Reviewerly/exHarmony huggingface dataset:
Research on synchronous coherent jamming technology of 5G mobile phone signal

The traditional mobile phone signal blocker adopts high power broadband noise suppression technology, which has poor jamming effect and has adverse effects on the health of people in the shielded area. This paper proposes a synchronous coherent jamming technology that jams with 5G mobile phone signals. According to the characteristics of 5G base station downlink signals, the technology can conduct coherent jamming modulation to the synchronous signal in the process of mobile phone search and destroy the cell search process of mobile phone terminals. It is suitable for 5G, 4G, and other mobile phone terminals. The simulation results show that the jamming signal is coherent with the base station downlink signal, and has the advantages of narrow jamming band width and high power utilization.
Impact of Metaverse and Marketing Innovation on Digital Transformation

Business stakeholders have expressed a great deal of interest in the idea of boosting the value of their companies by fusing them with the emerging metaverse notion. The metaverse is a brand-new phenomenon that will soon completely transform both the digital and physical environments. In light of this, the author creates a framework for how the metaverse alters the universe. To comprehend the value of the metaverse in relation to digital transformation, this research aimed to examine the effects of metaverse and marketing innovation based on empirical data on how companies use cutting-edge technology in conjunction with marketing initiatives and their impact on digital transformation. The quantitative analysis applied to explore the research model, a descriptive, exploratory and analytical research design applied using convenient sampling. 61 digital marketing agencies were approached located in Dubai. Data collected from 196 respondents were incorporated for analysis using SmartPLS 4.0. The findings suggest a positive significant relationship of metaverse and marketing innovation with digital transformation.
Evaluate the Performance Parameters for Web browsers Empowered with Fuzzy logic

In recent years, the Web browser achieved huge popularity in academic as well as industrial circles. A large number of web services and applications are trying to make their performance efficient. Decreasing the waiting of fetch Web browser has become a real challenge. This paper presents a parameter estimation methodology and model that measure the performance of Web browser. A system that displays a wide and natural manner of thinking based on a Multiple Criteria Decision-Making process. In this research paper, the proposed system uses different kinds of parameters to estimate the efficiency of the browser empowered with the Fuzzy logic to elaborate on this problem.
IoT Skills for Executives: A Futuristic Review

Nowadays, the IoT plays an essential role across different industries and sectors. Although some researchers support the idea that the IoT has a positive impact on all aspects of society, others argue that the IoT is also bringing some disadvantages that will affect us. This research study aims to investigate the impact of IoT skills, how they affect executives and managers, and its future implications.The study included quantitative and qualitative approaches using questionnaire methods and a random sampling technique, as well as interviews with specialists in IoT fields. Meanwhile, four case studies were used to support the research results.The findings reveal that it has been commonly agreed that utilizing IoT has improved the efficiency and productivity of business operations. For instance, they are implementing intelligent systems, collecting and sharing data, managing and tracking operations and processes. On the other hand, regarding the risks and challenges, appropriate policymaking can avoid and resolve such issues.
Time-series anomaly detection in telemetry of ISS providing the reasons with FRAM and SpecTRM

Automatic time-series anomaly detection has been utilized for several applications such as general infrastructure systems. While accuracy and quickness are important factors for anomaly detection, explainability is crucial for safety-critical systems such as space systems. In this context, traditional machine learning-based methods of time-series anomaly detection such as Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) are not able to explain why symptoms of anomaly are detected. However, flight controllers of International Space Station (ISS) need to assess trends of telemetries and understand situations before making coordination with other domestic and foreign flight controllers and taking any actions corresponding to anomaly detections. In this research, we conducted the following three steps to detect signs of anomaly with rationales behind the anomaly, i.e., 1) selecting objective and explanatory variables, 2) conducting anomaly detections with time-series machine learning, 3) providing additional information with assessing the anomaly detections. The method was verified with the data of two anomaly events of Low Temperature loop (LTL) pump inverter of Japanese Experimental Module of the ISS in 2012 and 2014. There were some different trends of telemetries in 2012 and 2014. A selected objective variable was successfully predicted based on explanatory variables in normal period and away from the actual measured value in off-nominal period with LSTM for both cases. The analysis results showed the effectiveness of our new methods as the machine learning-based time-series predictive failure detection with identifying possible causes. Our proposed method can contribute to health monitoring of space systems and anomaly detections with showing possible reasons behind anomaly events. It is expected to widely contribute to the safety of space systems.
SICUP: A Comment Updating Approach based on Structural Information

