Reviewerly/exHarmony huggingface dataset:
Application of ODN Technology in Park Communication Transmission System

This paper designs an intelligent data transmission management system based on ODN (optical distribution network) technology. This transmission system has the functional characteristics of standard, fast and pragmatic, which significantly improves data transmission's stability and convenience in the park data transmission system. Firstly, this paper will briefly introduce the ODN technology and intelligent management of communication transmission system; including the overall design scheme of the data transmission system; then, the author will present a comprehensive study on the ODN field module and the specific implementation; finally, the author will discuss the performance of the system through the experiment and make a conclusion. The experimental results show that the system can meet large-capacity data transmission management requirements under the multi-service mode, with low cost. This intelligent and standardized functional design significantly improves data transmission's stability and efficiency in the park communication transmission system.
Multi-Missile Information Fusion Based on Virtual Measurement Kalman Filter

Aiming at the problem that the multi-missile cooperative detection system may lose the true Doppler information of the target in a complex battlefield environment, resulting in the divergence or bias of the target state estimation of the target, a multi-projection based on the virtual measurement Kalman filter is proposed. The missile information fusion algorithm tracks each missile by constructing a virtual measurement based on a uniform turning motion, and then uses the covariance-based track fusion method to use the information of each missile to achieve high-precision tracking of the target. Simulation research shows that the proposed multi-projectile information fusion method based on virtual measurement Kalman filter has the advantages of fast convergence speed and high tracking accuracy, and has strong fault tolerance in the presence of interference, which provides practical applications for engineering Theoretical basis.
Optimasi Algoritma Rate Adaptation Control CARA untuk Meningkatkan Throughput pada Wireless Mesh Network

This paper examined throughput optimization issue in wireless mesh network (WMN), the weakest point of this network in regard to this matter. A number of previous studies on this issue have been conducted, but most focus has been on general wireless network, only few studies so far have attempted on this network. This research aimed to optimize the CARA rate adaptation control algorithm in WMN network. The optimization was performed by adjusting the successtreshold and timeout parameters in the CARA algorithm to obtain an optimal throughput. The optimal result is showed that the optimal points of the success threshold and timeout are at range of 30-35. It is obviously seen in grid 4×5 and 5x5 where the throughput value of the optimization result continues to increase. Moreover, by adding the data transmission time for 100 seconds on grid 5×5 resulting the throughput value of 0.52206412 Mbps and after the optimization the throughput value increases up to 117% to 1.1350768 Mbps, when the success threshold and timeout value are 30. For an additional 150 seconds, the throughput value is 0.5074419333 Mbps and after the optimization the throughput increases up to 120% to 1.1211402 Mbps, when the success threshold and timeout value are 35.
LibGuides: STRT 1000: Evaluating Web Sources

A research guide for STRT, designed to be delivered during a synchronous Zoom instruction session with a librarian
Behind the screen: content moderation in the shadows of social media

Sarah T. Roberts’ Behind the Screen: Content Moderation in the Shadows of Social Media sheds light on the fact that what we see on the internet, especially social media platforms, has gone through ...
고전문학의 연구 · 교육 · 활용과 창의

There is a close relation between research, education, and use of Korean premodern literature. And these three realms pursuit creativity.Recently the use of Korean premodern literature becomes a pending question in the academic world due to the rise of cultural industries and the crisis of the humanities. Accordingly researchers try to turn the results of research to cultural contents.But, for the long run, the education of Korean premodern literature plays key in succession and creative use of Korean premodern literature. Because the basis of reception and creative use of Korean premodern literature is the masses who learned Korean premodern literature in secondary school and college. Therefore the pivotal point is that how make learners experience the value and usefulness deeply and impressively in the class of Korean premodern literature.
Transcending the Teetering Tower of Trust

When it comes to security, there is a dangerous disconnect between the mental model we use for the modern computation stack and the real thing. The model we all use is not rich enough to understand the constant battle between attackers and security researchers across the layers of the stack. This paper offers an enhanced model and explains how security fits into the picture. We use our model to explain the implications of various types of defensive mechanisms. Then, we practice the recommended method for future security research by designing an operating system feature – software-enforced trusted execution enclaves using Virtual Memory Fuses (VMFs) – with the best-practices we argue for. The Teetering Tower of Trust model offers a new way to think about security across the computation stack, while the novel Virtual Memory Fuse creates the possibility of a new operating system feature: software enclaves.
Learning Non-Parametric Invariances from Data with Permanent Random Connectomes.

