Reviewerly/exHarmony huggingface dataset:
An automated detection system for colonoscopy images using a dual encoder-decoder model

Conventional computer-aided detection systems (CADs) for colonoscopic images utilize shape, texture, or temporal information to detect polyps, so they have limited sensitivity and specificity. This study proposes a method to extract possible polyp features automatically using convolutional neural networks (CNNs). The objective of this work aims at building up a light-weight dual encoder-decoder model structure for polyp detection in colonoscopy Images. This proposed model, though with a relatively shallow structure, is expected to have the capability of a similar performance to the methods with much deeper structures. The proposed CAD model consists of two sequential encoder-decoder networks that consist of several CNN layers and full connection layers. The front end of the model is a hetero-associator (also known as hetero-encoder) that uses backpropagation learning to generate a set of reliably corrupted labeled images with a certain degree of similarity to a ground truth image, which eliminates the need for a large amount of training data that is usually required for medical images tasks. This dual CNN architecture generates a set of noisy images that are similar to the labeled data to train its counterpart, the auto-associator (also known as auto-encoder), in order to increase the successor's discriminative power in classification. The auto-encoder is also equipped with CNNs to simultaneously capture the features of the labeled images that contain noise. The proposed method uses features that are learned from open medical datasets and the dataset of Zhejiang University (ZJU), which contains around one thousand images. The performance of the proposed architecture is compared with a state-of-the-art detection model in terms of the metrics of the Jaccard index, the DICE similarity score, and two other geometric measures. The improvements in the performance of the proposed model are attributed to the effective reduction in false positives in the auto-encoder and the generation of noisy candidate images by the hetero-encoder.
Crypto currency applications in financial markets: factors affecting crypto currency prices

Purpose-As the cryptocurrency market is beginning to attract investors, a new portfolio of cryptocurrencies has been published in the literature on macro-economic factors affecting these currencies.This research also aimed to identify the interaction between gold, brent oil, Bitcoin, Ethereum and Ripple.Methodology-The database includes the Daily prices of Bitcoin, Ethereum, Ripple, gold and brent oil prices between the period of 03.04.2018-31.12.2020 which consist of 500 daily data.Natural logaritm for each indicator is used.First, the stationarity of the series were analyzed with ADF (Augmented Dickey Fuller) unit root test.Lag lengths are determined.Interactions between the series were analyzed by the Johansen Cointegration test, Granger Causality test, Impulse-Response Function and Variance Decomposition method.Findings-The series are found out to be stationary at first difference.According to the cointegration test result, cointegration could not be found between our data.According to Granger causality analysis, only one-way relationship was found from bitcoin to gold.Impulse response graphs indicate that all variables respond in a reducing way to reducing shocks occurred in each indicator.Shocks have lost their effect on average in 2 days. Conclusion-The results indicate that the effect of gold and brent oil prices on bitcoin, ethereum, ripple daily prices do not have a strong effect.The results may be beneficial for investors to consider diversification for the portfolios.
Human Capital Development and Higher Education

The paper focuses on the role of higher education as an instrument for improving on human capital development in the country.Emphasis is laid on how Higher Education Institutions (HEIs) in the country can be repositioned for the development of human capital.This paper presents an empirical study that seeks to identify the role of higher education in development of human capital.Content analysis was done from various academic articles world over in relation to the current topic under study.Factors such as government policies, level of technological advancement and enrollment level were discussed.The challenges confronting human capital development in HEIs were highlighted and the strategies for overcoming such challenges deliberated.In the present global environment, characterized by rapid change, intense information flows and increasing competition, emergence of HEIs holds an important place.In today's knowledge based economy, HEIs are playing as centres for human resource development and therefore, aid in the development of the human capital.The paper concludes that higher education should contribute to meeting the social economic needs of the population in terms of relevance of the qualifications offered to the current labour needs.It therefore, recommends that all stakeholders should contribute towards the development and review of the curricula offered in HEIs to conform to the current needs of the Labour Market.
RadarViewer : Visualizing the dynamics of multivariate data

This showcase presents a visual approach based on clustering and superimposing to construct a high-level overview of sequential event data while balancing the amount of information and the cardinality in it. We also implement an interactive prototype, called RadarViewer , that allows domain analysts to simultaneously analyze sequence clustering, extract useful distribution patterns, drill multiple levels-of-detail to accelerate the analysis. The RadarViewer is demonstrated through case studies with real-world temporal datasets of different sizes.
Regression for Compositional Data with Compositional Data as Predictor Variables with or without Zero Values