High quality code comments are of great value for program maintenance. However, during the development process, developers often neglect to update corresponding comments when changing code. In such case, inconsistent comments are introduced which affect the maintainability of the software. In previous work, code changes are usually performed by treating the code as ordinary text and the structural information of the code are ignored. In this paper, we propose an approach named SICUP (Structural Information based Comment UPdater) to provide a new solution for comment updating tasks. SICUP uses the structural information of the code to help updating comments by constructing different sequences of ASTs. Experiments on a popular dataset demonstrates that SICUP outperforms CUP, which is an effective deep learning-based approach in terms of accuracy and recall.
Overview and Summary of Bruhat–Tits Theory

Contains a summary of Bruhat--Tits theory and closely related material. More precisely, in Section 1 we state most major results of the theory in the form of Axioms. This section can serve as a convenient reference for the expert as well as an initial overview for the beginner before diving into the specifics of the theory. Building on these axioms, in Section 2 we define and study the metric on the Bruhat--Tits building, in Section 3 we define the enlarged building, and in Section 4 we formulate various uniqueness properties of the building.
Agile Fixed-Wing UAVs for Urban Swarm Operations

Fixed-wing unmanned aerial vehicles (UAVs) offer significant performance advantages over rotary-wing UAVs in terms of speed, endurance, and efficiency. Such attributes make these vehicles ideally suited for long-range or high-speed reconnaissance operations and position them as valuable complementary members of a heterogeneous multi-robot team. However, these vehicles have traditionally been severely limited with regards to both vertical take-off and landing (VTOL) as well as maneuverability, which greatly restricts their utility in environments characterized by complex obstacle fields (e.g., forests or urban centers). This paper describes a set of algorithms and hardware advancements that enable agile fixed-wing UAVs to operate as members of a swarm in complex urban environments. At the core of our approach is a direct nonlinear model predictive control (NMPC) algorithm that is capable of controlling fixed-wing UAVs through aggressive post-stall maneuvers. We demonstrate in hardware how our online planning and control technique can enable navigation through tight corridors and in close proximity to obstacles.We also demonstrate how our approach can be combined with onboard stereo vision to enable high-speed flight in unknown environments. Finally, we describe our method for achieving swarm system integration; this includes a gimballed propeller design to facilitate automatic take-off, a precision deep-stall landing capability, multi-vehicle collision avoidance, and software integration with an existing swarm architecture.
Convolution Neural Network based Hand Gesture Recognition System

Strong hand gesture recognition has been essential in the area of human-computer interaction for a very long time. Due of their intricacy and breadth, these gestures are difficult for many people to understand, which hinders communication between those with and without speech impairments. There is a lot of active practical research being done in the field of computer vision because of the current boom in deep learning. Till the date, various image processing algorithms have used color and depth cameras to recognize hand gestures, however it is still difficult to classify movements from different subjects accurately. The objective of this study is to identify hand gestures by using a camera to quickly follow the region of interest (ROI), which in this case is the hand region, in the image range. In this project, the use of convolutional neural networks (CNNs) in an algorithm for real-time hand gesture recognition has been proposed in this paper. On a dataset of thirty-six hand gestures and 400 photos for each gesture, the suggested CNN is anticipated to achieve excellent accuracy.
Feature Selection Method Based On GWO-PSO for Coronary Artery Disease Classification

In this analysis, Optimization of Hybrid Grey Wolf technique for Feature Selection analysis is implemented. In many areas standard performance is given by Grey Wolf Optimizer's (GWO). To enhance the performance different GWO factors are signified GWO mainly based on the two conflicting concepts. This will exploit the optimal solutions. The performance of search algorithms is improved by balancing the both exploration and exploitation. For continuous search space problems are obtained from the original hybrid approach which is superior to performance. The main binary problem is fromfeature selection. Hence hybrid PSOGWO is also known as BGWOPSO. This is the best feature subset. To produce the best results, Euclidean separation matrix and K-Nearest Neighbor's classifier are used. Performance is evaluated using one common benchmark dataset from the UCI repository. According to the findings, the whale optimization algorithm, binary genetic algorithm, binary PSO, binary genetic algorithm, and Binary GWO (BGWO) provide the highest levels of performance in terms of accuracy, the selection of the most effective optimal features, and the amount of time required to compute.
A Novel Approach to Text Summarization of Document using BERT Embedding

