Reviewerly/exHarmony huggingface dataset:
Adaptive Binary Bat and Markov Clustering Algorithms for Optimal Text Feature Selection in News Events Detection Model

Wrapper Feature Selection (FS) methods based on the Binary Bat Algorithm (BBA) have recently been employed in a variety of detection applications to determine the most relevant feature subset. Despite the outstanding achievement of BBA in these domains, BBA has never been applied in Event Detection (ED). In our recent work, a novel wrapper FS approach based on BBA and Markov Clustering (MCL) method has been developed to bridge this gap and combat the curse of high dimensionality feature space for heterogeneous news text documents. However, ED from a massive number of heterogeneous news text documents with varying text lengths is a challenging task. The exploration performance of the BBA declines as the scale of the feature space grows due to the fast convergence rate problem that causes the BBA to fall into local optimum solutions. BBA’s loudness ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$A$ </tex-math></inline-formula> ) and emission rate ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$r$ </tex-math></inline-formula> ) are significantly responsible for controlling the convergence behaviour. As a result, this study proposes two adaptive techniques for the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$A$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$r$ </tex-math></inline-formula> parameters to adjust BBA’s convergence behavior as the dataset size changes. A new variant called Adaptive BBA (ABBA) with MCL (ABBAMCL) is proposed to improve the performance of the ED model. The ABBAMCL method has been tested over 10 benchmark datasets and two primary Facebook news datasets using several evaluation measures. The empirical results demonstrate the ability of ABBAMCL to identify a small number of informative features to detect real-world events from heterogeneous news text documents. Furthermore, with a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> -value of 0.00, the statistical results show that the ABBAMCL FS method based on the proposed controlling techniques outperforms most of other FS methods in producing high-quality event clusters.
Blockchain and Machine Learning for Fraud Detection: A Privacy-Preserving and Adaptive Incentive Based Approach

Financial fraud cases are on the rise even with the current technological advancements. Due to the lack of inter-organization synergy and because of privacy concerns, authentic financial transaction data is rarely available. On the other hand, data-driven technologies like machine learning need authentic data to perform precisely in real-world systems. This study proposes a blockchain and smart contract-based approach to achieve robust Machine Learning (ML) algorithm for e-commerce fraud detection by facilitating inter-organizational collaboration. The proposed method uses blockchain to secure the privacy of the data. Smart contract deployed inside the network fully automates the system. An ML model is incrementally upgraded from collaborative data provided by the organizations connected to the blockchain. To incentivize the organizations, we have introduced an incentive mechanism that is adaptive to the difficulty level in updating a model. The organizations receive incentives based on the difficulty faced in updating the ML model. A mining criterion has been proposed to mine the block efficiently. And finally, the blockchain network istested under different difficulty levels and under different volumes of data to test its efficiency. The model achieved 98.93% testing accuracy and 98.22% Fbeta score (recall-biased f measure) over eight incremental updates. Our experiment shows that both data volume and difficulty level of blockchain impacts the mining time. For difficulty level less than five, mining time and difficulty level has a positive correlation. For difficulty level two and three, less than a second is required to mine a block in our system. Difficulty level five poses much more difficulties to mine the blocks.
ContextMiner: Mining Contextual Features for Conceptualizing Knowledge in Security Texts

This paper presents <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ContextMiner</i> , a novel natural language processing (NLP) framework to automatically capture contextual features for the purpose of extracting meaningful context-aware phrases from cybersecurity unstructured textual data. The framework utilizes basic attributes such as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">part-of-speech</i> tagging, dependency parsing, and a domain-specific grammar to extract the contextual features. The effectiveness and applications of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ContextMiner</i> are evaluated and presented from two different perspectives: qualitative and quantitative. As for the qualitative analysis, our case studies show that the proposed framework is capable of retrieving additional contents from the given texts, both in a labeled and unlabeled setting, and thus building context-aware phrases in comparison with existing approaches. From a quantitative point of view, we evaluate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ContextMiner</i> as a pre-processing step to perform named entity recognition (NER). Our results show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ContextMiner</i> reduces the corpus up to 70% while maintaining 85% of its relevant entities, with a small drop in the classification metrics. Finally, we explored the utilization of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ContextMiner</i> in the construction and reasoning of knowledge graphs.
Dynamic strain anomalies detection at Stromboli before 2019 vulcanian explosions using machine learning