Compositional data are positive multivariate data, constrained to lie within the simplex space.Regression analysis of such data has been studied and many regression models have been proposed, but most of them not allowing for zero values.Secondly, the case of compositional data being in the predictor variables side has gained little research interest.Surprisingly enough, the case of both the response and predictor variables being compositional data has not been widely studied.This paper suggests a solution for this last problem.Principal components regression using the -transformation and Kulback-Leibler divergence are the key elements of the proposed approach.An advantage of this approach is that zero values are allowed, in both the response and the predictor variables side.Simulation studies and examples with real data illustrate the performance of our algorithm.
Routing and Key Resource Allocation in SDN-based Quantum Satellite Networks

Free-space long-distance quantum key distribution (QKD) has the characteristics of low attenuation and wide coverage, which can overcome the limitation of transmission distance based on ground optical fiber QKD networks. Long-distance QKD requests can be relayed by quantum satellites to achieve intercontinental QKD. At present, there is only one quantum satellite in space, quantum satellite network with multiple quantum satellites is necessary to be deployed to cover the global. Thus, how to construct the quantum satellite network becomes a new challenge. In this paper, we propose a centralized and distributed collaborative scheme of quantum satellite network. Low-Earth-orbit (LEO) quantum satellites, as important relay satellites, will affect the performance of the global QKD network. Based on the above architecture, we analyze the influence of the structure of LEO quantum satellite constellation on the QKD performance. Simulation results show that the key relay performance of the constellation is related to the form of inter-satellite link. and the more the number of satellites is, the greater the successful probability of the key relay services will be.
A Comparative Study of Audio Steganography Schemes

Steganography conceals the existence of secret information, while cryptography hides the meaning of the confidential information.Therefore, combining these two can provide a secure communication between a sender and a receiver.This article proposes a double-layer message security scheme where cryptography is the first layer and steganography is the second layer.AES-256, Blowfish, or a Logistic map is used in the cryptography layer.The LSB substitution technique is used for the steganography layer to embed a secret message inside cover audio.The LSB substitution technique is applied by alternating between the left and the right channels, respectively.Performance of the proposed schemes is evaluated in terms of a hearing test, running time, embedding capacity, waveform plots, as well as a large number of statistical, time-domain, and frequency-domain metrics.Furthermore, a comparison between the proposed scheme using AES-256 and its counterparts from the literature is also provided.The numerical results show that the proposed schemes exhibit very good performance.
Pricing QoE With Reinforcement Learning For Intelligent Wireless Multimedia Communications

With the unification of modern wireless services of Mixed Reality and Tactile Internet, providing Quality of Experience (QoE) with ultra low latency become critical challenges in edge resource allocation. In this paper we propose a reinforcement learning-based economic pricing model for wireless multimedia QoE, leveraging economic theories and machine intelligence. In the proposed approach, the QoE pricing model considers the User Equipment (UE)'s perceived QoE, the amount of purchased data, the wireless channel conditions, and the user's subjective multimedia content preference. In addition, the QoE gain of UE, cost of three entities in wireless networks Content Provider (CP), Wireless Carrier (WC), and UE, are integrated in the economic concept of social utility. The social utility would be affected by all system factors such as unit data price, multimedia quality requirement, and wireless channel conditions. The proposed reinforcement learning method improves the social utility performance by maximizing the accumulated utility through obtaining the optimal factors set up. At last, through numerical simulations we show the impacts of different system parameters on UE's QoE gain and the improvement of social utility performance by using the proposed reinforcement learning approach.
Traid-bit embedding process on Arabic text steganography method

The enormous development in the utilization of the Internet has driven by a continuous improvement in the region of security. The enhancement of the security embedded techniques is applied to save the intellectual property. There are numerous types of security mechanisms. Steganography is the art and science of concealing secret information inside a cover media such as image, audio, video and text, without drawing any suspicion to the eavesdropper. The text is ideal for steganography due to its ubiquity. There are many steganography embedded techniques used Arabic language to embed the hidden message in the cover text. Kashida, Shifting Point and Sharp-edges are the three Arabic steganography embedded techniques with high capacity. However, these three techniques have lack of performance to embed the hidden message into the cover text. This paper present about traid-bit method by integrating these three Arabic text steganography embedded techniques. It is an effective way to evaluate many embedded techniques at the same time, and introduced one solution for various cases.
Path Planner for Autonomous Exploration of Underground Mines by Aerial Vehicles

