Reviewerly/exHarmony huggingface dataset:
Parallel Local Feature Selection and High Dimensional Data Classification

Recent technological progress has expanded high-dimensional datasets. This phenomenon along with irrelevant and redundant features is led to a challenging feature selection process. The major objective of feature selection is to decrease the dimensions of such datasets by eliminating non-essential and irrelevant features resulting in an improvement in the performance of learning algorithms. An existing major challenge is that most of the feature selection methods intend to select a global feature group that is employed across the whole sample space. As each region of the sample space, with a special set of features, responds to the patterns correctly, the global feature selection methods are not efficient. A novel scheme of localized feature selection is presented in this paper, in which by parallel processing and distribution of data, classification is performed. In the proposed method, each region of the sample space is associated with its own distinct optimized feature set. Afterward, classification is applied to classifiers learned from each group. The simulation results on several real-world datasets on multiple classifiers demonstrate that the proposed local feature selection is superior to global feature selection methods.
Using a Hybrid Pseudorandom Number Generator for Cryptography in the Internet of Things

The rapid growth of mobile phones, telecommunication networks, and the internet resulted in the advent of the Internet of Things (IoT). As a result, the need for security algorithms to keep up with these advances is becoming more pressing. One method of symmetric encryption is a stream cipher, which encrypts one bit or one byte of the digital data stream at a time. The cipher is unbreakable if the cryptographic keystream is random. However, the keystream must be delivered to all users in advance through a separated and protected channel. The solution proposed in this paper is based on the combine of both logistic maps and Geffe generator techniques by using an intelligent master key to generate a random key of stream cipher. The suggestion is easy to implement in hardware. It provides a high degree of protection. Additionally, the suggested method was compared with the other algorithms by utilizing the same 512-bits plaintext as well as the same master key as input parameters. The proposed cipher’s findings are successful in the statistical NIST test, despite the failure of some test results for some comparative algorithms. Also, the suggestion was evaluated by other different tests including, histogram and correlation coefficient analysis, Shannon entropy analyses, and keyspace and key sensitivity. The tests results proved that the proposed method is effective in security issues.
The Title of the Paper: A Query Expansion Method Oriented Sentence Component Similarity Calculation

Using short sentences as input for information retrieval can obtain richer semantic features than keywords and their combination. On the basis of Sentence trunk extraction, a Query Expansion Oriented sentence-Component Similarity Calculation (QE-SCSC) is proposed. In this method, keywords of different components in query statements were extracted, and the semantic extension of keywords was carried out from similarity and relevance based on ontology, and the weight of extended keywords is adjusted by comprehensively considering the original weight, relation weight, component weight and proportion. Experimental results show that the QE-SCSC method can greatly improve the query accuracy under the premise of ensuring the recall ratio.
RLEPSO:Reinforcement learning based Ensemble particle swarm optimizer✱

Evolution is the driving force behind the evolution of biological intelligence. Learning is the driving force behind human civilization. The combination of evolution and learning can form an entire natural world. Now, reinforcement learning has shown significant effects in many places. However, Currently, researchers in the field of optimization algorithms mainly focus on evolution strategies. And there is very little research on learning. Inspired by these ideas, this paper proposes a new particle swarm optimization algorithm Reinforcement learning based Ensemble particle swarm optimizer (RLEPSO) that combines reinforcement learning. The algorithm uses reinforcement learning for pre-training in the design phase to automatically find a more effective combination of parameters for the algorithm to run better and Complete optimization tasks faster. Besides, this algorithm integrates two robust particle swarm variants. And it sets the weight parameters for different algorithms to better adapt to the solution requirements of a variety of different optimization problems, which significantly improves the robustness of the algorithm. RLEPSO makes a certain number of sub-swarms to increase the probability of finding the global optimum and increasing the diversity of particle swarms. This proposed RLEPSO is evaluated on an optimization test functions benchmark set (CEC2013) with 28 functions and compared with other eight particle swarm optimization variants, including three state-of-the-art optimization algorithms. The results show that RLEPSO has better performance and outperforms all compared algorithms.
Single Infrared Image Super-Resolution with Lightweight Self-corrected Attention Network

