Reviewerly/exHarmony huggingface dataset:
Honeypot System for Automatic Reporting of Illegal Online Gambling Sites Utilizing SMS Spam

The emergence of illegal online gambling (IOG) has led to an increase in gambling addiction and threats to cyber security. Since IOG is advertised through short message service (SMS), we propose a novel system to detect and extract uniform resource locator (URL) information from SMS spam. For majority of the cases, these URLs are not directly linked to a real website address, which causes difficulties for detection systems. In order to address this problem, we utilized a readable transformation technique (RTT). The resulting suggestions can be enhanced to extract URLs automatically from SMS spam, which will allow local authorities identify and block IOG operations.
Approximate Inverse Reinforcement Learning from Vision-based Imitation Learning

In this work, we present a method for obtaining an implicit objective function for vision-based navigation. The proposed methodology relies on Imitation Learning, Model Predictive Control (MPC), and an interpretation technique used in Deep Neural Networks. We use Imitation Learning as a means to do Inverse Reinforcement Learning in order to create an approximate cost function generator for a visual navigation challenge. The resulting cost function, the costmap, is used in conjunction with MPC for real-time control and outperforms other state-of-the-art costmap generators in novel environments. The proposed process allows for simple training and robustness to out-of-sample data. We apply our method to the task of vision-based autonomous driving in multiple real and simulated environments and show its generalizability. Supplementary video: https://youtu.be/WyJfT5lc0aQ
Quantitative and Semi-quantitative Methods for Assessing the Degree of Methylene Blue Staining in Sentinel Lymph Nodes in Dogs

Background: To develop a digital algorithm for quantitative assessment of surface methylene blue staining in whole lymph nodes and validate a semi-quantitative visual scoring method for patient-side use. Methods: Lymph nodes from canine patients with spontaneous tumors undergoing sentinel lymph node mapping were prospectively assessed ex vivo and photographed. Using an open-source computer-based imaging software, an algorithm was developed for quantification of staining based on a signal-to-background ratio. Next, two blinded observers evaluated images and assigned a semi-quantitative visual score based on surface staining (0-no blue stain, 1-1-50% stained, and 2-51-100% stained) and those results were compared to the established quantitative standard. Results: Forty-three lymph nodes were included. Image analysis successfully quantified blue staining and differentiated from normal lymph node tissue in all cases. Agreement between observers using the Kappa coefficient demonstrated strong agreement (k = 0.8581, p < 0.0001) between semi-quantitative visual scoring and image analysis. There was substantial interobserver and intraobserver agreement for the scoring system (k = 0.7340, p < 0.0001 and k = 0.8983, p < 0.0001, respectively). Conclusion: A digital algorithm using an open-source software was simple and straightforward to use for quantification of blue staining. The use of a semi-quantitative visual scoring system shows promise for a simple, objective, repeatable assessment of methylene blue staining at the time of surgery. This study demonstrates reliable and repeatable methods for blue staining quantification thereby providing a novel and objective reporting mechanism in scientific research involving sentinel lymph node mapping.
Real-Time Object Detection using Machine Learning

Real-time-object-detection is object detection in authentic time with expeditious inference while maintaining a base level of precision. It is a cosmic, energetic yet uncertain and complicated space of PC vision. Because of its increased use in reconnaissance, the global positioning framework used in security and numerous other applications has moved scientists to never-ending device more severe and proficient calculations. In any case, difficulties arise in executing object identification and following in actual time, like following under robust climate, an excessive calculation to fit the accurate time execution, or multi-camera multiobjects following make this assignment exhaustingly burdensome. Item identification is a PC vision procedure that sanctions us to distinguish and find objects in a picture or video. Article identification sanctions us to immediately consign the sorts of things found while finding occasions of them inside the picture. However, numerous strategies and procedures have been grown, yet in this writing audit, we have examined some celebrated and straightforward techniques for object recognition and following. In the end, we have withal given their overall applications and results.
Time-Aware Graph Embedding: A Temporal Smoothness and Task-Oriented Approach

Knowledge graph embedding, which aims at learning the low-dimensional representations of entities and relationships, has attracted considerable research efforts recently. However, most knowledge graph embedding methods focus on the structural relationships in fixed triples while ignoring the temporal information. Currently, existing time-aware graph embedding methods only focus on the factual plausibility, while ignoring the temporal smoothness, which models the interactions between a fact and its contexts, and thus can capture fine-granularity temporal relationships. This leads to the limited performance of embedding related applications. To solve this problem, this article presents a Robustly Time-aware Graph Embedding (RTGE) method by incorporating temporal smoothness. Two major innovations of our article are presented here. At first, RTGE integrates a measure of temporal smoothness in the learning process of the time-aware graph embedding. Via the proposed additional smoothing factor, RTGE can preserve both structural information and evolutionary patterns of a given graph. Secondly, RTGE provides a general task-oriented negative sampling strategy associated with temporally aware information, which further improves the adaptive ability of the proposed algorithm and plays an essential role in obtaining superior performance in various tasks. Extensive experiments conducted on multiple benchmark tasks show that RTGE can increase performance in entity/relationship/temporal scoping prediction tasks.
A Supervised Approach to Credit Card Fraud Detection Using an Artificial Neural Network