This study examines and compares existing studies on various text summaries of documents and the processes associated with them. Despite the fact that the literature contains a large number of research contributions, we have critically and fully analysed current research and review papers that are relevant to text summary of document systems. The various approaches are classified based on the main ideas used in their procedures. The emphasis is on the concept utilised by the concerned authors, the approach used for experiments, and the performance evaluation measures. The researchers' claims are also emphasised. Our findings from the exhaustive literature review are presented together with the detected flaws. This study is very important for the comparative examination of various text summarizer approaches, which is necessary for addressing associated difficulties. On a review of the literature, we developed our own way in which we constructed a programme in Python and wrote it in the Spyder console using the Anaconda culture. The following standard libraries are used: NLTK for text processing, TensorFlow hub for getting the BERT pretrained model, correlation from the Sklearn library, and certain other standard libraries such as Panda for importing the dataset, Numpy, and so on. The data set must be imported after all of the libraries have been included. To summarise, the Kaggle datasets news summary training dataset was utilised. There are headlines, the full text, a synopsis, and links to related news stories for each story in the collection. In this experiment, the first 100 articles from the news summary collection were used. We analysed the outcome using the Rouge scoring system on both the created summary and the original summary. Rouge's average score for the first 100, 50, and 30 documents. The results obtained are encouraging.
Quantum assisted Genetic Algorithm for Sequencing Compatible Amino Acids in Drug Design

Using quantum computing for drug design is among the most promising applications in quantum technologies. Genetic algorithms with their evolutionary iterations make it a propitious approach in various tasks, including drug discovery, gene prediction, docking of ligands to receptors, and the design to combinatorial libraries. As its computational power and methods limits classical computing, quantum computing intends to break these limits with exponential computing capabilities. This application complements both quantum theory and genetic programming as we use true randomness with mutation and fitness function based on information encoded onto qubits storing quantum data. In this article, we present the results of encoding quantum data to our quantum genetic algorithm, which predicts the best possible drug structure to bind onto the target protein. The qubits hold the genome structure to perform bit string mutation over quantum gates. These results are later than compared to classical computing with various approaches in the evolutionary algorithm's parameters.
Computing Boundary Crossing Probabilities of General Empirical Processes

Order statistics play a fundamental role in the construction of empirical distribution functions, which are fundamental to empirical process theory and non- parametric statistics. In some applications, it is desirable to compute the joint cumulative distribution function (cdf) of d order statistics exactly. Efficient algorithms to compute this quantity are known when the data are i.i.d.; however, the task becomes significantly more challenging when relaxing either the identically distributed or the independence assumption. Existing methods for the non-i.i.d. setting obtain the joint cdf indirectly, by first computing and then aggregating over the marginal distributions. In this paper, we simplify an existing dynamic programming solution to achieve an exponential-in-d factor improvement in both time and space complexity over known methods. We detail the independent, non-identical setting, and then outline how our method extends to more general settings (e.g., dependent random variables) in an online appendix.
Generalized Scaling for the Constrained Maximum-Entropy Sampling Problem

The best practical techniques for exact solution of instances of the constrained maximum-entropy sampling problem, a discrete-optimization problem arising in the design of experiments, are via a branch-and-bound framework applied to a variety of concave continuous relaxations of the objective function. A standard and computationally-important bound-enhancement technique in this context is scaling, via a single positive parameter. We extend this technique to generalized scaling, employing a positive vector of parameters, we give mathematical results aimed at supporting algorithmic methods for computing optimal generalized scalings, and we give computational results demonstrating the usefulness of generalized scaling on benchmark problem instances.
Decentralized Finance, Non-Fungible Tokens and Money Laundering: Is the European Union Legal Framework Ready to Address Financial Crime Risks Arising from the Crypto Business while Fostering Innovation?

