Reviewerly/exHarmony huggingface dataset:
Enhanced Ultrasound Speckle Reduction technique Using SLIC Segmentation and Method Noise Compensation

In this paper, an enhanced ultrasound speckle reduction technique using Simple linear iterative clustering (SLIC) segmentation and method noise compensation technique has been proposed. The noisy image is first sparsely represented in its low frequency components with the SLIC superpixel clustering and reconstruction. Then the high frequency method noise is calculated by the difference of the noisy image and the low frequency reconstructed image. The details in the method image are then utilized to enhance the low frequency reconstructed image. The performance of the proposed technique is tested on synthetic ultrasound images with various noise components of different variance. The experiments show promising results of the proposed algorithm in comparison to other existing methods in terms of PSNR, SSIM, EKI and CC.
Hybrid Cloud Computing for Data Security System

Computer without network connectivity is an exception today. Earlier everyone was using the computer to solve its individual problem. Now a days these problems related application is not available on the standalone desktop, these applications are working as a client server application. In this system, server part is available at some remote machine while the client part is available at user’s machine. Cloud computing is implemented using the four deployment models private cloud, public cloud, cloud hybrid and community cloud. Some of the user organization feels that the data can be misused by the company who owned the infrastructure. As a result, either they do not use the cloud services hosted on public cloud or they use only some limited services, so negate the use of cloud computing to a great extent. The solution to this is provided by the evolution of hybrid cloud. The corporate that feels security as one of the major issues in migration to cloud computing makes the use of hybrid cloud for their usage.
Applying Deep Residual Network to Suppress Optical Coding Access Interference

Optical network has a lot of advantage such as high transmit rate, large bandwidth, immunity to electromagnetic interference, low transmission loss and so on. We adopt Spectral-Amplitude Coding (SAC) of Optical code-division multiple-access (OCDMA) as our system framework. In the paper, we aim at intelligent computations to mitigate Gaussian distribution noises and interference from noisy accumulated multi-carrier transmissions. We proposed the deep learning interference cancellation (DLC) which is composed of convolutional neural network (CNN) and deep residual network (ResNet) concept. The DLC conceptually consists of three phase: Noisy signal extraction, Signal reconstruction and Data decoding. We will compare the hard decision and soft decision method and evaluate the performance over optical coding access receiver. In the simulation, the result shows that RIC with embed DLC will have better performance.
Road Target Classification Method Based on GA-BP Neural Network for Event Camera

Vehicle classification is an important technology of intelligent transportation system, and most methods are based on video streams currently. However, video detectors have some limitations in extreme illumination or high-speed motion. In our study, the event camera is installed in the actual road scenario for vehicle and pedestrian detection. The geometrical, quantitative and Gaussian projection characteristics of the point cloud targets are extracted. The BP neural network is used for vehicle and pedestrian classification, and we optimize the weight and threshold values of the network by GA (genetic algorithm). The accuracy of classification reaches 95%.
Multi-channel Relation Modeling for Session-Based Recommendation with Self-supervised Learning

Recommender system plays an important role in industrial application, because it can handle the problem of information overload and provide users with content they are interested in. And session recommendation can help anonymous users without auxiliary information or historical interactions. However, previous methods only model a single data relationship. Ignoring the possible existence of chronological relationship, graph structured relationship and mutual relationship among session recommendation items may leads insufficient information mining and then reduces the effect of recommendation task. To handle this problem, we propose Multi-channel Relation Modeling Net, using three classical modules to fully mine information in the data. After that, we design multi-relation factorized pooling module to integrate multi-relation embedding. To get more informative embedding, we then use self-supervised learning to maximizing mutual information between relationships. Our method fully leverages the possible complex structure in the data, which has a certain practical significance in the specific application.
A study on film score applying the Neo-Riemannian theory: focusing on Bernard Herrmann’s mysterious island

This paper, takes three pieces of music in the film score work Mysterious Island by film music composer Bernard Herrmann (1911-1975) as the analysis object. Bernard Herrmann's composition techniques are very innovative, and even difficult to interpret harmonic design when analyzing his works with tonal music. The Neo-Riemannian theory formed in the 1980s is used to analyze the chord progression that cannot be accurately explained by tonal music analysis in Bernard Herrmann's film score, and to prove the Neo-Riemannian theory by marking the transformation methods used and deriving the evolution process of harmony Neo-Riemannian theory's rationality and superiority in analyzing film music. This study hopes to use the Neo-Riemannian theory to provide a new perspective for exploring the development of film musicology.
Claim Stance Classification Optimized by Data Augment