We propose a deep-learning-based single image super-resolution (SISR) method for the infrared imaging system. We construct a self-corrected attention network (SCANet) to reconstruct the high-resolution (HR) infrared image of a target from the low-resolution (LR) one. Specifically, we design a self-corrected attention block (SCAB) that incorporates upsampling and downsampling operation with attention module in recursive and feedback manners. With SCAB, we train an end-to-end network with infrared images to achieve the reduction of parameters and computational cost. The effectiveness of the proposed method is verified by extensive experiments. The results demonstrate that the SCANet can realize single infrared image super-resolution with multiple upscaling factors (e.g., x 2, x 3 and x 4).
A Formalism for Specifying Capability-based Task Allocation in MAS

Task allocation, as an important issue in multi-agent systems (MAS), is defined as allocating the tasks to the agents such that maximum tasks are performed in minimum time. The vast range of application domains, such as scheduling, cooperation in crisis management, and project management, deal with the task allocation problem. Despite the plethora of algorithms that are proposed to solve this problem in different application domains, research on proposing a formalism for this problem is scarce. Such a formalism can be used as a way for better understanding and analyzing the behavior of real-world systems. In this paper, we propose a new formalism for specifying capability-based task allocation in MAS. The formalism can be used in different application domains to help domain experts better analyze and test their algorithms with more precision. To show the applicability of the formalism, we consider two algorithms as the case studies and formalize the inputs and outputs of these algorithms using the proposed formalism. The results indicate that our formalism is promising for specifying the capability-based task allocation in MAS at a proper level of abstraction.
Comparison of two target detection algorithms based on remote sensing images

Theoretically, the detection accuracy of two-stage target detection algorithm is higher than that of single-stage target detection algorithm.The single-stage target detection algorithm realizes end to end target detection and greatly improves detection speed while losing part of accuracy.However, different networks have different extraction capabilities for remote sensing image features. In this paper, two algorithms, Faster RCNN and YOLOv3, were respectively used to train and test remote sensing image data sets. The results show that YOLO V3 has higher detection accuracy and Faster speed for remote sensing image.
Cooperative Task Assignment of Heterogeneous UAVs in SEAD Mission Using A Mutil-Type Genes Chromosome Genetic Algorithm

This paper puts forward a novel multi-type genes chromosome genetic algorithm(MTGC-GA) to solve the cooperative multiple task assignment problem (CMTAP) in the SEAD mission. MTGC-GA considers not only heterogeneity, kinematic and resource constraints of the UAVs, but also the timing-precedence constraint between tasks. Considering the situation of deadlock in solutions caused by timing-precedence constraint and task execution sequences of UAVs, we abstract the execution process of tasks into a directed graph and design a deadlock-free algorithm based on the directed graph to detect and remove the deadlock. In addition, specific genetic operators such as initialization, fitness, mutation, and crossover are designed to ensure the feasibility of chromosomes during the process of evolution. MTGC-GA also introduces the Dubins curve and path extension strategy to generate the real flight paths of UAVs. Finally, Monte Carlo simulation experiments show that MTGC-GA can provide a flying solution for UAVs and achieve better performance than random search method (RSM).
Realizing the Potential of Blockchain in Cancer Research

One of the leading causes of preventable deaths across the globe with almost 10 million deaths in 2020. Scientists and researchers are working day in, day out to develop new methods for the earliest possible diagnosis of this disease and thus save millions of human lives. Today, the healthcare sector is miraculously backed by technologies like machine learning, the Internet of Things (IoT), and computational intelligence which aid in the early prediction of the disease with precision, thereby improving the chances of survival and also providing a better healthcare experience with lower treatment morbidity. Blockchain, the technology behind Bitcoin, is another revolutionary technology that can be combined with it. Blockchain is a kind of decentralized and distributed ledger which uses a peer-to-peer network to make a continuously growing chain of chronological records to form a digital ledger. The fact that it has no central authority offers numerous advantages like transparency, immutability, improved traceability, and enhanced security of the transactions being carried out. Blockchain has incredibly useful applications in cancer research ranging from secure data storage to full patient ownership of records. This chapter predominantly aims to highlight how its characteristics can be utilized to revolutionize cancer research and also discuss its future prospects.
Seeing like a state (of surveillance)

column Share on Seeing like a state (of surveillance) Authors: Jaz Hee-jeong Choi RMIT University RMIT UniversityView Profile , Roopa Vasudevan University of Pennsylvania University of PennsylvaniaView Profile Authors Info & Claims InteractionsVolume 29Issue 2March - April 2022 pp 26–28https://doi.org/10.1145/3511669Published:25 February 2022Publication History 0citation1,131DownloadsMetricsTotal Citations0Total Downloads1,131Last 12 Months1,131Last 6 weeks39 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
A Compehencive Review of Ontology-based Information Systems for Educational Process Support

