Reviewerly/exHarmony huggingface dataset:
Optimized Text Summarization method based on Fuzzy Logic

Text summarization is the task of condensing a text segment into a shorter version, reducing the size of the original text context while also preserving the informational elements and the meaning of the content. Manual text summarization will involve a significant amount of time and thus become a time expensive and generally laborious task. Aiming to reduce these pitfalls in manual text summarization, automatic text summarization has been evolving now bearing a strong motivation for academic research. Text Summarization is carried out by two main approaches, namely Extraction and Abstraction. This paper utilizes the extraction process for sentence selection. We also used some feature-based sentence scoring techniques, which play an important role in text summarization. Recently fuzzy logic-based research projects have been popularized among researchers and have been extensively applied in the domain of Natural Language Processing. Our main goal in this paper is to apply fuzzy logic in the task of text summarization. Finally, we analyzed the performance metrics resulting from the fuzzy logic-based text summarization with the benchmark methods; Rule Base and Neural Network techniques for computing the values for Precision, Recall, and F-Measure. In the process of applying the Fuzzy logic, rules were used to balance the weights between important and unimportant features based on the Feature Extraction. With the experimental results achieved, it was concluded that approaching Fuzzy Logic in the process of text summarization yields more successful results than the Rule Base and Neural Network methods.
Emitted light features of a three-level atom enclosed in an optical cavity

The three-level atom in a V configuration is confined in the single-mode optical cavity. The behaviors of this system in the parametric space have been studied theoretically in the steady state. The descriptive master equation of this system is numerically solved in the basis of the atom and cavity. The different characteristics of the system are differentiated from one another based on the various values of the critical photon number. The limiting treatments of this system can vary between those of a semiclassical laser and an effective two-level atom. Numerical simulations appropriately confirm the shown pattern in these two limiting cases.
Phishing Attack Detection with ML-Based Siamese Empowered ORB Logo Recognition and IP Mapper

Visual cues are the most convincing entities which are weaponized by cyber-attackers to carry out phishing attacks. Generally, a genuine-looking UI is sufficient to convince a user. Thinking that it is a legitimate website, users provide their private information to attackers. This is a very serious problem that needs to be solved right before the users enter their private information. Recent advances in phishing attack detection include the use of Machine Learning (ML) algorithms. In this paper, we propose a hybrid of ML-based and IP-based phishing attack detection mechanisms. We employ Oriented FAST and Rotated BRIEF (ORB) keypoints extractor to capture the brand name, and homography-based transformation to localize the logo. The object recognition and localization is followed by the Siamese network which verifies the logo localized by ORB. We then map the IP address of the incoming webpage with the IP pool of the organization associated with the logo. We focus our study on 4 highly phished organizational logos viz. Bank of America, Dropbox, Google, and PayPal. With 100 test samples, our system provides an accuracy of 90% for ORB, and accuracy, precision, recall, and f1-score of 93.68%, 95.56%, 97.73%, and 96.63% respectively for the Siamese network.
Handwritten Multi-Digit Recognition With Machine Learning

Offline handwritten digit recognition is a well-known problem that remains at best partially solved. This paper presents a study of three different algorithms for offline handwritten multi-digit recognition using the MNIST dataset: Decision Trees, Multilayer Perceptrons and Random Forest. Our results indicate that Random Forest had the best accuracy at 96% with reasonable runtime performance. This kind of study is not novel-however, the authors developed a mechanism for reading multi-digit numbers from image files and webcams that may be of interest.
A New Calibration Method of Stereo Line-Scan Cameras for 2D Coordinate Measurement

Camera calibration plays an important role in various applications including machine vision and optical measurement. In this paper, we propose a new calibration method of stereo line-scan cameras for 2D coordinate measurement using a simple calibration target rather than requiring complex calibration patterns or an auxiliary area-scan camera. Considering the viewing planes associated with the stereo line-scan cameras are coplanar, an imaging model combining perspective projection and lens distortion are established and can depict the relationship between the 2D world coordinate and its corresponding 1D pixel coordinates of the image pairs. A two-step calibration algorithm is proposed to obtain the optimal values of intrinsic, extrinsic and distortion parameters of stereo line-scan cameras. The performance of the proposed camera calibration method is evaluated on 2D coordinate measurement and the experimental results demonstrate that the proposed method is more effective and accurate than the existing method.
Automatic image caption generation using deep learning and multimodal attention