This paper presents a path planner solution that makes it possible to autonomously explore underground mines with aerial robots (typically multicopters). In these environments the operations may be limited by many factors like the lack of external navigation signals, the narrow passages and the absence of radio communications. The designed path planner is defined as a simple and highly computationally efficient algorithm that, only relying on a laser imaging detection and ranging (LIDAR) sensor with Simultaneous localization and mapping (SLAM) capability, permits the exploration of a set of single-level mining tunnels. It performs dynamic planning based on exploration vectors, a novel variant of the open sector method with reinforced filtering. The algorithm incorporates global awareness and obstacle avoidance modules. The first one prevents the possibility of getting trapped in a loop, whereas the second one facilitates the navigation along narrow tunnels. The performance of the proposed solution has been tested in different study cases with a Hardware-in-the-loop (HIL) simulator developed for this purpose. In all situations the path planner logic performed as expected and the used routing was optimal. Furthermore, the path efficiency, measured in terms of traveled distance and used time, was high when compared with an ideal reference case. The result is a very fast, real-time, and static memory capable algorithm, which implemented on the proposed architecture presents a feasible solution for the autonomous exploration of underground mines.
Kamu Adaletinin Büyük Veri Aracılığı ile İnşası: Şule Çet Davası Üzerine Bir Twitter Analizi

This study focuses on the opportunities provided by social networks based on Big Data, specifically Twitter, for social movements and rights struggles.Discussing
A Mask-Pooling Model With Local-Level Triplet Loss for Person Re-Identification

Person Re-Identification (ReID) is an important yet challenging task in computer vision. Background clutter is one of the greatest challenges to overcome. In this paper, we propose a Mask-pooling model with local-level triplet loss (MPM-LTL) to tackle this problem and improve person ReID performance. Specifically, we present a novel pooling method, called mask pooling (MP), to gradually remove background features in feature maps through deep convolutional network. With mask pooling, the network can learn the most crucial person features. Moreover, we raise a novel local-level triplet loss for discriminative feature training. Furthermore, we propose a new hard triplets selection algorithm named Mask-guided TriHard. The method is based on human outline information, which is, to our best knowledge, employed for the first time for hard triplets selection. We achieve the state-of-the-art results on three benchmark person datasets Market-1501 [1], CUHK03 [2] and DukeMTMC-reID [3], [4].
Grid Model-Based Global Color Correction for Multiple Image Mosaicking

Color consistency optimization for multiple images is a challenging problem in image mosaicking. To facilitate the global color optimization, existing approaches mainly use less flexible models, e.g., linear or gamma function, to eliminate the color differences between multiple images. However, these models often struggle to eliminate the color differences that existed in the local areas and preserve the image gradient information. To solve this problem, we creatively propose a novel color-correction model, which comprised a series of local grid linear models. This model is simple, but it is flexible enough to approximate a variety of complicated local color variations. To obtain the optimal model parameters for each image globally, a specific cost function that considers both color consistency and gradient preservation is designed and solved. The aim of our approach is to generate a composite image with visually consistent color. The original color information may be destroyed. Thus, this approach is unsuitable for the quantitative remote sensing applications. The experimental results on several challenging data sets show that the proposed approach outperforms state-of-the-art approaches in both visual quality and quantitative metrics.
Preprocessing Method for Encrypted Traffic Based on Semisupervised Clustering

The explosive growth in network traffic in recent times has resulted in increased processing pressure on network intrusion detection systems. In addition, there is a lack of reliable methods for preprocessing network traffic generated by benign applications that do not steal users’ data from their devices. To alleviate these problems, this study analyzed the differences between benign and malicious traffic produced by benign applications and malware, respectively. To fully express these differences, this study proposed a new set of statistical features for training a clustering model. Furthermore, to mine the communication channels generated by benign applications in batches, a semisupervised clustering method was adopted. Using a small number of labeled samples, our method aggregated historical network traffic into two types of clusters. The cluster that did not contain labeled malicious samples was regarded as a benign traffic cluster. The experimental results were compared using four types of clustering algorithms. The density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm was selected to mine benign communication channels. We also compared our method with two other methods, and the results demonstrated that the benign channels mined through our method were more reliable. Finally, using our method, 1,811 benign transport layer security (TLS) channels were mined from 18,357 TLS communication channels. The number of flows carried by these benign channels comprised 65.37% of the entire network flows, and no malicious flow was included in our results, which proves the effectiveness of our method.
Robot control and kinematic analysis with 6DoF manipulator using direct kinematic method