The paper analyses the research on the use of ontologies for education informational systems development. Creation of curriculums, development of different courses and educational materials that deals with this courses, improvement of mechanisms of academic sources recommendations, academic evaluation, higher education management, integration of different types of educational data, information and repositories based on ontologies shows that ontologies are a useful means of organizing information for educational process. Various systems developed using ontologies and used for different purposes in educational process are described. As well, the research presents cognitive information system to provide educational activities based on the principles of ontological modelling, system structure is described, its main characteristics and software platform for its development are depicted.
Image Dehazing Using Conditional Patch Generative Adversarial Network

research-article Share on Image Dehazing Using Conditional Patch Generative Adversarial Network Authors: Changyou Shi College of Communication NCOs,Army Engineering University of PLA, China College of Communication NCOs,Army Engineering University of PLA, ChinaView Profile , Jianping Lu College of Communication NCOs,Army Engineering University of PLA, China College of Communication NCOs,Army Engineering University of PLA, ChinaView Profile , Qiang Sun College of Communication NCOs,Army Engineering University of PLA, China College of Communication NCOs,Army Engineering University of PLA, ChinaView Profile , Jing Zhou College of Communication NCOs,Army Engineering University of PLA, China College of Communication NCOs,Army Engineering University of PLA, ChinaView Profile , Rongze Xia College of Communication NCOs,Army Engineering University of PLA, China College of Communication NCOs,Army Engineering University of PLA, ChinaView Profile , Wei Huang College of Communication NCOs,Army Engineering University of PLA, China College of Communication NCOs,Army Engineering University of PLA, ChinaView Profile Authors Info & Claims ACAI'21: 2021 4th International Conference on Algorithms, Computing and Artificial IntelligenceDecember 2021 Article No.: 15Pages 1–7https://doi.org/10.1145/3508546.3508561Online:25 February 2022Publication History 0citation11DownloadsMetricsTotal Citations0Total Downloads11Last 12 Months11Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Geochronology of the Polycyclic Granulite Terrain of The Eastern Ghats Mobile Belt: An Overview

The Eastern Ghats Mobile Belt is dominantly characterized by the granulite facies of high-grade metamorphic rocks lying along the eastern coast of the Indian subcontinent.These polycyclic high-grade rocks include several blocks bounded by faults in the northern margin and a prominent shear zone towards the western margin.Recent imputes made in the EGMB proclaim various smaller domains characterized by their distinct tectono-metamorphic evolutions.However, the correlation made between the various metamorphic and tectonic events with respect to their geochronologic ages still lack solid results.As such, we contemplate to impart an overview based on the observations made from the available isotopic data of the Eastern Ghats Mobile Belt.
<scp>WoT</scp>‐based Gaussian process multi‐view learning for real‐time monitoring and identification

With the rapid development of imaging and communication technology, monitoring equipment is becoming more and more popular, and how to realize the real‐time monitoring and identification of key figures has become an important research direction. However, the traditional monitoring and identification systems usually upload the collected data to the server directly, and then the monitoring staff manually identifies them, or uses the artificial intelligence algorithm to identify the data stored in the past, which may lead to missed detection or false detection, and the data uploading manner will take a lot of time, so that the final identification result does not have real‐time. To solve these issues, based on the Web of Things (WoT) technology, a Gaussian process multi‐view learning system is proposed in this paper. Specifically, the Gaussian process is first used to learn multi‐view fusion features, and the identification is carried out based on these fusion features. Secondly, according to the identification results, only the monitoring data of key figures or suspected key figures are uploaded to the server and displayed at the monitoring end, so as to effectively reduce the amount of data to be transmitted. Experimental results show that the proposed Gaussian process multi‐view learning algorithm can effectively identify multi view face images.
Use of Blockchain Technology in Automation of Ad-Hoc Leasing Agreements

In this research, we have delved into the industry application of BlockChain (BC) technology, Internet of Things (IoT) within the real estate sector, more specifically the leasing of a venue space. With the concept of ad-hoc leasing not picking up due to bureaucratic and learning processes currently in place, we have developed a prototype that fully automated the leasing agreement, monitor, assess and settlements of payments. The use of BC has proven to be an ideal tool to ensure that every test step within the leasing agreement is properly audited given access to all parties including legislative and taxation personnel to monitor such transactions. We have created a number of scenarios utilising our prototype to consider different potential costings of this technology given the challenges around cryptocurrency more specifically the fluctuations in price indexes which are quite considered as one of the irreparable reasons for mass adoption of this technology. The use of IoT has proven that the leasing process flow can be automated with few devices and helped to monitor the specific rented office space.
Evaluating the Work Efficiency of the Library in the Web Environment