The wide acceptability and usage of credit card-based transactions can be attributed to improved technological availability and increased demand due to ease of use. As a result of the increased adoption levels, this domain has become profitable and one of the most popular targets for fraudsters who use it to conduct regular exploitations or assaults. Merchants and financial processing providers that sell credit cards suffer substantial financial damages as a result of credit card theft. Because of the possibility of large casualties, it is one of the most serious risks to these organizations and individuals. Credit card fraudulent transaction can be viewed as a binary classification task in which a supervised machine learning technique could be used to analyze and classify a credit card transaction dataset into genuine or fraudulent cases. Therefore, this study explored the use of Artificial Neural Network (ANN) for credit card fraud detection. ULB Machine Learning Group dataset that has 284, 315 legitimate and 492 fraudulent transaction were used to validate the proposed model. Performance evaluation results revealed that model achieved a 100% and 99.95% classification accuracy during training and testing respectively. This affirmed the fact that ANN model could be efficiently used to predict credit card fraudulent transactions.
Analysis of Solar Energy Potential Using Sunshine Based Model

Solar photovoltaics are in recent times being used to replace fuel-based lighting and off-grid electrical needs. This study presents the evaluation of solar photovoltaic power systems in Dirashe Woreda of Ethiopia. The study was performed to assess and analysis the data which were acquired from the selected site. A descriptive survey was used to assess the information gathered. Empirical models were used to forecast the radiation based on sunshine hours. The linear monthly average solar radiation of the study site was calculated using data from the National Aeronautics and Space Administration (NASA). The study also includes an estimate of the study area’s solar energy resource based on primary data collected between January and December 2019. Finally, an equation using Angstrom-Prescott Model was derived for the study area.
Attentive and context-aware deep network for saliency prediction on omni-directional images

Understanding visual attention of observers on omni-directional images gains interest along with the booming trend of virtual reality applications. In this paper, we propose a novel attentive and context-aware network for saliency prediction on omni-directional images, which is named as ACSalNet. In this architecture, considering the problem of insufficient receptive fields of high-level features, a Deformable Attention Bottleneck (DAB) is first proposed to strengthen the high-level feature extractor and effectively focus the limited receptive field of the model to the key areas. Then, to reduce the semantic gap between features of different levels and introduce context-aware information, we further design a Context-aware Feature Pyramid Module (CFPM). In the testing phase, in order to reduce the error of prediction directly on the equirectangular images while retaining their integrity, a novel projection method called Multiple Sphere Rotation (MSR) is proposed. Extensive experiments illustrate that the proposed method outperforms the state-of-the-art models under different evaluation metrics on the public saliency benchmarks.
Generalizing Few-Shot Classification of Whole-Genome Doubling Across Cancer Types

The study and treatment of cancer is traditionally specialized to the cancer’s primary site of origin. However, certain phenotypes are shared across cancer types and have important implications for clinical care. To date, automating the identification of these characteristics from routine clinical data - irrespective of the type of cancer - is impaired by tissue-specific variability and limited labeled data. Whole-genome doubling is one such phenotype; whole-genome doubling events occur in nearly every type of cancer and have significant prognostic implications. Using digitized histopathology slide images of primary tumor biopsies, we train a deep neural network model end-to-end to accurately generalize few-shot classification of whole-genome doubling across 17 cancer types. By taking a meta-learning approach, cancer types are treated as separate but jointly-learned tasks. This approach outperforms a traditional neural network classifier and quickly generalizes to both held-out cancer types and batch effects. These results demonstrate the unrealized potential for meta-learning to not only account for between-cancer type variability but also remedy technical variability, enabling real-time identification of cancer phenotypes that are too often costly and inefficient to obtain.
Smells in system user interactive tests

