network representations are being explored for use on small aircraft with limited storage capacity. However, the black-box nature of deep neural networks raises safety concerns because simulation results are not exhaustive. This work takes steps towards addressing these concerns by applying formal methods to analyze the behavior of collision avoidance neural networks both in isolation and in a closed-loop system. We evaluate our approach on a specific set of collision avoidance networks and show that even though the networks are not always locally robust, their closed-loop behavior ensures that they will not reach an unsafe (collision) state.
A Siamese Neural Network for Non-Invasive Baggage Re-Identification

Baggage travelling on a conveyor belt in the sterile area (the rear collector located after the check-in counters) often gets stuck due to traffic jams, mainly caused by incorrect entries from the check-in counters on the collector belt. Using suitcase appearance captured on the Baggage Handling System (BHS) and airport checkpoints and their re-identification allows for us to handle baggage safer and faster. In this paper, we propose a Siamese Neural Network-based model that is able to estimate the baggage similarity: given a set of training images of the same suitcase (taken in different conditions), the network predicts whether the two input images belong to the same baggage identity. The proposed network learns discriminative features in order to measure the similarity among two different images of the same baggage identity. It can be easily applied on different pre-trained backbones. We demonstrate our model in a publicly available suitcase dataset that outperforms the leading latest state-of-the-art architecture in terms of accuracy.
Multi-Robot Path Planning Method Based on Prior Knowledge and Q-learning Algorithms

Abstract The path planning method based on prior knowledge and Q-learning algorithm proposed in this paper solves the problems of low operating efficiency and slow learning speed in existing multi-robot coordination and collision avoidance. This method can be divided into two stages. Firstly, the improved Q-learning algorithm is used to plan the static collision free path of a single robot in the built model; After the Q-table is initialized with the prior knowledge obtained in the previous step, the Q-learning algorithm is used to achieve conflict-free motion among multiple robots. Finally, the simulation experiment shows that the proposed path planning method based on prior knowledge and Q-learning algorithm performs well in handling collision avoidance path planning of multiple robots.
Challenges in Using Neural Networks in Safety-Critical Applications

In this paper, we discuss challenges when using neural networks (NNs) in safety-critical applications. We address the challenges one by one, with aviation safety in mind. We then introduce a possible implementation to overcome the challenges. Only a small portion of the solution has been implemented physically and much work is considered as future work. Our current understanding is that a real implementation in a safety-critical system would be extremely difficult. Firstly, to design the intended function of the NN, and secondly, designing monitors needed to achieve a deterministic and fail-safe behavior of the system. We conclude that only the most valuable implementations of NNs should be considered as meaningful to implement in safety-critical systems.
Pre-processing memory dumps to improve similarity score of Windows modules

Memory forensics is useful to provide a fast triage on running processes at the time of memory acquisition in order to avoid unnecessary forensic analysis. However, due to the effects of the execution of the process itself, traditional cryptographic hashes, normally used in disk forensics to identify files, are unsuitable in memory forensics. Similarity digest algorithms allow an analyst to compute a similarity score of inputs that can be slightly different. In this paper, we focus on the issues caused by relocation of Windows processes and system libraries when computing similarities between them. To overcome these issues, we introduce two methods (Guided De-relocation and Linear Sweep De-relocation) to pre-process a memory dump. The goal of both methods is to identify and undo the effect of relocation in every module contained in the dump, providing sanitized inputs to similarity digest algorithms that improve similarity scores between modules. Guided De-relocation relies on specific structures of the Windows PE format, while Linear Sweep De-relocation relies on a disassembling process to identify assembly instructions having memory operands that address to the memory range of the module. We have integrated both methods in a Volatility plugin and evaluated them in different scenarios. Our results demonstrate that pre-processing memory dumps with these methods significantly improves similarity scores between memory modules.
Few-shot object detection with feature attention highlight module in remote sensing images