Abstract We present an improved image caption generation model that incorporating multimodal attention mechanism. We use ResNet‐101 to extract image features while incorporating channel attention mechanism and spatial attention mechanism. We use Faster R‐CNN for object detection and use a multi‐head attention structure consisting of spatial attention and self‐attention. This allows our algorithm to improve the model's capability to learn and use the internal grammatical features of natural sentences. Moreover, we use GPU parallel computing to accelerate the entire model training. We apply our model and algorithm to early education scenarios: show and tell for kids. We compare our algorithm with the state‐of‐the‐art deep learning algorithms. Our experimental results show that our model improves the captioning accuracy in terms of standard automatic evaluation metrics.
Large-scale Evaluation of Malicious Tor Hidden Service Directory Discovery

Tor is the largest anonymous communication system, providing anonymous communication services to approximately 2.8 million users and 170,000 hidden services per day. The Tor hidden service mechanism can protect a server from exposing its real identity during the communication. However, due to a design flaw of the Tor hidden service mechanism, adversaries can deploy malicious Tor hidden service directories (HSDirs) to covertly collect all onion addresses of hidden services and further probe the hidden services. To mitigate this issue, we design customized honeypot hidden services based on one-to-one and many-to-one HSDir monitoring approaches to luring and identifying the malicious HSDirs conducting the rapid and delayed probing attacks, respectively. By analyzing the probing behaviors and payloads, we investigate a novel semantic-based probing pattern clustering approach to classify the adversaries so as to shed light on the purposes of the malicious HSDirs. Moreover, we perform theoretical analysis of the capability and accuracy of our approaches. Large-scale experiments are conducted in the real-world Tor network by deploying hundreds of thousands of honeypots during a monitoring period of more than three months. Finally, we identify 8 groups of 32 malicious HSDirs, discover 25 probing pattern clusters and reveal 3 major probing purposes.
A predictive intelligence approach for low-light enhancement

Images captured in low-light scenes often suffer from poor contrast, severe loss of details and a lot of noise. These issues mentioned above can degrade the performance of some computer vision tasks such as object detection and recognition. The Retinex theory, which decomposes low-light images into reflection and illumination components, is an effective tool for predicting high-light images. Most Retinex-based methods cannot deal with noise reduction and over-smoothing simultaneously. In this paper, we propose a predictive intelligence approach of a multi-task framework to enhance the low-light image. The framework is composed of four coarse recovery sub-networks and three cross refinement sub-networks. Firstly, we use the LIME method to pre-process the low-light image and input the results to the coarse recovery sub-networks as priori knowledges along with the original low-light image. The residual connection and mixed-domain attention module are embedded in each coarse recovery sub-network so that the feature map processed by the attention mechanism is merged with the feature map from the main branch to improve the ability of predicting local bright color patches and effectively control the number of noises. The refinement sub-networks take the output of the coarse sub-networks as input and feed them into U-Net architectures in cross manner. The refinement sub-networks establish a multi-task framework with a cross-task distillation module that can significantly improve the predictive ability of the whole network. Extensive experiments demonstrate that our method achieves 4.1 in MOS, 19.82 in PSNR, 0.8470 in SSIM, 3.0271 in NIQE, and improves by more than 5% in both subjective metrics and objective metrics compared to state-of-the-art alternatives.
SQL ChatBot – using Context Free Grammar

In this work, we derive the semantics of a given English query and convert it into its equivalent SQL query. Instead of using neural networks for semantic analysis of English queries, we opt for a Context Free Grammar approach. Most neural-network-based systems can handle only one semantic at a time, whereas, because of the flexibility offered by our CFG approach, our system manages to handle simultaneous usage of conjunctive, disjunctive, and negative semantics. It also handles complex statements comprising of main as well as dependent clauses. In addition, the system also takes into account aggregate functions and constructs the required GROUP-BY and HAVING clauses. We describe how the system analyzes English queries by understanding the role that each part-of-speech has to play in constructing SQL queries. Numerous examples demonstrate the effectiveness of our approach where state-of-the-art techniques relying on deep learning algorithms fail to deliver.
Automatic and real-time green screen keying

Green screen keying has always been an essential and fundamental part of film and television special effects. In the actual shooting process, captured green screen images vary significantly due to the comprehensive influence of lighting, shooting angle, green cloth material, characters, etc. In order to obtain visually pleasing effects, traditional methods usually require professionals to adjust the corresponding parameters with lots of workload for different images, which is inefficient. Meanwhile, there are tedious steps in dealing with the green spill problem. In this paper, we propose a deep learning-based green screen keying method which is automatic, effective, and real-time. Firstly, we create a green screen dataset that contains not only alpha and foreground maps but also samples with green spill phenomenon and a large number of distinct green screen backgrounds. Secondly, we propose an end-to-end network that can automatically tackle green screen keying and green spill removal problems. Our method only takes a single image as input without user interaction and estimates alpha and foreground simultaneously. Extensive experiments clearly demonstrate the superiority of our proposed method. Moreover, our method achieves approximately 75fps on 720P videos (1280 $$\times $$ 720) and 25fps on 1080P videos (1920 $$\times $$ 1080), which can be considered real-time for many applications.
Location Privacy-Aware Coded Offloading for Distributed Edge Computing