Test smells are known as bad development practices that reflect poor design and implementation choices in software tests. Over the last decade, there are few attempts to study test smells in the context of system tests that interact with the System Under Test through a Graphical User Interface. To fill the gap, we conduct an exploratory analysis of test smells occurring in System User Interactive Tests (SUIT). We thus, compose a catalog of 35 SUIT-specific smells, identified through a multi-vocal literature review, and show how they differ from smells encountered in unit tests. We also conduct an empirical analysis to assess the diffuseness and removal of these smells in 48 industrial repositories and 12 open-source projects. Our results show that the same type of smells tends to appear in both industrial and open-source projects, but they are not addressed in the same way. We also find that smells originating from a combination of multiple code locations appear more often than those that are localized on a single line. This happens because of the difficulty to observe non-local smells without tool support. Furthermore, we find that smell-removing actions are not frequent with less than 50% of the affected tests ever undergoing a smell removal. Interestingly, while smell-removing actions are rare, some smells disappear while discarding tests, i.e., these smells do not appear in follow-up tests that replace the discarded ones.
Interventional Sum-Product Networks: Causal Inference with Tractable Probabilistic Models

While probabilistic models are an important tool for studying causality, doing so suffers from the intractability of inference. As a step towards tractable causal models, we consider the problem of learning interventional distributions using sum-product networks (SPNs) that are over-parameterized by gate functions, e.g., neural networks. Providing an arbitrarily intervened causal graph as input, effectively subsuming Pearl's do-operator, the gate function predicts the parameters of the SPN. The resulting interventional SPNs are motivated and illustrated by a structural causal model themed around personal health. Our empirical evaluation on three benchmark data sets as well as a synthetic health data set clearly demonstrates that interventional SPNs indeed are both expressive in modelling and flexible in adapting to the interventions.
Hybrid consensus algorithm based on hierarchy authority

Consensus algorithm is the key to achieve consistent data state in blockchain system. The existing consensus algorithm of consortium blockchain is mainly designed on the basis of equal status of participating nodes. For the hierarchical structure more commonly seen in social life, business management and military command, the current algorithms cannot provide good solutions. In this paper, we propose a hybrid consensus algorithm based on hierarchical authority, which is suitable for node groups with hierarchical relations. By analyzing the advantages and disadvantages of PBFT algorithm and Raft algorithm, the method combines the two algorithms to suit characteristics of hierarchical systems. Firstly, an authority value is initialized for each node according to the hierarchical relationship, and then the nodes are stratified according to the authority value. Raft algorithm is adopted at the lower level, and PBFT algorithm is adopted at the higher level. Through the stratification mechanism, the communication volume is reduced and the consensus efficiency is effectively improved. Secondly, the authoritative value assignment strategy is introduced in the PBFT to effectively limit the malicious attack behavior, and the monitor node is introduced in the Raft algorithm to improve the Byzantine fault tolerance. The experimental results show that, our proposed method has high consensus efficiency while maintaining Byzantine fault-tolerant ability in hierarchical systems.
Open-Set Recognition based on the Combination of Deep Learning and Ensemble Method for Detecting Unknown Traffic Scenarios

An understanding and classification of driving scenarios are important for testing and development of autonomous driving functionalities. Machine learning models are useful for scenario classification but most of them assume that data received during the testing are from one of the classes used in the training. This assumption is not true always because of the open environment where vehicles operate. This is addressed by a new machine learning paradigm called open-set recognition. Open-set recognition is the problem of assigning test samples to one of the classes used in training or to an unknown class. This work proposes a combination of Convolutional Neural Networks (CNN) and Random Forest (RF) for open set recognition of traffic scenarios. CNNs are used for the feature generation and the RF algorithm along with extreme value theory for the detection of known and unknown classes. The proposed solution is featured by exploring the vote patterns of trees in RF instead of just majority voting. By inheriting the ensemble nature of RF, the vote pattern of all trees combined with extreme value theory is shown to be well suited for detecting unknown classes. The proposed method has been tested on the highD and OpenTraffic datasets and has demonstrated superior performance in various aspects compared to existing solutions.
Smart Tourism: A Proof of Concept For Cyprus Museum of Modern Arts In The IoT Era

Smart tourism is one of the booming industries, several countries are trying to have smart cities in which technologies will be used to ease life and connect almost everything. Several objects detectors were developed by many researchers, but there is a need to have lightweight models so that they can fit smartphones and other edge devices. In this study, the aim is to prove the concept of using a mobile application for smart tourism. The mobile application which has EfficientDet-d2 as a backbone will detect several statues belonging to the Cyprus Museum of Modern Arts and provide relevant information about each statue.
A Simple Network with Progressive Structure for Salient Object Detection