As online fora increasingly become the main media for argument and debate, the automatic processing of such data is rapidly becoming more and more important. Stance classification, which aims to classify the stance of the claims towards the given topic, can be applied in many application areas such as users' feelings about services and products. We propose a ensemble model for stance classification with data augment for small sample scenarios, multi-sample dropout for low training speed scenarios, focal loss for imbalance sample scenarios, pseudo labels for self-supervised training scenarios, adversarial training for low robustness scenarios, and all the above can be used in normal scenarios. Besides, the ensemble model is composed of task-specific RoBERTa and MacBERT, which can make more reasonable predictions. We used dataset from NLPCC to validate the model and it worked well.
Arabic Handwritten Text to Line Segmentation

Text to line segmentation is a crucial phase in character recognition system since segmentation errors affects the recognition accuracy. In this work we present a novel and simple method for Arabic handwritten text images segmentation into text-lines. After converting the gray scale images to binary ones, we combine in this proposed method three approaches based on horizontal projection profile (HPP), on connected components (CC) and on skeleton. Firstly, we apply the smoothed horizontal projection profile to detect approximately the beginning and the end of each line. Then, we identify the connected components in each line basing on computing their centroids in order to cluster them to form an individual text-line. Finally, in case there are vertically touching characters, we use the skeleton to separate them by calculating its intersection point. The experiments are performed with 100 text images from the database Khatt. This approach is evaluated by the MatchScore criterion. The obtained results prove the efficiency of our method.
Smart Office Automation System for Covid Prevention

Today, this coronavirus is spread all around the world. Most organizations and businesses start to think about how to continue their business in a situation like COVID-19 and their employees’ health and business security. To avoid and be safe from this type of disease, there are some common rules to follow. Keeping a distance, wearing a mask, cleaning our hands, are some health guidelines from them. According to the current situation, many inventors are trying and have already given some solutions to avoid these kinds of situations aligning with health guidance’ provided by WHO. With the advantage of advanced modern-day technologies and ideas, researchers started to think about how to face situations like these with the new technologies and found that many users are highly interested and motivated with automated systems. Thus, from this study, we aim to provide a fully automated office management system to prevent corona with advanced technology in combination with IoT technologies, Machine learning, Cloud technologies, and sensor technologies. Considering the security aspect, Controlling the main entrance, identifying, ensuring user’s authentication before entering the building, and monitoring employee activities are very significant aspects of the study. As the result of the study, the combination of IoT technologies and Machine Learning with deep learning mechanisms have guaranteed organizational business continuity, employees' health, and security.
List Recommendation via Co-attentive User Preference Fine-Tuning

Most existing recommendation models focus on individual items. However, in many real-world scenarios, items are organized into lists (also called bundles) composed of multiple items with certain common correlations, e.g., video lists containing videos in the same genres, which makes list recommendation a significant task. In this paper, we propose a model named CAPLE which learns coarse-grained and fine-grained user preferences from user-list and user-item interactions respectively to achieve list recommendation. In particular, CAPLE develops a co-attention module, which fine-tunes user preferences by learning collaborative relations between user preferences of different granularities via jointly modeling tangled interactions of two different granularities. We conduct extensive experiments on real-world datasets. The performance enhancement on recall@K and NDCG@K verifies the effectiveness of the model.
Budget-constrained coalition strategies with discounting

Abstract Discounting future costs and rewards is a common practice in accounting, game theory and machine learning. In spite of this, existing logics for reasoning about strategies with cost and resource constraints do not account for discounting. The article proposes a sound and complete logical system for reasoning about budget-constrained strategic abilities that incorporates discounting into its semantics.
Sparse Crowd Flow Analysis of Tawaaf of Kaaba During the COVID-19 Pandemic

The advent of the COVID-19 pandemic has adversely affected the entire world and has put forth high demand for techniques that remotely manage crowd-related tasks. Video surveillance and crowd management using video analysis techniques have significantly impacted today's research, and numerous applications have been developed in this domain. This research proposed an anomaly detection technique applied to Umrah videos in Kaaba during the COVID-19 pandemic through sparse crowd analysis. Managing the Kaaba rituals is crucial since the crowd gathers from around the world and requires proper analysis during these days of the pandemic. The Umrah videos are analyzed, and a system is devised that can track and monitor the crowd flow in Kaaba. The crowd in these videos is sparse due to the pandemic, and we have developed a technique to track the maximum crowd flow and detect any object (person) moving in the direction unlikely of the major flow. We have detected abnormal movement by creating the histograms for the vertical and horizontal flows and applying thresholds to identify the non-majority flow. Our algorithm aims to analyze the crowd through video surveillance and timely detect any abnormal activity to maintain a smooth crowd flow in Kaaba during the pandemic.
Gaussian Adaptive Filtering of Low Resolution Video Using Anisotropic Tensor