The article deals with assessing the efficiency of website management (EWM) in order to its development and increase the efficiency of its functioning. The concept “efficiency” in relation to the activities of the library in the web environment is analyzed It was concluded that the efficiency of a library website development can be measured by assessing the dynamics of webometric data, and one can get important information on positive or negative effect of the library website management process. A point-based methodology for assessing EWM has been developed, which makes it possible not only to control the process of managing the library own website, but also allows one the comparison of the evaluation coefficients of the EWM in other libraries, regardless of their type. The obtained coefficient for the evaluation of the EUS library indicates how effectively the website is being developed in the information web space. The proposed methodology is quite flexible and can vary depending on the set of key tools.
Research on Crowd Counting Algorithm Based on Multi-scale Adaptive Network

The variable spatial scale and crowd distribution in crowd images are the main challenges faced by crowd counting problems in recent years. In order to solve the above problems, a crowd counting method based on a multi-scale adaptive network is proposed in this paper. The first 10 layers of the VGG-16 network are used to extract basic features, and the spatial pyramid pooling layer is introduced to make the network adapt to images of any size. Then, multi-scale features are extracted through hybrid dilated convolution, and contrast features are obtained by comparing with basic features. Finally, the weight and density map are calculated to obtain the number of people according to contrast features. The experimental results on the Shanghai Tech and UCF_CC_50 datasets show that, compared with the previous best method, the MAE of the two parts of the Shanghai Tech dataset in this paper is reduced by 1.1 and 0.1, respectively, the MSE is the same in part A, and the part B is reduced by 0.2. On the UCF_CC_50 dataset, the MAE is reduced by 10.9, and the MSE is reduced by 61.4. It shows that the method proposed in this paper has better accuracy and robustness.
Significance of Syntactic Type Identification in Embedding Vector based Schema Matching

Data Interoperability provides a bridge between information systems to store, exchange and consume heterogeneous data. In order to achieve this goal, schema maps provide the necessary foundations. Traditional solutions rely on expert generated rules, ontologies, and syntactic matching to identify the similarity between attributes in the various data schema. While previously we have presented the effectiveness of transformer based models and unsupervised learning to calculate attribute similarities, in this paper we present the additional application of a naive syntactic similarity measurement We have evaluated the results of agreement between the computed and human annotated results, in terms of Mathews Correlation Coefficient (MCC). These results indicate that on weighted comparison there is no positive effect of including naive syntactic similarity in addition to semantic similarity.
Robust Non-parameter Clustering Algorithm Based on Saturated Neighborhood Graph

Clustering is a very popular technique in data mining applications for discovering patterns in underlying data. However, most traditional clustering algorithms need to manually set one or more parameters so that it is very difficult to select an appropriate parameter for each dataset. To solve the above problems, a robust non-parameter clustering algorithm based on the saturated neighborhood graph is proposed. It uses the natural neighbor search algorithm to adaptive obtain the nearest neighbor information for each data points, then constructs a saturated neighborhood graph for clustering. The proposed algorithm does not require any parameters due to the non-parametric characteristics of natural neighbors. Moreover, the algorithm is suitable for clustering of complex manifold data. Experiments show that the algorithm has excellent performance on both clean and noisy datasets.
Generating targeted e-mail at scale using neural machine translation

Advances in deep learning, specifically in Neural Machine Translation (NMT) [1, 20, 28], have opened up new application opportunities for generating human-readable text by machines. In this paper, we discuss one such application – the generation of spearphishing e-mail at scale – that has ramifications for cybersecurity. To this end, the ability to characterize text generated by people and by machines is important, and specific characteristics of text could be used as differentiating features. For example, we could evaluate whether the generated text is grammatically well-formed or semantically relevant to some topic. However, existing metrics for evaluating NMT models do not adequately address these needs. As a result, we propose new metrics that capture both well-formedness and topical relevance to evaluate the quality of text. We show that the proposed metrics corroborate the findings of existing metrics and provide additional insights. Although the metrics consistently indicate that text generated by state-of-the-art NMT models possess grammatical structure, the machine-generated text differ in performance with respect to the metrics when compared to human-generated text. By using these new metrics, we assessed whether the automatically-generated content would pass a spam/spearphishing filter [6]. The filter performs poorly in distinguishing between human-generated and machine-generated text indicating the robustness of existing models.