Recently, most CNNs-based Salient Object Detection (SOD) models have achieved great progress through various feature aggregation strategies. But most of them usually introduce a large number of features into a module for fusion, which results in information dilution. In this paper, we propose a Progressive Fusion Network (PFNet) to solve this problem. The PFNet alleviates the information dilution through a Progressive Fusion Architecture (PFA), which aggregates the features extracted from encoder in a progressive fusion manner. In addition, we use a simple Feature Fusion Module (FFM) that utilize high-level features to enhance the semantic information of low-level features, thereby ensuring the effective fusion of features. Finally, we leverage an Enhanced Loss to guide the optimization process of the network and obtain high-quality saliency maps. The whole network is trained end-to-end without any pre-processing and post-processing. The quantitative and qualitative experimental results on five benchmark datasets demonstrate that the superiority of the proposed approaches.
Cooperative Scheduling Schemes for Explainable DNN Acceleration in Satellite Image Analysis and Retraining

The deep learning-based satellite image analysis and retraining systems are getting emerging technologies to enhance the capability of the sophisticated analysis of terrestrial objects. In principle, to apply the explainable DNN model for the process of satellite image analysis and retraining, we consider a new acceleration scheduling mechanism. Especially, the conventional DNN acceleration schemes cause serious performance degradation due to computational complexity and costs in satellite image analysis and retraining. In this article, to overcome the performance degradation, we propose cooperative scheduling schemes for explainable DNN acceleration in analysis and retraining process. For the purpose of it, we define the latency and energy cost modeling to derive the optimized processing time and cost required for explainable DNN acceleration. Especially, we show a minimum processing cost considered in the proposed scheduling via layer-level management of the explainable DNN on FPGA-GPU acceleration system. In addition, we evaluate the performance using an adaptive unlabeled data selection scheme with confidence threshold and a semi-supervised learning driven data parallelism scheme in accelerating retraining process. The experimental results demonstrate that the proposed schemes reduce the energy cost of the conventional DNN acceleration systems by up to about 40% while guaranteeing the latency constraints.
Fragment of Binary Object Contour Points on the Basis of Energy Level for Shape Classification

This paper addresses the problem of translation, rotation and scale invariant recognition and classification of objects. A new set of translation and rotation invariant features are introduced called sigma-pi bond model in a lattice. They are the magnitude of energy required to disintegrate the particle from an object also called bond dissociation energy. The scale invariance of the energy level features of an object is obtained by using systematic computation of Hu’s seven moments that are used in the matching stage of classification and analysis of binary object shape. The proposed algorithm has been tested on a variety of binary objects data sets, including Kimia silhouettes and MPEG-7 CE-Shape-1 databases. In all the experiments, our approach demonstrate effective performance compared with other algorithms.
Differences in Ethical Cyber Behavioural Intention of Nigerian and South African Students: A Multi-Group Analysis Based on the Theory of Planned Behaviour

Abstract Cybertechnology has become a fundamental part of educational institutions, with the student’s routine to use these technologies to communicate, learn and play, causing a need for understanding the impact and general principles of ethical computer use in academia. However, the practice of cyberethics in these environments has presented many challenges for the institutions. This study, based on the theory of planned behaviour, examined the differences in ethical cyber behavioural intention of Nigerian and South African students using a multi-group analysis. The study adopted a survey research design– structured questionnaires were randomly distributed among students of two purposively selected universities, one in South Africa and the other Nigeria. Structural equation modelling was employed to test the proposed model fit. Overall, 450 undergraduate students participated in the survey; 343 respondents completed and returned the questionnaire, resulting in a response rate of 76.2%. The subjective norm was found to be statistically significant in influencing students’ ethical cyber behavioural intention with cyber technologies. The study contributes to the extant literature on the application of the Theory of Planned Behaviour (TPB). The originality and value of this study can be viewed from the application of a socio-psychology theory to determine ethical cyber behavioural intention in relation to cybertechnology, which provides further evidence that the TPB can be applied to cyberethics research. Moreover, to the best of authors’ knowledge, this is a pioneer study that comprehensively examines the linkage of components of TPB with ethical cyber behavioural intention in joint samples from South Africa and Nigeria.
Image Steganography for Confidential Data Communication

In the present-day communication system, one of the most important tasks is securing confidential data through a communication channel. Many adequate encryption and decryption algorithms have been developed. But due to growth in technology, these algorithms are prone to attack and there would be loss of information. This led to the development of the steganography method which hides the existence of confidential data. This paper mainly concentrates on steganography using the LSB algorithm. The process involves hiding a secret image in a cover image and then transferring it over the internet. This method can use multimedia such as text, images, audio files, and video files. Here, steganography is developed to hide a location of an area (ex in form of a map) in a cover image and transfer the image through the internet. The performance of this technique has been analyzed through MSE and PSNR values for its utility in security-based applications.