One of the fundamental problems in supervised classification and in machine learning in general, is the modelling of non-parametric invariances that exist in data. Most prior art has focused on enforcing priors in the form of invariances to parametric nuisance transformations that are expected to be present in data. Learning non-parametric invariances directly from data remains an important open problem. In this paper, we introduce a new architectural layer for convolutional networks which is capable of learning general invariances from data itself. This layer can learn invariance to non-parametric transformations and interestingly, motivates and incorporates permanent random connectomes, thereby being called Permanent Random Connectome Non-Parametric Transformation Networks (PRC-NPTN). PRC-NPTN networks are initialized with random connections (not just weights) which are a small subset of the connections in a fully connected convolution layer. Importantly, these connections in PRC-NPTNs once initialized remain permanent throughout training and testing. Permanent random connectomes make these architectures loosely more biologically plausible than many other mainstream network architectures which require highly ordered structures. We motivate randomly initialized connections as a simple method to learn invariance from data itself while invoking invariance towards multiple nuisance transformations simultaneously. We find that these randomly initialized permanent connections have positive effects on generalization, outperform much larger ConvNet baselines and the recently proposed Non-Parametric Transformation Network (NPTN) on benchmarks that enforce learning invariances from the data itself.
Representations of Quantized Light

Light can be epitomized in classical realm by ordinary mathematical function that can be expressed in terms of frequency, wavelength and amplitude of the wave train. Such description can explain much of the optical phenomena related to reflection, refraction, diffraction, dispersion and interference.
Image deblurring algorithm based on deep convolution neural network

Purpose to effectively remove multiple kinds of image blurring and improve image quality, an image deblurring method based on deep reinforcement learning and an image inpainting algorithm based on convolutional neural network. The high-dimensional characteristics of blurred images are obtained through convolutional neural network, and a deblurring framework is constructed through deep reinforcement learning integrated with various kinds of CNN deblurring tools, so as to select the optimal inpainting strategy and restore the blurred images step by step. The effectiveness of the above image inpainting enhancement method designed and its superiority compared with other traditional methods of the same kind are verified through subjective and objective contrast experiments
Using Detection, Tracking and Prediction in Visual SLAM to Achieve Real-time Semantic Mapping of Dynamic Scenarios.

In this paper, we propose a lightweight system, RDS-SLAM, based on ORB-SLAM2, which can accurately estimate poses and build semantic maps at object level for dynamic scenarios in real time using only one commonly used Intel Core i7 CPU. In RDS-SLAM, three major improvements, as well as major architectural modifications, are proposed to overcome the limitations of ORB-SLAM2. Firstly, it adopts a lightweight object detection neural network in key frames. Secondly, an efficient tracking and prediction mechanism is embedded into the system to remove the feature points belonging to movable objects in all incoming frames. Thirdly, a semantic octree map is built by probabilistic fusion of detection and tracking results, which enables a robot to maintain a semantic description at object level for potential interactions in dynamic scenarios. We evaluate RDS-SLAM in TUM RGB-D dataset, and experimental results show that RDS-SLAM can run with 30.3 ms per frame in dynamic scenarios using only an Intel Core i7 CPU, and achieves comparable accuracy compared with the state-of-the-art SLAM systems which heavily rely on both Intel Core i7 CPUs and powerful GPUs.
CAT-SAC: Soft Actor-Critic with Curiosity-Aware Entropy Temperature

The trade-off between exploration and exploitation has long been a crucial issue in reinforcement learning~(RL). Most of the existing RL methods handle this problem by adding action noise to the policies, such as the Soft Actor-Critic (SAC) that introduces an entropy temperature for maximizing both the external value and the entropy of the policy. However, this temperature is applied indiscriminately to all different environment states, undermining the potential of exploration. In this paper, we argue that the agent should explore more in an unfamiliar state, while less in a familiar state, so as to understand the environment more efficiently. To this purpose, we propose \textbf{C}uriosity-\textbf{A}ware entropy \textbf{T}emperature for SAC (CAT-SAC), which utilizes the curiosity mechanism in developing an instance-level entropy temperature. CAT-SAC uses the state prediction error to model curiosity because an unfamiliar state generally has a large prediction error. The curiosity is added to the target entropy to increase the entropy temperature for unfamiliar states and decrease the target entropy for familiar states. By tuning the entropy specifically and adaptively, CAT-SAC is encouraged to explore when its curiosity is large, otherwise, it is encouraged to exploit. Experimental results on the difficult MuJoCo benchmark testify that the proposed CAT-SAC significantly improves the sample efficiency, outperforming the advanced model-based / model-free RL baselines.
LibGuides: Virtual & Augmented Reality: Programs and Apps