In this paper an optimization procedure is proposed for finding the optimal parameters of the Anisotropic Gaussian filter for low resolution video, containing Additive White Gaussian Noise, using anisotropic tensor. The tensor is found from the video content by estimating the brightness gradient along horizontal and vertical direction, according to the algorithm of Kimmel et al. High quality of the filtered frames is achieved, corresponding to Peak signal to Noise Ratio of 30.07 dB and Structural Similarity Index Measure of 0.8641, when the input video contains noise with variance, equal to 0.001. Comparative analysis with the filter of Middendorf et al., employing also the structure tensor, along with the non-adaptive Average and Gaussian filters, reveal better structural preservation of the video, processed by the optimized version of the proposed filter.
Earthquake Prediction Based on Spatial-Temporal Data Mining

Considering that earthquakes are spatially and temporally correlated in the same seismic zone, earthquake prediction for a particular region should not be conducted only based on the history data in that region, but according to the history data in a larger area. This paper proposes a seismic prediction research based on spatial-temporal data analysis. The historical seismic data of three seismic zones in Sichuan-Yunnan were used in the experiment, and each zone was divided into several sub-regions. Eight seismic indicators representing the internal seismic state of the region were calculated from the historical seismic events. The spatial correlation and seismic indicators of each sub-regions were taken as the input of the model to analyze whether earthquakes with \(M_{s} \ge 4.5\) would occur in each sub-region next month. The results show that method proposed in this paper is able to exploit the spatial-temporal correlations among earthquakes to make better predictions than before.
Automatically Annotating Sentences for Task-specific Bug Report Summarization

There is a need to summarize bug reports as they can become long due to many comments from conversations between developers and various DevOps tools. Although automated approaches to bug report summarization have been developed, we believe they aim at the wrong target - getting as close as possible to a gold-standard summary. Instead, researchers should create automated bug report annotation approaches that allow project members to create summaries based on their task-specific information needs. We present such an approach.
Graph-Based Local Feature Adaptation for Cross-Domain Person Re-Identification

The performance of cross-domain person re-identification has been greatly improved in recent years. However, there are still two problems in existing cross-domain person re-identification methods. First, most of them conduct domain adaptation on features that contain background noise. Second, they ignore the correlation between different features, including intra-domain and inter-domain. To overcome these problems, we propose a novel Graph-based Local Feature Adaptation (GLFA) framework for cross-domain person re-identification, which promotes domain adaptation by correlating intra-domain and inter-domain semantic local features with graph convolutional network. Specifically, in the feature extraction stage, we utilize a parsing model to extract semantic local features for both source and target domain samples, so as to avoid the feature shift caused by background discrepancy. In the feature adaptation stage, we apply two stacked Graph Convolutional Networks (GCN) to propagate semantic local feature information within each domain and across different domains respectively. It promotes the transfer of specific knowledge from the source domain to the target domain. Furthermore, to ensure the features from different domains updated by GCN are well aligned, a local feature distribution alignment loss is introduced on the top of GCN. The combination of GCN and alignment loss enables our framework diminish the feature shift caused by other factors effectively. Extensive experiments on Market-1501, DukeMTMC-reID and MSMT17 datasets demonstrate that our method outperforms the state-of-the-art methods evidently.
Coarse-to-Fine Attribute Editing for Fashion Images

With the development of Generative Adversarial Networks, attribute editing has been more and more popular in computer vision. The previous works employed the multi-domain image-to-image translation framework to solve attribute editing. However, it is difficult to generate consistent texture with the original images. Meanwhile, the un-target attribute regions are changed by using these methods. To address these problems, this paper presents a coarse-to-fine attribute editing scheme (CFAE) for fashion images. CFAE is composed of coarse stage and refine stage. In the coarse stage, we design a landmark-based attention scheme in conjunction with StarGAN [] to locate and edit the target attribute regions. Subsequently, DeepFillv2 [] is employed in the refine stage to make the target attribute regions consistent with the original image. In experiment section, we compare our method with several state-of-the-art methods on OUTFIT Dataset and the results demonstrate the effectiveness of CFAE.
Sistem Informasi Perpustakaan Pada SMKN 1 Ende Berbasis Website

