Reviewerly/exHarmony huggingface dataset:
Evalt: Authenticate Implicitly Before Attacks

Privileged credentials are one of the key targets of attackers. Password authentication is plagued by phishing scams and keyloggers for years. Using a second factor, such as user behavior, as a part of the authentication process offers higher assurance. A great deal of research has been proposed to authenticate based on the behavior of various entities. However, they often play effects after user logging on to the system. Even if the attacks are detected successfully, the malicious activities have been performed and the damage is done. In this paper, we present Evalt, an implicit approach that takes effect before user logging on to enhance authentication with an additional security layer. Evalt exploits the features extracted from authentication events to detect anomalies. Hence it could block the attackers before they cause damage to systems. We test Evalt on an open-source Windows security log dataset. The experiment shows that our method could identify threats with a good performance before the actual damage occurs based on the authentication events' features.
An Improved Semantic Segmentation Model for Remote Sensing Images based on HRNet

Abstract Deep learning has been greatly improved recently, and natural image processing based on deep learning has also been greatly improved. However, there are still great differences between natural images and remote sensing images, among which the biggest is that the size of the target in remote sensing images is greatly different, which requires the model to have a strong multi-scale processing ability. In order to meet this goal, we use HRNet with full multi-scale fusion capability to replace ResNet to process remote sensing images. HRNet fully integrates low-level detail features, middle-level structure features and high-level semantic features, which is very suitable for remote sensing images. The experimental results show that our method has been greatly improved.
The Method of Communication System Fault Diagnosis Based on Deep Belief Net

To meet the need of fault diagnosis for military communication system, an effective method based on deep belief (DBN) net is proposed. During the fault diagnosis, the bottom layer of DBN model is used to receive the input fault signals to extract the fault features and the fault classification results will be outputted after softmax classified. Accordingly, algorithms for DBN model and training and RBM parameter learning have been designed. To reduce the running time, parallel solutions based on MapReduce framework have been provided. In order to test and verify the effect of DBN fault diagnosis, the communication experiment system is built in the laboratory which the output signals of the transmitter and the receiver are measured and collected as the original data for further learning and training. Compared with the traditional fault diagnosis methods, it can be found that DBN method has high accuracy in fault diagnosis and the process is simple and friendly. It is impossible to realize real-time diagnosis and online diagnosis for the communication system. The research can be applicated to the health management of communication equipment, and it will provide advanced technical support and software program for the health of communication equipment
The Users’ Body and Their Digital Representation in Interactive Installation- a Mereological Perspective

This article examines the relationship between the users’ body and its digital representations in digital body interactive installations, a sub-category of digital interactive art. Due to the reflective capacity of the interfaces incorporated in these artworks, the researchers designate the connection between the body and its digital embodied image as identification, or self- identification, terms which are not contextualized and detailed, leading to theoretical misinterpretations. To approach the problem, we use an ontological branch of study- mereology, and we discuss the membership of the body within the interactive system and the connection between the users’ body and the digital embodied self: component-integral relationship, functional, mandatory, and configurational, separation, encapsulation, and exchange relationships. Afterwards, we use the composition as identity thesis to show that instead of identification or self-identification, we characterize the connection between the users’ body and the digital body as “partial, temporary and stationary exchange” based on active engagement, self- consciousness, and body awareness. The relevance and novelty of the article tackle two directions: first, the interdisciplinary combination of logical philosophy and digital art, and secondly, the lack of ontological explanations regarding the structural relations in digital interactive art.
Recommending the Title of a Research Paper Based on Its Abstract Using Deep Learning-Based Text Summarization Approaches

Due to the increasing use of the Internet and other online resources, there is tremendous growth in the data of text documents. It is not possible to manage this huge data manually. This has led to the growth of fields like text mining and text summarization. This paper presents a title prediction model for research papers using Recursive Recurrent Neural Network (Recursive RNN) and evaluates its performance by comparing it with sequence-to-sequence models. Research papers published between 2018 and 2020 were obtained from a standard repository, viz. Kaggle, to train the title prediction model. The performance of different versions of Recursive RNN and Seq2Seq was evaluated in terms of training and hold-out loss. The experimental results show that Recursive RNN models perform significantly better than the other models.
Glimpse-Attend-and-Explore: Self-Attention for Active Visual Exploration