Learn about Virtual and Augmented Reality, and services available at the Nexus Collaborative Learning Lab!
Dampak Alih Fungsi Lahan dan Curah Hujan terhadap Banjir di Kota Pekalongan, Jawa Tengah

Laksmi GS. 2020. Impact of land use change and rainfall on flooding in Pekalongan city, Central Java. In : Herlinda S et al . ( Eds. ), Prosiding Seminar Nasional Lahan Suboptimal ke-8 Tahun 2020, Palembang 20 Oktober 2020. pp. xxx.  Palembang: Penerbit & Percetakan Universitas Sriwijaya (UNSRI). Pekalongan City is located on the northern coast of Central Java Province which connects Jakarta, Semarang, and Surabaya. Located on the main route to Java Island, Pekalongan City has strategic economic potential, especially in the industrial sector. This condition caused Pekalongan City to become the area with the highest demographic urbanization value in Central Java in 2006 and led to changes in land cover. Land-use change will have negative impacts if uncontrolled like the reduction in catchment areas, especially if the rainfall is high. This study is a literature study that contains the effect of land-use change and rainfall on flooding in Pekalongan City. Data and information come from various journals related to floods in Pekalongan City. This study aims to analyze the impact of land use change and rainfall on waterways in Pekalongan City. Changes in land cover and rainfall have a strong correlation. The reduced water catchment area causes the increase of flood index. Pekalongan City was categorized as a city that was not prone to flooding in 2011, but in 2017, all villages in North Pekalongan Regency were flooded with an area of 1,249,420 hectares.
Human Computer Interaction on Enterprise Information Systems

Safety and pliability, for Enterprise Information Systems (EIS), are fundamental aspects in business. These data systems incorporate the human users with the required behaviours, experiences, and capabilities. In that regard, they have to be pliable, usable and secure. Pliability necessitates the capability to adapt and prepare to handle the perpetuating transforming conditions, which are meant to restore the complete capacity of the incidents and the attack in EIS. In this research is purpose to discuss pliability, security and the Information Systems (IS) problems in the EIS. The problem necessitates the consideration of ergonomics of efficiency, effectiveness and interactions of obligation realization, user trust and user satisfaction, including human emotions when utilizing the secured services. This paper also proposes a technique centred on the socio-technical paradigm and systems meant to model the kinds of interplays between usability, security and pliability. We provide a discussion, based on case study, meant to display the projected approach and focussing on the user-experience centred on the design structures.
LibGuides: STRT 1000: Facts

A research guide for STRT, designed to be delivered during a synchronous Zoom instruction session with a librarian
Deep Learning Models for Heterogeneous Big Data Analytics

In recent times, Big data is modifying the style life of workplaces and thinking by improved performance in knowledge discovering and decision making ever-greater volumes of data are being produced data due to the network of sensors and communication technologies Heterogeneous data is a category of unstructured data with an unknown pace in several ways. Current data analysis techniques are inadequate to handle the huge volumes of data produced, this data difficult to manage, store, handle, interpret, analyze using traditional techniques. Deep learning (DL) is extremely popular among many data scientists and experts thanks to the high precision in speech recognition, image handling, and data analytics. DL has become much more important because it can be used for largescale heterogeneous data. DL has been applied efficiently in several fields and has exceeded most of the traditional techniques, DL algorithmic can study large unclassified data with the ability to select features. This study concentrates on the discussion of a variety of new algorithms that handle this data and DL models that provide greater accuracy for heterogeneous data.
Deep Learning Based Traffic Signs Boundary Estimation.

In the context of autonomous navigation, the localization of the vehicle relies on the accurate detection and tracking of artificial landmarks. These landmarks are based on handcrafted features. However, because of their low- level nature, they are not informative but also not robust under various conditions (lightning, weather, point-of-view). Moreover, in Advanced Driver-Assistance Systems (ADAS), and road safety, intense efforts have been made to implement automatic visual data processing, with special emphasis on road object recognition. The main idea of this work is to detect accurate higher-level landmarks such as static semantic objects using Deep learning frameworks. We mainly focus on the accurate detection, segmentation and classification of vertical traffic signs according to their function (danger, give way, prohibition/obligation, and indication). This paper presents the boundary estimation of European traffic signs from an embedded monocular camera in a vehicle. We propose a framework using two different deep neural networks in order to: (1) detect and recognize traffic signs in the video flow and (2) regress the coordinates of each vertices of the detected traffic sign to estimate its shape boundary. We also provide a comparison of our method with Mask R-CNN [1] which is the state-of-the-art segmentation method.
The impact of distinct loss mechanisms on multi-photon states