Identifying and characterizing the dynamics of explosive activity is impelling to build tools for hazard assessment at open-conduit volcanoes: machine learning techniques are now a feasible choice. During the summer of 2019, Stromboli experienced two paroxysmal eruptions that occurred in two different volcanic phases, which gave us the possibility to conceive and test an early-warning algorithm on a real use case: the paroxysm on July, 3 was clearly preceded by smaller and less perceptible changes in the volcano dynamics, while the second paroxysm, on August 28 concluded the eruptive phase. Among the changes observed in the weeks preceding the July paroxysm one of the most significant is represented by the shape variation of the ordinary minor explosions, filtered in the very long period (VLP 2–50 s) band, recorded by the Sacks-Evertson strainmeter installed near the village of Stromboli. Starting from these observations, the usage of two independent methods (an unsupervised machine learning strategy and a cross-correlation algorithm) to classify strain transients falling in the ultra long period (ULP 50–200 s) frequency band, allowed us to validate the robustness of the approach. This classification leads us to establish a link between VLP and ULP shape variation forms and volcanic activity, especially related to the unforeseen 3 July 2019 paroxysm. Previous warning times used to precede paroxysms at Stromboli are of a few minutes only. For paroxysmal events occurring outside any long-lasting eruption, the initial success of our approach, although applied only to the few available examples, could permit us to anticipate this time to several days by detecting medium-term strain anomalies: this could be crucial for risk mitigation by prohibiting access to the summit. Our innovative analysis of dynamic strain may be used to provide an early-warning system also on other open conduit active volcanoes.
Security Awareness Domain and Resources (SADAR) Framework Based on Activity Domain for Product Use: Case Study in Banking Industry

Enterprise Resource Planning (ERP) provides a real-time data and information on a business process effectively and efficiently, which can determine the success over its operational activity. Therefore, one factor that lead to the failures related to many employees have been ignored information security awareness (ISA) in utilizing the ERP features. This study was designed to develop the framework of ISA by implementing previous framework of SADAR through introducing new domain of activity into the antecedent. It is emphasized the competence of employee with capability and skill to transform their formulation of awareness in the mind into the work flow, activity pattern and organization culture. From 20 hypotheses that were proposed, the results rejected 7 hypotheses, which are IC to AT, RI to SP, BV to ISA, PP to ISA, SP to ISA, CW to ISA and CM to ISA as can be seen the detail in the discussion.
Corner‐based object detection method for reactivating box constraints

Corner-based detectors usually generate a large number of false detection boxes because of insufficient attention to the detection area. Recent corner-based detectors can achieve good performance, but the training equipment requirements have greatly increased. For example, due to the dense network structure and the large input image size, CenterNet requires expensive equipment for network training (e.g. Tesla V100). Its performance will be greatly reduced when a more mainstream and cheaper device is used for fine-tuning. The high equipment requirements make it difficult for most researchers to follow up these studies. In this work, CenternessNet, a detector that adds additional box-edge length constraints to CenterNet is proposed, thereby allowing the network to be trained on more general devices and obtain a better performance. It simply introduces the box as a constraint into the corner-based network. In this way, the method improves the ability to aggregate corners during training and enhances the model's ability to discriminate the corners of objects in the same category to some extent. The method achieves a better performance than other corner-based detection networks trained on similar low-memory devices.
Practical Software-Based Shadow Stacks on x86-64

Control-Flow Integrity (CFI) techniques focus often on protecting forward edges and assume that backward edges are protected by shadow stacks. However, software-based shadow stacks that can provide performance, security, and compatibility are still hard to obtain, leaving an important security gap on x86-64. In this article, we introduce a simple, efficient, and effective parallel shadow stack design (based on LLVM), FlashStack , for protecting return addresses in single- and multi-threaded programs running under 64-bit Linux on x86-64, with three distinctive features. First, we introduce a novel dual-prologue approach to enable a protected function to thwart the TOCTTOU attacks, which are constructed by Microsoft’s red team and lead to the deprecation of Microsoft’s RFG. Second, we design a new mapping mechanism, Segment+Rsp-S , to allow the parallel shadow stack to be accessed efficiently while satisfying the constraints of arch_prctl() and ASLR in 64-bit Linux. Finally, we introduce a lightweight inspection mechanism, SideChannel-K , to harden FlashStack further by detecting entropy-reduction attacks efficiently and protecting the parallel shadow stack effectively with a 10-ms shuffling policy. Our evaluation on SPEC CPU2006 , Nginx, and Firefox shows that FlashStack can provide high performance, meaningful security, and reasonable compatibility for server- and client-side programs on x86-64.
On Usability of Hash Fingerprinting for Endpoint Application Identification