In recent years, there are many applications of object detection in remote sensing field, which demands a great number of labeled data. However, in many cases, data is extremely rare. In this paper, we proposed a few-shot object detector which is designed for detecting novel objects based on only a few examples. Through fully leveraging labeled base classes, our model that is composed of a feature-extractor, a feature attention highlight module as well as a two-stage detection backend can quickly adapt to novel classes. The pre-trained feature extractor whose parameters are shared produces general features. While the feature attention highlight module is designed to be light-weighted and simple in order to fit the few-shot cases. Although it is simple, the information provided by it in a serial way is helpful to make the general features to be specific for few-shot objects. Then the object-specific features are delivered to the two-stage detection backend for the detection results. The experiments demonstrate the effectiveness of the proposed method for few-shot cases.
A fast X-shaped foreground segmentation network with CompactASPP

Foreground segmentation models are designed to extract moving objects of varying sizes from the background, which can benefit from representations of various scales. As an effective module for capturing multi-scale contexts, Atrous Spatial Pyramid Pooling (ASPP) convolves a final feature representation via multiple parallel atrous convolutions with different dilation rates. However, as the dilation rate increases, ASPP gradually loses its large-scale modeling ability because the sampling of atrous kernel becomes progressively sparse within the receptive field. To solve this problem, we design a CompactASPP module to convolve feature maps compactly. Without significantly increasing the module size, the CompactASPP can encode multi-scale features from all neurons within the receptive field rather than from neurons in several sparsely distributed positions. Furthermore, we leverage CompactASPP modules to enhance our previous X-Net. The proposed Fast X-Net substantially improves the segmentation speed by over 63.6% and attains new state-of-the-art performances on CDnet2014, SBI2015 and UCSD benchmarks.
VROHI: Visibility Recovery for Outdoor Hazy Image in Scattering Media

Additive haze model (AHM), due to its high simplicity, has a potential to increase the efficiency of the restoration procedure of images degraded by scattering media. However, AHM is designed for hazy remote sensing data and is not suitable to be used on outdoor images. In this paper, according to the low-frequency feature (LFC) of haze, AHM is modified via gamma correction technique to make it suitable for modeling outdoor images. Benefitting from the modified AHM (MAHM), a simple yet effective method called VROHI is proposed to enhance the visibility of an outdoor hazy image. In specific, a low complexity LFC extraction method is designed by utilizing characteristic of the discrete cosine transform. Subsequently, by constructing the linear function of unknown parameters and imposing the saturation prior on MAHM, the image dehazing problem can be derived into a global optimization function. Experiments reveal that the proposed VROHI is superior to the other state-of-the-art techniques in terms of both the processing efficiency and recovery quality.
Heterogeneous Cross-Domain Identity Authentication Scheme Based on Proxy Resignature in Cloud Environment

Based on proxy resignature, the signature transformation between trust domains of different cryptographic systems is realized with the help of the cloud authentication center, so as to achieve cross-domain access between users and cloud service providers in heterogeneous environment. Hierarchical ID tree realizes the uniqueness of identity in communication, and the security of heterogeneous cross-domain identity authentication is enhanced by the two-factor authentication of “password + key” and temporary identity replacing real identity. The security of the scheme is proved under the CK model, which can anonymously trace entity identity, resist replay attacks, replacement attacks, and man-in-the-middle attacks, and the simulation experiment is carried out. By searching it in related fields, no paper on heterogeneous cross-domain identity authentication based on proxy resignature has been published yet. The results of this paper show that the proposed scheme has better computing performance and higher security.
Hardware-Based Real-Time Deep Neural Network Lossless Weights Compression

Deep Neural Networks (DNN) are widely applied to many mobile applications demanding real-time implementation and large memory space. Therefore, it presents a new challenge for low-power and efficient implementation of a diversity of applications, such as speech recognition and image classification, for embedded edge devices. This work presents a hardware-based DNN compression approach to address the limited memory resources in edge devices. We propose a new entropy-based compression algorithm for encoding DNN weights, as well as a real-time decoding method and efficient dedicated hardware implementation. The proposed approach enables a significant reduction of the required DNN weights memory (approximately 70% and 63% for AlexNet and VGG19, respectively), while allowing the decoding of one weight per clock cycle. Results show a high compression ratio compared to well-known lossless compression algorithms. The proposed hardware decoder enables an efficient implementation of large DNN networks in low-power edge devices with limited memory resources.