The robots pay important role in all parts of our life. Hence, the modeling of the robot is essential to develop the performance specification. Robot model of six degree of freedom (6DoF) manipulator implemented numerically using model-based technique. The kinematic analysis and simulation were studied with Inverse kinematics of the robot manipulator through Denevit and Hartenberg method. Matrix transformation method is used in this work in order to separate joint variables from kinematic equations. The finding of the desired configuration is obtained precisely in all motion trajectory along the end-effector path. MATLAB/SIMULINK with R2018b is used for the implementation of the model-based robot system. Simulation results showed that the robot rinks follow their references smoothly and precisely and ensure the effectiveness of direct kinematic algorithm in the analysis and control of the robotic field.
Impacts of Sustainable Information Technology Capabilities on Information Security Assimilation: The Moderating Effects of Policy—Technology Balance

Information security management (ISM) has emerged as a major challenge to sustainable management of companies that highly rely on information technology (IT). To achieve organizational sustainability for managing assets, it is critical for all members of the organization to be assimilated into ISM. An important consideration of ISM assimilation is sustainable IT capabilities. However, so far, there are limited empirical studies on ISM assimilation, particularly those focusing on importance of the organization’s sustainable IT capabilities. Therefore, this study proposes three sustainable IT capabilities (viz., IT infrastructure, IT business spanning capability, and IT proactive stance) with their antecedents based on the existing research, and attempts to empirically prove the impact of these sustainable IT capabilities on ISM assimilation for sustainable management of assets. Additionally, this study proposes policy-to-technology balance as a moderator on the relationships between the three sustainable IT capabilities and ISM assimilation to examine the impact of the non-technical aspect. Responses from 232 upper-management-level employees at various firms currently implementing ISM were collected. Structural equation analysis was run using AMOS 22.0. The results show that the three sustainable IT capabilities were found to have a positive effect on ISM. Furthermore, policy-to-technology balance was found to strengthen the relationship between two IT capabilities (IT infrastructure and IT business spanning capability) and ISM assimilation. However, it emerges that the policy-to-technology balance does not impact IT proactive stance and assimilation. The findings provide meaningful information for future research on sustainable IT capabilities and ISM along with key guidance for the organization to establish a complementary strategy for sustainable assets.
The minimum description length principle for pattern mining: a survey

Abstract Mining patterns is a core task in data analysis and, beyond issues of efficient enumeration, the selection of patterns constitutes a major challenge. The Minimum Description Length (MDL) principle, a model selection method grounded in information theory, has been applied to pattern mining with the aim to obtain compact high-quality sets of patterns. After giving an outline of relevant concepts from information theory and coding, we review MDL-based methods for mining different kinds of patterns from various types of data. Finally, we open a discussion on some issues regarding these methods.
Research on Recognition of Motion Behaviors of Copepods

The motion behaviors of copepods has important scientific research value and there is very little research on recognition of their motion behaviors simultaneously. Recognition of the basic motion behaviors of copepods using deep learning methods can greatly reduce the time cost of distinguishing and statistics, as well as achieve the purpose of improving efficiency. Based on the characteristics of motion of copepods that bring challenges to the extraction of motion fragments from raw video and the establishment of data set, such as instantaneous moving, static status most time, small-scale and high-frequency, this article propose an improved Camshift algorithm for detection of moving targets to overcome these challenges and establish the motion behaviors image acquisition system and a standard data set of motion behaviors, which provides the experience and methods of marine zooplankton behaviors database. Finally, the LRCN network that combines the advantages of CNN and LSTM is adopted to study the impacts of different factors on the model performance, such as the number of frames of sample, preprocessing operations and sample dimensions. Experimental results show that the LRCN network has excellent potential in classification of motion behaviors of copepods, when the number of frames of sample reaches 7, the precison rate, recall rate, f1-score are 0.96, 0.95, 0.95, respectively. In addition, the rise in number of frames and preprocessing has a positive effect on the recognition, the 4D samples (image sequence) is more suitable for the LRCN model than 3D samples (trajectory image).
MEDIA PEMBELAJARAN MULTIMEDIA INTERAKTIF UNTUK ANAK TUNAGRAHITA RINGAN

The purpose of this study is to determine the feasibility and influence of the application of interactive multimedia learning media as teaching material for mild retarded students.This paper is the result of a review of previous articles related to mild retarded children..This paper was written to find out the effect of learning assisted by interactive multimedia media on motor skills, in the form of video and Adobe Flash with the learning motivation of students with mild mental retardation
Distill, Adapt, Distill: Training Small, In-Domain Models for Neural Machine Translation

We explore best practices for training small, memory efficient machine translation models with sequence-level knowledge distillation in the domain adaptation setting.While both domain adaptation and knowledge distillation are widely-used, their interaction remains little understood.Our large-scale empirical results in machine translation (on three language pairs with three domains each) suggest distilling twice for best performance: once using general-domain data and again using indomain data with an adapted teacher.The code for these experiments can be found here. 1