In network security, a common challenge is the ability to gain information about the communicating endpoints based only on the network traffic. Methods for gaining end-point awareness on the network level by fingerprinting different network protocol layers have existed for long. A fairly recent addition to these techniques have been different hash finger-printing algorithms, such as JA3 and JA3S, that can be used for identifying the communicating endpoint applications of the network connection. These algorithms pick a suitable set of protocol specific parameters and concatenate their values into a string. An MD5 hash value is calculated from this string, which comprises the fingerprint. In this article we contest the use of the MD5 hash in the fingerprinting process, and propose that the original string of concatenated protocol parameter values should be used instead. We argue that the original string provides more value for the network security landscape.
Height and Weight Estimation of an Individual from Virtual Visuals

The rapid growth of technology has made people lead digital dynamic lives, where technology dominates normal human life. With the advancements in technology, there is a constant update to new methods, and techniques replacing the outdated existing methodologies. Adapting to the improvements in technology, this paper proposes a novel approach to estimate the height and weight of a person. Despite using measuring instruments, the orthodox style of knowing these attributes of a person, these values can be computed with the use of a person’s visuals in real-time or virtual images. After leveraging some considerable amount of attention to the human face, the information that a face can exhibit motivated us to predict an individual’s height, and weight by using facial traits. Using this information, and available applications, a novel algorithm has been developed to estimate the height, and weight of an individual with the help of either a photograph or video consisting full-length image. In different to other previous models, our model predicts the height and weight of an individual without any external reference object in the video frame or the photograph. And when this model is tested with 50 full-length images acceptable results were seen, with the mean absolute height difference of 6.9 cms, and 3.2 kgs of the weight difference between predicted and original values. OpenCV, Python, Mediapipe, and Machine learning algorithms are the technologies used to implement this gender-based height and weight prediction system.
NBA web portal: A Comprehensive Survey on NBA Accreditation and MERN Stack for the Purpose of Implementing a Portal

In recent years, India has witnessed tremendous growth in the number of institutions that provide higher education. India now has over 4000 engineering colleges in various states which include IITs, NITs, Central Universities departments, State Universities departments, constituent colleges, affiliated institutions, Government Aided Institutions, Institutes under Private Universities, Institutes under State Universities, and Autonomous Institutions. The demand for education and training has become more critical than before but of late there has been a drop in the quality of education. There may have been an outburst in the number of educational institutions but over the years, the quality itself has not seen a very positive impact. The Government of India, Ministry of Human Resource Development has established NAAC under UGC and NBA under AICTE in 1994 to look into the quality aspects of HEI and undertake accreditation. To maintain quality standards, Engineering institutions are now being encouraged to apply for NAAC accreditation on an institutional level as well as NBA accreditation for individual programmes. NBA accreditation has now become a prerequisite for technical institutions to seek autonomous status from university/UGC. This paper aims to study the different aspects of NBA accreditation and help us understand its importance in addition to exploring the possibility of this process even being digitally carried out with the help of a customized web portal.
A Novel Credit Grounded Job Scheduling Algorithm for the Cloud Computing Environment

Cloud computing is kind of presumably the main technology utilized lately, which permits clients (people and association) to get to registering assets (application tool, equipment, and program) as administrations distant over the web. Cloud computing is recognized since customary registering standards through its skillfulness, change expenses, availability, dependability, and on-request pay-more only as cost rise administrations. As cloud computing is serving a huge quantity of clients concurrently, it should meet all client demands requests with superior and assurance of the Quality of Service (QoS). In this manner, a proper job scheduling algorithmic program to meet these solicitations reasonably and productively needs to be executed. The job scheduling problem is an exceedingly the foremost basic issues in a cloud computing environment since cloud execution relies principally upon it. There are different sorts of scheduling algorithms; most of them are unit static scheduling algorithms that are applicable for immense scope for little or medium-scale cloud computing; and dynamic scheduling algorithms that are applicable for huge immense scope cloud computing environments. This paper presents a further developed scheduling algorithm, subsequently breaking down the customary algorithms which depend on client need and task length. After they arrive, people who are hyper-focused on errands are not given any special significance. These aspects are taken into account in the proposed approach.
A Convolutional Autoencoder based Keyword Spotting in Historical Handwritten Devanagari Documents