Abstract Quantum optics plays an important role in many contemporary applications of quantum information processing and quantum metrology. Experimental implementations of these protocols, however, inevitably exhibit loss. This loss has two distinct forms, stochastic loss and deterministic loss, which impact quantum mechanical systems in distinct ways. In this exposition, we offer a straightforward experimental illustration of the impact of both forms of loss on multi-photon states. Specifically, we show that heralding statistics for two-photon states decrease in the obvious way under stochastic loss, and yet are unchanged in the presence of deterministic loss. This demonstration offers an intuitive clarification of loss for multi-photon systems for the student familiar with elementary quantum mechanics, and emphasizes the difference between pure and mixed states in quantum theory more generally. This presentation is targeted towards advanced undergraduate students familiar with quantum optics.
Electronic Marketing for Financial Services: a Case Study on Islamic Banks in the United Arab Emirates

E-marketing widely facilitated the banking sector as it enabled financial institutions to enhance their servicesand access global arenas. To further validate this, the current study intends to identify the extent to whichIslamic banks in the UAE employ e-marketing and its role in enhancing the relationship with customers andits influence on the quality of banking services. The researchers also seek to provide pertinent information tohelp bank departments to adapt and integrate e-marketing in Islamic services. For data gathering purposes, n=12 questionnaires were distributed among the services sector employees in Dubai Islamic Bank and SharjahIslamic Bank. Results revealed that there is a significant relationship between the adaptation of e-marketingand the improved quality of banking services. Additionally, the results also indicated statistically significantdifferences in the responses regarding the banking services due to the varying demographic factors of thepotential clients. Thus, the researchers recommend that the awareness and adoption of e-marketing throughemployee training can be of greater pre-eminence. Especially in Islamic banking, relying on contemporarymarketing technology can play a sturdy role to improve their services and sustain better relations with thecustomers.
Mobile Cloud Computing

Mobile cloud computing (MCC) is the administrations of distributed computing. The facilitation is available to a cell phone or any hand-versatile framework. Portable processing coordinates with distributed computing on account of the fundamental attributes of the cloud model. Some examples include on-request selfadministration, asset pooling, quick flexibility, and estimated administrations. Portable Sensor-cloud collects the readings in an unsupervised environment and provides the queried data to the respective clients. This chapter provides a vision into the taxonomy of the mobile cloud system, its security issues, management issues, advantages, applications, challenges, and future directions.
Analisis Dan Implementasi Metode Patchwork Untuk Pengamanan Video

In the increasingly rapid development of information, data information sent or received by someone is very important to keep its confidentiality and existence. For this reason, it is very important to increase data security so that it is not known by other people who have no interest so that our data is not misused for their own interests. One of the reasons for applying watermaking is to ensure that an object that is received or owned is the original data object, and its existence cannot be known by others. Benefit from watermaking to protect pictures, text, video, sound. Another purpose of watermaking is to protect copyright ownership of data or information. In this study the authors will analyze the implementation process of the patchwork method for video security. The Patchwork method is used in watermaking by performing statistical calculations on the insertion process. Keywords : Analysis, Implementasi, Watermaking, Video, Patchwork.
¿Esto es am<3r?, un diario más allá de lo textual

A textual and visual diary is produced from the mechanical language of emojis, swipes, likes and comments found in online dating apps such as Tinder, Bumble and Inner Circle. This diary questions not only one's own thoughts about love, but also the place of the body, the love encounter, and 'actu-virtuality'.
Implementation of Sequential and Parallel Alpha-beta Pruning Algorithm

In the past, game applications were proved to be inefficient compared to present day. This is mostly because of limitation of computer computation and memory space and inadequate game tree algorithms to help determine the next move. Games that require extensive searching needed a faster and more efficacious technique. Alpha-Beta Pruning is one of the most fundamental optimization technique for Minimax algorithm. Just by passing 2 extra parameters in the Minimax function, namely alpha and beta, this algorithm helps increase the speed of the game drastically. The pruning helps in eliminating the number of searches whenever there already exists a better move available in the game tree. In this paper, the effects of Alpha-Beta Pruning on the game tree are discussed by taking into account bimatrix games such as Tic-Tac-Toe and Checkers etc.