The ever-increasing scale and complexity of the computing tasks arising from various mobile applications has fostered wide research interests in distributed edge computing. Coded edge computing is among the recent advancements in this area, which can effectively mitigate the task processing delay caused by straggling edge nodes (ENs). In edge computing, as the mobile user usually tends to offload to closer ENs to save transmit power, the adversary may stealthily infer user location by exploiting this feature. Although there have been some pioneering works on location privacy-aware offloading, they mainly focused on the single EN scenarios and may not be directly applicable to coded edge computing that involves multiple ENs. To the best of our knowledge, the location privacy issue in coded edge computing still remains largely unexplored. With this consideration, a sequential hypothesis testing based location inference attack is identified in this work to reveal the potential vulnerability of existing coded edge computing methods. Besides, a countermeasure based on dynamic EN selection is proposed, together with a location privacy-aware coded offloading scheme based on the generic Lyapunov optimization framework. In addition to analysis, simulation results are provided to justify the effectiveness of the proposed scheme.
Hierarchical Attention Network for Interpretable and Fine-Grained Vulnerability Detection

With the rapid development of software technology, the number of vulnerabilities is proliferating, which makes vulnerability detection an important topic of security research. Existing works only focus on predicting whether a given program code is vulnerable but less interpretable. To overcome these deficits, we first apply the hierarchical attention network into vulnerability detection for interpretable and fine-grained vulnerability discovery. Especially, our model consists of two level attention layers at both the line-level and the token-level of the code to locate which lines or tokens are important to discover vulnerabilities. Furthermore, in order to accurately extract features from source code, we process the code based on the abstract syntax tree and embed the syntax tokens into vectors. We evaluate the performance of our model on two widely used benchmark datasets, CWE-119 (Buffer Error) and CWE399 (Resource Management Error) from SARD. Experiments show that the F1 score of our model achieves 86.1% (CWE-119) and 90.0% (CWE-399) on two datasets, which is significantly better than the-state-of-the-art models. In particular, our model can directly mark the importance of different lines and different tokens, which can provide useful information for further vulnerability exploitation and repair.
Optimal Entanglement Distribution using Satellite Based Quantum Networks

Recent technological advancements in satellite based quantum communication has made it a promising technology for realizing global scale quantum networks. Due to better loss distance scaling compared to ground based fiber communication, satellite quantum communication can distribute high quality quantum entanglements among ground stations that are geographically separated at very long distances. This work focuses on optimal distribution of bipartite entanglements to a set of pair of ground stations using a constellation of orbiting satellites. In particular, we characterize the optimal satellite-to-ground station transmission scheduling policy with respect to the aggregate entanglement distribution rate subject to various resource constraints at the satellites and ground stations. We cast the optimal transmission scheduling problem as an integer linear programming problem and solve it efficiently for some specific scenarios. Our framework can also be used as a benchmark tool to measure the performance of other potential transmission scheduling policies.
Deep Learning based Modeling and Inference for Extracting Airspace Constraints for Planning

Natural Language Understanding and Extraction of Flight Constraints Recorded in Letters of Agreement This paper presents an automated information extraction and inference technique using natural language processing for extracting flight operational procedures and constraints embedded in heritage air traffic management documents. The extracted flight constraints can be digitized and fit into existing airspace information exchange models such as the Aeronautical Information Exchange Model (AIXM). This approach offers a digitized solution to disseminate airspace operating conditions to diverse air users and stakeholders in the National Airspace System (NAS). Furthermore, the digitized flight procedures can provide operational flexibility for emerging advanced air mobility providers and reduce traffic controller workload while maintaining current safety standards. To demonstrate this process, 1,972 Letters of Agreement (LOAs) have been selected for processing, named entity extraction, constraint identification and extraction. This dataset is derived from a subset of documents related to Air Route Traffic Control Centers (ARTCC) operations. We experimented with various traditional information extraction techniques, state-of-the-art machine learning and deep learning models to perform named entity recognition and pattern recognition on our dataset. We present the results from our experiments and demonstrate 99.0% F-1 score for named entity recognition, and a 96.6% accuracy for our entire workflow up to named entity recognition. We also discuss constraint definitions using generic patterned templates and extensions to this work in applying entity linking to digitally extracting relevant constraints.
Pulsar Candidate Selection Using Gaussian Hellinger Extremely Fast Decision Tree