SMKN 1 Ende has a library as one of the facilities provided by the school to support student learning activities. The existence of a library is very important in increasing knowledge for students in schools. However, based on the results of an interview conducted with one of the librarians of SMKN 1 Ende, currently school library data management is still using manual methods such as recording visitor member data, book data, borrowing data and returning books in the ledger. As a result, it is inefficient and has a very big risk of mistakenly recording data on borrowing and returning books and other problems that often occur, namely when searching for data on borrowing and returning books where librarians have to reopen existing files so that it takes time. long enough. To overcome the existing problems, it is necessary to make a school library information system that will be tailored to the needs of the library. Based on the description of the problems in the library of SMKN 1 Ende, the theme that will be discussed by the researcher is "Library Information System at SMKN 1 Ende is Website Based". With this system, it can help the data management process by library officers in carrying out their duties such as collecting data on library visitor members, data collection of books, data collection of borrowing transactions and returning books so that officers complete their duties quickly.&#x0D;
Investigating the Consistency of Uncertainty Sampling in Deep Active Learning

Uncertainty sampling is a widely used active learning strategy to select unlabeled examples for annotation. However, previous work hints at weaknesses of uncertainty sampling when combined with deep learning, where the amount of data is even more significant. To investigate these problems, we analyze the properties of the latent statistical estimators of uncertainty sampling in simple scenarios. We prove that uncertainty sampling converges towards some decision boundary. Additionally, we show that it can be inconsistent, leading to incorrect estimates of the optimal latent boundary. The inconsistency depends on the latent class distribution, more specifically on the class overlap. Further, we empirically analyze the variance of the decision boundary and find that the performance of uncertainty sampling is also connected to the class regions overlap. We argue that our findings could be the first step towards explaining the poor performance of uncertainty sampling combined with deep models.
The Art of i-War

Strategy requires creativity and imagination. AI can be creative in a limited sense, by exploring its domain and finding new, useful options. Human creativity is richer, with the potential to combine options in new ways, or even to transform the domain entirely.
Dialogue management based on forcing a user through a discourse tree of a text

In this chapter, we build a chatbot whose task is to encourage a user to learn a short text by conversing about it. We refer to this chatbot as persistent because it enforces the user to stay focused on a given topic. To organize such dialogue based on a short text, the chatbot navigates the discourse structure of this text to conduct a meaningful, cohesive conversation. We develop an algorithm to navigate the discourse tree (DT) of text and form an utterance based on user intent, type of node, and a rhetorical relation linking this node. The evaluation covers the effectiveness and user satisfaction of this navigation approach. We conclude that the traversal of the DT for the purpose of learning a short text in the educational context is more efficient than a traditional data-driven dialogue management approach.
Network Video Frame-Skip Modeling and Simulation

A high video frame rate is ideal for many computer vision applications; however, this is not is usually the case for video feeds streamed from network cameras, which also exhibit frame skipping. Frame skipping can be considered as a type of degradation of the live-streamed video, and is detrimental to object tracking and other computer vision tasks. In this paper, we empirically model frame skipping and present a method for simulating frame skipping using videos with high frame rates. This simulation method was then validated by comparing the frame rate histograms of real and simulated frame-skipped videos. Finally, we demonstrate how frame skipping degrades the performance of an object tracker.
Remittances and the future of African economies

Abstract African nations have, with the passage of time, over‐relied on remittance inflow to augment domestic savings for growth and development. Although remittance—both as a viable investment financing option and as a growth complementary factor—helps drive growth and development, concerns of fading altruism exist. As regards altruistic giving, decreasing external financing options might overwhelm growth and development. We argue that the altruistic connection, which has been the bedrock of sending remittances to African countries, could eventually fade into oblivion. We assigned numerical weights to establish the influence of remittances on the future of African economies. We took cognizance of endogeneity of regressors and accounted for cross‐sectional dependence. We found a positive relationship between remittances and financial development besides the influence of exchange rate, technological change, inflation and population on the latter. African governments, donors, investment partners and the society at large should be concerned about fading altruistic connections from old and young African migrants. Altruism could be a springboard to the diversification of external capital sourcing and creation of a migrant policy that puts Africa at the forefront of development ahead of seeking increased worker remittances from abroad.