There exist huge amount of valuable historical Devanagari documents archived in many national libraries that need to be preserved in digital form. Additionally, there is a growing requirement in the area of document image processing for automation and information extraction from old handwritten documents. Retrieval of relevant information from handwritten historical document images would ideally necessitate an efficient An alternate approach to accurate document transcription is to use a keyword spotting method. The keyword spotting system's primary application is to create digital libraries that facilitate quick searching and browsing of old manuscripts in order to preserve the world's cultural heritage. For word spotting in handwritten manuscripts, we propose a convolution neural network (CNN) architecture based on Autoencoder representation. The proposed technique was demonstrated using historical handwritten Devanagari manuscript which is collected from Oriental Research Institute of University of Mysore, Mysore. As a result, the proposed convolution neural network (CNN) method exhibits superior accuracy with favorable results.
Automatic recognition and assessment of physical exercises from RGB images

Physical exercises are important for a healthy life. However, many people do the exercises without professional assistance, especially when practicing at home during Covid-19. Inappropriate exercising can negatively impact and even result in muscle pain. In this paper, an exercise coaching application is developed to understand what the user is doing and provide useful assessments and guidelines to assist the users. The proposed application takes RGB image sequences from any off-the-shelf cameras widely integrated into smartphones or laptops as input. First, skeleton sequences are extracted from RGB images using the public tool Google MediaPipe. Then, a real-time action recognition based on the temporal sliding window and DD-Net model is proposed to determine the action class. Two frame-based and sequence-based scores are estimated to provide a quantitative assessment. Finally, a tool with GUI and a database are developed.
Deep Learning Models for Skeleton-Based Action Recognition for UAVs

Human action recognition (HAR) is an important task for UAVs for instant decision-making from captured videos. HAR for UAVs is a challenging task due to the UAVs’ motion, attitudes, and view changes during flight. Moreover, UAVs’ video sequences may suffer from blurs and low resolution. All these issues cause difficulty in HAR for UAVs, necessitating the quest for the HAR method that considers UAV data characteristics. In this paper, we revisit some state-of-the-art deep learning methods and evaluate their performance on the UAV-Human dataset- the largest public UAV dataset up to now. Based on the evaluation, we propose a new framework that combines AAGCN and MS-G3D through a Feature Fusion module for data pre-processing in all streams. Experimental results show that our proposed method outperforms state-of-the-art methods on the UAV-Human dataset.
NCFS: new chaotic fuzzy system as a general function approximator

Conventional fuzzy systems (type-1 and type-2) are universal approximators. The goal of this paper is to design and implement a new chaotic fuzzy system (NCFS) based on the Lee oscillator for function approximation and chaotic modelling. NCFS incorporates fuzzy reasoning of the fuzzy systems, self-adaptation of the neural networks, and chaotic signal generation in a unique structure. These features enable the structure to handle uncertainties by generating new information or by chaotic search among prior knowledge. The fusion of chaotic structure into the neurons of the membership layer of a conventional fuzzy system makes the NCFS more capable of confronting nonlinear problems. Based on the GFA and Stone-Weierstrass theorems, we show that the proposed model has the function approximation property. The NCFS performance is investigated by applying it to the problem of chaotic modelling. Simulation results are demonstrated to illustrate the concept of function approximation.
Lightweight human pose estimation algorithm based on polarized self-attention

In recent years, human pose estimation has been widely used in human-computer interaction, augmented reality, video surveillance, and many other fields, but the task of pose estimation still faces many challenges. To address the large number of parameters and complicated calculation in the current mainstream human pose estimation network, this paper proposes a lightweight pose estimation network (Lightweight Polarized Network, referred to as LPNet) based on a polarized self-attention mechanism. First, ghost convolution is used to reduce the number of parameters of the feature extraction network; second, by introducing the polarized self-attention module, the pixel-level regression task can be better solved, the lack of extracted features due to the decrease in the number of parameters can be reduced, and the accuracy of the regression of human keypoints can be improved; finally, a new coordinate decoding method is designed to reduce the error in the heatmap decoding process and improve the accuracy of keypoint regression. The method proposed in this paper was evaluated on the human keypoint detection datasets COCO and MPII, and compared with the current mainstream methods. The experimental results show that the proposed method greatly reduces the number of parameters of the model while ensuring a small loss in accuracy.