Radio wave data gathered by pulsar finding telescopes are required to be classified while being streamed. The reason for that is the practical constraints of traditional machine learning algorithms on streaming datasets. Traditional machine learning algorithms would take considerable compute power, memory and time to give pragmatic results.(recent surveys collect data at the rate of 0.5 – 1 terabyte per second) Stream classification algorithms are specifically developed to address the above limitations and can classify data streams without taking up a lot of memory or training time. They relate with characteristics of data streams such as concept drift and limited memory. Extremely Fast Decision Tree is one of the stream classification algorithms that can learn incrementally when it sees new data. However, data from pulsar detecting datastreams are highly imbalanced (there are less examples of pulsars in the data than non-pulsar objects). Learning incrementally from such a datastream would be a destructive interference for the model’s precision (of detecting pulsars). In this research, we introduce an improved version of the Extremely Fast Decision Tree, that is able to learn imbalanced data streams. Our approach is fast, accurate, and avoids the pitfalls of class imbalance and concept drift.
Convolutional Neural Network Structure to Detect and Localize CTC Using Image Processing

Many cybersecurity attacks utilize Covert Timing Channels as a method to secretly transmit (steal) sensitive information from target networks such as untrusted Internet of Things (IoT) and 5G/6G networks. Such attacks aim to violate the confidentiality and privacy of the data that resides in the targeted networks by transmitting the stolen information in a stealth manner over a prolonged period of time to avoid detection by cyber defenses and anti-exfiltration tools.In this work, we proposed a novel approach that utilize novel Artificial Intelligence (AI) algorithms, in particular, deep learning to detect and localize covert channels over cyber networks. Taking advantage of the rapidly improving deep learning algorithms in image processing, we convert the malicious and normal network traffic (or packets) inter-arrival times to colored images. Then, we implement an AI-based approach using the popular deep learning algorithm Convolutional Neural Network (CNN) to process images and detect the ones that contain malicious CTC activities. Finally, we design and conduct a set of experiments to evaluate the ability of our proposed system to detect and localize CTC-based privacy attacks. The conducted experiments show that our approach yielded a high accuracy of 96.75% in detecting stealth covert channels.
Assessment method for camouflage performance based on visual perception

• The proposed model is comprehensive, including image color, structure, and texture. • The iCAM is applied in camouflage effect assessment for the first time. • The weights of the model are determined by subjective evaluation results. • The proposed model improves the consistency with human visual perception. Camouflage stealth is of great significance in concealing military targets and countering military reconnaissance, but the assessment methods for camouflage performance remain some problems, such as incomplete evaluation indexes and inconsistency with the visual perception. This study proposes a comprehensive assessment model, named comprehensive similarity, C S s u b for short. The model bases on subjective visual evaluation and contains three metrics of image color appearance, structure, and texture. The metrics are analysed by the image color appearance similarity (ICAS), structure similarity (ISS), and texture similarity (ITS), respectively, to measure the camouflage effectiveness between background and camouflage target images. The three weights, in C S s u b , are fitted through the results of subjective experiments. In the experiments, five ships are camouflaged in six sea states, a total of 30 camouflage scenes. Finally, seven evaluation methods, including the proposed model, are analyzed, and compared to predict camouflage effectiveness. The results show that C S s u b model has the best prediction performance. And the ICAS index, which is applied in camouflage assessment for the first time, accounts for a significant weight in the model, indicating that color has a great impact on image perception. So, this method based on visual perception has more superiority than others and can be used as an accurate and reliable assessment model in predicting camouflage performance.
Using Trajectory Smoothness Metrics to Identify Drones in Radar Track Data

The identification of unmanned aircraft systems (UAS) using trajectory data is considered. Specifically, a number of smoothness metrics are proposed, which can be used to distinguish UAS from other aerial objects even when they are engaged in accelerative maneuvers (non-constant-velocity flight). The metrics are evaluated on a data set from a UAS sense-and-avoid field test, which contains track data of aerial objects recorded by a vehicle-board radar system during a flight test. The metrics are found to effectively differentiate UAS from other objects such as birds for this data set. In addition, an initial statistical performance analysis of one of the smoothness metrics is undertaken, using 15 data sets deriving from multiple flight tests. The smoothness metric is shown to identify the target UAS with 95% accuracy (95% true positive rate), while achieving a false positive rate of less than 9%.