Active visual exploration aims to assist an agent with a limited field of view to understand its environment based on partial observations made by choosing the best viewing directions in the scene. Recent methods have tried to ad-dress this problem either by using reinforcement learning, which is difficult to train, or by uncertainty maps, which are task-specific and can only be implemented for dense prediction tasks. In this paper, we propose the Glimpse-Attend-and-Explore model which: (a) employs self-attention to guide the visual exploration instead of task-specific uncertainty maps; (b) can be used for both dense and sparse prediction tasks; and (c) uses a contrastive stream to further improve the representations learned. Unlike previous works, we show the application of our model on multiple tasks like reconstruction, segmentation and classification. Our model provides encouraging results while being less dependent on dataset bias in driving the exploration. We further perform an ablation study to investigate the features and attention learned by our model. Finally, we show that our self-attention module learns to attend different regions of the scene by minimizing the loss on the downstream task. Code: https://github.com/soroushseifi/glimpse-attend-explore.
Bayesian Belief Network Model Using Sematic Concept for Expert Finding

The Expert finding is a research hotspot in the area of entity retrieval. However, due to the small number of search terms, the retrieval effect will be poor due to the mechanical text matching. In view of the above shortcomings, we use Bayesian belief network as a model frame, and two expert finding models are proposed. One is a basic semantic belief network retrieval model, in which BERT and LDA models are used, and the other is a compound semantic belief network model. The compound model uses an effective data fusion technique to integrate the retrieval results of the two sub-models in this paper. The paper presents the topology and retrieval algorithm of two models proposed. The experiments verify the validity of the research content on Amine platform. Experimental results show that the semantic model can improve the MAP value, and the compound semantic model is better than the existing expert finding model on multiple evaluation indicators such as P@N, MAP and MRR, and it can improve the performance of expert retrieval.
The Development of Running Event Ontology for Sport Tourism in Thailand

Nowadays, there are a considerable number of running events in Thailand which causes the growth of tourists to be increased in the place where the sport tourists participate. As sport tourists are a special interest tourism that may need an immense amount of specific information to match their needs, the unstructured information on the internet causes tourists difficulty in finding such information. Therefore, this paper presents a running event ontology that provides information regarding running events satisfying tourists' specific interests. The use of ontology technology results in the data and information to be structured and the relationship among these relevant concepts to be linked. The result of the ontology development shows that the developed ontology can be used to retrieve the running events information that matches with the factor of sport tourists. This is demonstrated with the high accuracy of on-tology evaluation as we expect.
Application of Data Mining with Classification Methods for Promotion of New Student Admissions at Muhammadiyah University of Sidoarjo Using Web-Based Naïve Bayes Algorithm

The University of Muhammadiyah Sidoarjo (UMSIDA) is one of Indonesia's superior and innovative private colleges in developing IPTEKS based on Islamic values for community welfare. UMSIDA that has stood long enough with the number of students received in each year is quite a lot. Each new school year opening, this private college regularly organizes new student admissions (PMB) activities. Admission for new students (PMB) at UMSIDA can be done at pmb.umsida.ac.id. Therefore, research aims to create data mining applications classification method with the algorithm Naïve Bayes. This research uses the classification method used to Megukur accuracy level. To predict the promotion of new students receiving Muhammadiyah Sidoarjo University (UMSIDA) can be done using the Naïve Bayes algorithm with 7 predefined variables. Offline and online predictor of the dataset of 2601 data is divided into 2 as many as 70% of 2000 Training data and as much as 30% from 601 of Testing data.
Implementation of a Machine Learning based OFDM detector using GNU Radio

Software-Defined Radios (SDRs) are powerful and versatile tools that can implement various communication systems in the software domain. The user can redefine the system by modifying the code. GNU Radio is an open-source SDR that uses a block-based graphical interface with a Python backend framework. Machine Learning (ML) technologies are powerful tools that can bolster the development of the next generation of wireless communication systems. Orthogonal frequency-division multiplexing (OFDM) is a modulation scheme used in modern communication systems such as 4G/5G, WiMax, etc. This study uses the powerful ML ecosystem in Python to model conventional OFDM receivers using the GNU Radio ecosystem. The work trains and compares Deep Neural Networks as well as Linear Regression algorithms (using PHY-layer data) to model the receiver. The trained models improve upon the Bit Error Rate (BER) of conventional systems by a factor of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> .
Localization Uncertainty-Based Attention For Object Detection

Object detection has been applied in a wide variety of real world scenarios, so detection algorithms must provide confidence in the results to ensure that appropriate decisions can be made based on their results. Accordingly, several studies have investigated the probabilistic confidence of bounding box regression. However, such approaches have been restricted to anchor-based detectors, which use box confidence values as additional screening scores during non-maximum suppression (NMS) procedures. In this paper, we propose a more efficient uncertainty-aware dense detector (UADET) that predicts four-directional localization uncertainties via Gaussian modeling. Furthermore, a simple uncertainty attention module (UAM) that exploits box confidence maps is proposed to improve performance through feature refinement. Experiments using the MS COCO benchmark show that our UADET consistently surpasses baseline FCOS, and that our best model, ResNext-64x4d-101-DCN, obtains a single model, single-scale AP of 48.3% on COCO test-dev, thus achieving the state-of-the-art among various object detectors.
Implementation of Nussinov’s RNA Folding Using the Kokkos Library