This thesis offers an analysis of Decentralized Finance underpinnings, particularly Non-Fungible Tokens and the challenges these innovations may pose over the European Union AML/CTF legal framework. On the one hand, the thesis will analyze the changes these innovations may bring to the art market and creative industry. On the other hand, it will focus on the financial crime risks arising from the increased ease to layer the crime proceeds on the blockchain.
Enhancing Semantic Relation Classification With Shortest Dependency Path Reasoning

Relation Classification (RC) is a basic and essential task of Natural Language Processing. Existing RC methods can be classified into two categories: sequence-based methods and dependency-based methods. Sequence-based methods identify the target relation based on the overall semantics of the whole sentence, which will inevitably introduce noisy features. Dependency-based methods extract indicative word-level features from the Shortest Dependency Path (SDP) between given entities and attempt to establish a statistical association between the words and the target relations. This pattern relatively eliminates the influence of noisy features and achieves a robust performance on long sentences. Nevertheless, we observe that majority of relation classification processes involve complex semantic reasoning which is hard to be achieved based on the word-level statistical association. To solve this problem, we categorize all relations into atomic relations and composed-relations. The atomic relations are the basic relations that can be identified based on the word-level features, while the composed-relation requires to be deducted from multiple atomic relations. Correspondingly, we propose the <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">At</b> omic Relation <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</b> ncoding and <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</b> easoning <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</b> odel (ATERM). In the atomic relation encoding stage, ATERM groups the word-level features and encodes multiple atomic relations in parallel. In the atomic relation reasoning stage, ATERM establishes the atomic relation chain where relation-level features are extracted to identify composed-relations. Experiments show that our method achieves state-of-the-art results on the three most popular relation classification datasets – TACRED, TACRED-Revisit, and SemEval 2010 task 8 with significant improvements.
Review on Blockchain in Bitcoin Security

Blockchain technology is required in the progress of mostly all industries. Privacy, network services, inspection, and database access inside digital platforms might all benefit from blockchain decentralized technology and privacy regulations. The nodes in the network produce, add, and authenticate blocks of data; blockchain is based on decentralized and secure decentralized protocols with no central authority or control. The use of blockchain technology across a range of manufacturing sectors has risen in recent years, according to this report. Also, this paper evaluates the study using a complete literature review, as well as exploring and providing insights into the security problems and risks associated with blockchain installations.
Young Driver Gaze (YDGaze): Dataset for driver gaze analysis

One of the most important factors in ensuring successful and safe driving in the road traffic network is the driver’s sense of sight. The driver can determine the environmental factors in the traffic with his gaze and can predict the next move after evaluating it with the current driving position. In this process, the focus of the driver’s gaze should be on the area to which the vehicle is heading. Driver eye movements and gaze analysis is a topic that has been intensively studied in recent years in “Intelligent Driver Assistance Systems”. In this study, a data set was prepared with a group of 40 participants between the ages of 18–28 in order to determine which points the driver looked at while driving. The dataset was created by having each participant look at the labeled areas on the vehicle in turn for one minute. Experimental studies lasted approximately 11 days. Approximately 350 hours of work was carried out to prepare the obtained data set.
Fault Diagnosis Method Based on CND-SMOTE and BA-SVM Algorithm

Abstract The problem of unbalanced data classification has gotten extensive attention in the past few years. Unbalanced sample data makes the fault diagnosis and classification accuracy rate low, and the capability to classify minority-class fault samples is restricted. To address the problem that the classification algorithm in machine learning has the insufficient capability to identify minority class samples for unbalanced sample data classification problems. Therefore, this paper proposes an improved support vector machine (SVM) classification method based on the synthetic minority over-sampling technique (SMOTE). For the sampler, an improved synthetic minority over-sampling technique based on the characteristics of neighborhood distribution (CND-SMOTE) algorithm is used to equilibrate the minority class samples and the majority class samples. For the classifier, the parameter optimization method of support vector machines based on the bat algorithm (BA-SVM) is used to solve the multi-classification problem of faulty samples. Finally, experimental results prove that the CND-SMOTE+BA-SVM algorithm can synthesize high-quality minority fault samples, increase the classification accuracy rate of fault samples, and decrease the time spent on the classification.
Study of Language Models for Fine-Grained Socio-Political Event Classification

Since the introduction of inductive transfer learning methods and transformers models in natural language processing, we have witnessed a state of the art results in various tasks like machine translation, text summarization, image captioning, sentiment analysis, etc. The cornerstone of our study was to analyze various language modeling architectures and use various techniques for the task of event classification in order to classify short text snippets, which consist of news reportings of socio-political events with 25 fine-grained event types. We used four different fine-tuned language modeling architectures: RoBERTa, XLNet, ELMo, and BERT for our research. The corpus used for the model preparation was a re-sampled subset of the ACLED event dataset consisting of all 25 event sub-classes. Our best-performing model achieved an F1 score of 0.80 on the final unseen test set. For the overall result evaluation, we also conducted an error analysis of the consistently misclassified event types.