Dynamic programming of bioinformatics codes requires efficient programming on multi- and many-core platforms. In this paper, we propose an implementation of Nussinov’s RNA folding using the innovative Kokkos framework. Kokkos Core allows us to quickly code parallel programs, including bioinformatics tasks, for major HPC platforms. The Kokkos abstracts enable multi-threaded patterns like Posix Threads, OpenMP, or CUDA without significant code modifications. Our implementation consists of two steps. First, we parallelize Nussinov’s RNA folding code using well-known compilers like Traco and PLUTO. Second, we re-implement manually output to the Kokkos C++ modern and multi-platform code using templates and lambdas. We explain and practice the framework features for memory and threads management in terms of RNA folding programming. We carry out experiments to study the performance of Nussinov’s codes within Kokkos. We analyze speed-up as a ratio of serial run-time to the time taken by the parallel codes for various RNA sequences lengths. Related and future work are outlined.
Filtering of Stochastic Systems

Filter problems are formulated for stochastic systems which are not Gaussian systems. Both the estimation problem, the sequential estimation problem, and the filter problem are treated. A sufficient condition for the existence of a finite-dimensional filter system is formulated. The concept of a family of invariant conditional distributions is defined. It is described how to solve the filter problem by a measure transformation method. Cases treated include the Poisson–Gamma filter and the filter of an output-finite–state-finite stochastic system.
JPEG File Fragmentation Point Detection using Huffman Code and Quantization Array Validation

File carving is a data recovery technique used in many investigations in digital forensics, with some limitations. Especially JPEG files are difficult to recover when fragmented, because they consist almost entirely of large blobs of highly compressed entropy-coded data, with no clearly discernible structure.
Optical computation of the spin glass dynamics

Spin Glasses(SG) are paradigmatic models with many applications, while their dynamics is difficult to treat because is NP-hard. Here we implement the optical simulation of a SG demonstrating a speedup with respect to in-silico simulations.
Research on Particle Swarm Practical Algorithm of Partial Differential Exact Solution Based on Computer Software Analysis

Abstract Particle swarm collaboration has many applications on the Internet. It is necessary to cluster a large number of algebraic matrices to obtain enough data. The current research focuses on the correlation between particle swarm coordination and matrix, and does not fully consider the difference between geometries. There is a large overlap, which leads to the same results for different matrices, leading to double counting problems. Therefore, based on the analysis of computer technology, this paper proposes a particle swarm optimization algorithm based on partial differential exact solutions. The selected geometric matrix has higher correlation and lower overlap. Experimental results show that this method can significantly improve the accuracy and efficiency of particle swarm coordination.
Images super-resolution using improved generative adversarial networks

Image super-resolution (ISR) is an important image processing technology to improve image resolution in computer vision tasks. The purpose of this paper is to study the super-resolution reconstruction of single image based on the depth learning method. Aiming at the problem that the existing pixel loss-based super-resolution image reconstruction algorithms have poor reconstruction effect on high-frequency details, such as textures, a lighter algorithm is proposed on the basis of the existing deep learning method (SRGAN). Firstly, the feedback structure is applied in the generator to process the feedback information and enhance the high frequency information of the image. Secondly, a general residual feature aggregation framework (RFA), is applied to make full use of the residual information of each layer to improve the quality of the SR image. Finally, the solution space of the function is further reduced and the image quality is improved by using a new loss function. The algorithms are implemented on pytorch framework. The experimental results on VOC2012 data sets show that, compared with the original SRGAN algorithm, the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) of the proposed algorithm on the benchmark data set Set5 are improved by 0.83dB and 0.028, respectively, on Set14, the PSNR and SSIM of the proposed algorithm are improved by 0.56dB and 0.009, on Urban100, the PSNR and SSIM of the proposed algorithm are improved by 0.51dB and 0.031, on BSD100, the PSNR and SSIM of the proposed algorithm are improved by 0.33dB and 0.014, and compared with other improved algorithms, the effect of this algorithm is also better than other algorithms.
A Study on the Teaching Model of Multicultural College English Based on the Computer

Abstract With the rapid development of the times, the social economy of our country has a high-speed and stable development state, people’s living standard is increasing constantly, the current people are in the age of information high-speed development, the appearance of the computer Internet brings great convenience to people’s daily life [1, 2]. The Internet not only changes people’s daily life style, but also has a certain influence on the education industry. College English teaching has been a lot of attention, but from the current specific development situation, how can the teaching efficiency of college English be more efficient, it is the problem that the relevant department of college English teaching should focus on now [3, 4]. This paper starts with the challenge of college English under the Internet age, and makes an in-depth analysis and research on the teaching reform of the current college English.
METHODOLOGICAL ASPECTS OF TEACHING MATHEMATICS BASED ON THE RESULTS OF MONITORING RESEARCH

The article reviews the actual problems of mathematics education. The difficulties encountered by a modern schoolchild when solving tasks of monitoring research in mathematics are indicated. Methodological recommendations are given that should increase the productivity of interaction between a modern teacher and a modern schoolchild, and, accordingly, the quality result of training, through the improvement of pedagogical techniques, an increase in the level of professionalism of teachers, their motivation, initiatives, the creative and emotional component of the educational process, professional competence of the teacher.
Pheromone Based Independent Reinforcement Learning for Multiagent Navigation

Multiagent systems (MAS) have been generally applied in numerous applications, including computer networks, robotics, and smart grids due to their flexibility, reliability for complex problem-solving. Communication is an important factor for the multiagent world to stay organized and productive. Previously, most existing studies try to pre-define the communication protocols or adopt additional decision modules for instructing the communication schedule, which induces significant communication cost overhead and cannot generalized to a large collection of agents directly. In this paper, we propose a lightweight communication framework—Pheromone Collaborative Deep Q-Network (PCDQN), which combines deep Q-network with the pheromone-driven stigmergy mechanism. In partially observable environments, this framework exploits the stigmergy as circuitous communication connections among independent reinforcement learning agents. Experiments dependent on the minefield navigation task have shown that PCDQN displays superiority in accomplishing higher learning productivity of multiple agents when contrasted with Deep Q-network (DQN).
Unsupervised Deep Hashing via Adaptive Clustering

Similarity-preserved hashing has become a popular technique for large-scale image retrieval because of its low storage cost and high search efficiency. Unsupervised hashing has high practical value because it learns hash functions without any annotated label. Previous unsupervised hashing methods usually obtain the semantic similarities between data points by taking use of deep features extracted from pre-trained CNN networks. The semantic structure learned from fixed embeddings are often not the optimal, leading to sub-optimal retrieval performance. To tackle the problem, in this paper, we propose a Deep Clustering based Unsupervised Hashing architecture, called DCUH. The proposed model can simultaneously learn the intrinsic semantic relationships and hash codes. Specifically, DCUH first clusters the deep features to generate the pseudo classification labels. Then, DCUH is trained by both the classification loss and the discriminative loss. Concretely, the pseudo class label is used as the supervision for classification. The learned hash code should be invariant under different data augmentations with the local semantic structure preserved. Finally, DCUH is designed to update the cluster assignments and train the deep hashing network iteratively. Extensive experiments demonstrate that the proposed model outperforms the state-of-the-art unsupervised hashing methods.
Exploration of Online and Offline Media Convergence Based on Artificial Intelligence Technology

This paper analyzes the content shortage problems faced by museums, art galleries, private cinemas, various types of experience museums and other cultural complexes, and finds that it is mainly due to the high production cost caused by the existing production pattern of creating and producing cultural products entirely on its own. With the help of artificial intelligence technology, this paper puts forward a solution to realize the convergence of online and offline media, that is, using software technology such as video understanding and video enhancement and collecting a wide variety of audio and video programs on the Internet. Then we integrate, optimize and re-create them to overcome the defects of fragmentation and poor quality, so that the quality of cultural products can reach the level of high resolution, high color depth and high frame rate, and the cost is not so high. To meet the sustainable needs of new era cultural complex for high-quality cultural products in low price.
Method of Indoor Navigation and Safety Improvement by SE-Dense-DQN

Abstract Since the increasing requirement of indoor navigation with the assurance of safety, Deep Learning was been used in this area by numerous scholars. In order to help unmanned aerial vehicle navigate in various environment without prior experience, this paper proposed the method of Deep Q Learning with the Squeeze-and-Excitation Networks and Dense blocks. The structure of the Q-Network also amended with the information of LIDAR processed by Long Short-Term Memory to make sure the accuracy. The structure of the network shows in figure 6. The experiment conducted in simulated environment. Experimental results indicated that this method excelled in efficiency and accuracy than other classic Deep Q Networks like classic Deep Q Network and Double Deep Q Network. The results displaying in figure 8 and figure 9 shows unmanned aerial vehicle can arrive at target place more accurately than other networks.