Reviewerly/exHarmony huggingface dataset:
Study on the relationship between the oxidation degree of GO and the adsorption capacity for Pb2+

In this study, four kinds of graphite oxide (GO) with different oxidation degree have been prepared by a modified Hummers method, by changing the amount of oxidation agent KMnO4. To investigate the relationship between the oxidation degree of GO and the adsorption capacity for heavy metal irons, Pb2+ solution has been selected as a model waste water. The effects of initial Pb2+ concentration, solution pH and contact time on the adsorption capacity of series GO have been studied. It is found that the adsorption capacity does not always increase with the increasing of oxidation degree. The adsorption process of series GO for Pb2+ are well described by the Langmuir isotherm model, which reveales that the adsorption process is homogeneous monolayer coverage.
Session hijacking vulnerabilities and prevention algorithms

The concept of Internet security is studied by computer science as a safe medium for exchanging data while minimizing the likelihood of online threats. The extensive use of advanced web-based software in different industries such as education, retail, medical care, and payment systems, represents a security challenge for the programmers and an opportunity for the hackers to attack through session hijacking. Based on recent OWASP guidelines, this kind of attack is indeed one of the most frequent attacks that happens lately. Session hijacking happens as a result of poorly designed websites and a lack of security mechanisms, where the user's identity and session data are exposed. This paper will present this kind of vulnerability with the respective control mechanisms and will propose an approach for avoiding hijacking threats by using one-time cookies along with other prevention strategies.&#x0D; &#x0D; Keywords: session hijacking, vulnerability, one-time cookies.
Development of Digital Teaching Materials Based Microsoft Sway to Increase Literature Science

This project intends to design and evaluate digital teaching materials based on Microsoft Sway to improve the scientific literacy of primary school pupils.This study is a development study employing methodologies with a design that is consistent with Borg and Gall.This study focuses on teachers and students Public Alementary Cchool Karangmlati 1, Public Alementary Cchool Bintoro 9, and Public Alementary Cchool Bolo.Data collecting strategies include (1) interviews ( 2) questionnaires (3) testing, and (4) documentation.Data analysis strategies employing qualitative and quantitative analysis.The conclusion of this study is This instructional material has also been evaluated for practicability using a validity test, with an average score of over 85 and a very practicable category.The findings of the model's effectiveness test using the t-test indicated that digital teaching materials based on Microsoft Sway were effective in enhancing the scientific literacy of primary school students..as proven by the posttest results being greater than the pretest results.This was also supported by the results of teacher responses and 85% students expressed interest in the developed teaching materials.
Improving Video Instance Segmentation via Temporal Pyramid Routing

Video Instance Segmentation (VIS) is a new and inherently multi-task problem, which aims to detect, segment, and track each instance in a video sequence. Existing approaches are mainly based on single-frame features or single-scale features of multiple frames, where either temporal information or multi-scale information is ignored. To incorporate both temporal and scale information, we propose a Temporal Pyramid Routing (TPR) strategy to conditionally align and conduct pixel-level aggregation from a feature pyramid pair of two adjacent frames. Specifically, TPR contains two novel components, including Dynamic Aligned Cell Routing (DACR) and Cross Pyramid Routing (CPR), where DACR is designed for aligning and gating pyramid features across temporal dimension, while CPR transfers temporally aggregated features across scale dimension. Moreover, our approach is a light-weight and plug-and-play module and can be easily applied to existing instance segmentation methods. Extensive experiments on three datasets including YouTube-VIS (2019, 2021) and Cityscapes-VPS demonstrate the effectiveness and efficiency of the proposed approach on several state-of-the-art instance and panoptic segmentation methods. Codes will be publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/lxtGH/TemporalPyramidRouting</uri> .
Bypassing Cloudflare’s reverse proxy: a case study / Contornando o proxy reverso do Cloudflare: um estudo de caso

Reverse proxy is a functionality provided by companies such as Cloudflare and its designed to protect virtual assets on the internet by acting as a middleman between end users and an origin server. While working on a law enforcement case, we performed OSINT research, designed and deployed a tool that allowed us to reach an original website's IP address associated with criminal activity, thus achieving partial bypass of Cloudflare's reverse proxy protection.
Data Visualization Design Strategies for Promoting Exercise Motivation in Self-Tracking Applications

In self-tracking applications, data visualizations play a fundamental role in delivering efficient information and creating personalized user experiences. Literature consistently indicates that data visualization is a powerful tool to make data persuasive and improve motivation. However, how to leverage different data visualizations to boost motivation remains largely unknown. In this study, the researcher explores the effects of different data visualizations on user motivation within self-tracking mobile applications. Through design space analysis and semi-structured interviews, the researcher defines a set of design factors that impact users’ exercise motivation at different levels of exercise adoption. Based on these factors, the researcher delivers a set of practical design suggestions for design practitioners and people who create visualizations for large data sets.
Internet of Things under a semantic perspective with user profiles

Internet of Things (IoT) is a network made up of different types of devices integrated into the Internet. For the use of IoT in cities to assist humans beings in their daily activities, it is necessary to extract and process as much information as possible and therefore, semantic web technologies are a key point. This new environment is known as the Semantic Web of Things (SWoT). The application of semantic techniques to IoT can improve interoperability, effective access to data, discovery of integration resources, reasoning and processing of knowledge extraction from data, opening up opportunities for new applications. We show an overview of the most relevant semantic technologies, focusing on SWoT and well-accepted ontologies for IoT and we stress that considering users’ preferences, interests and needs are of vital importance, as a complement to the semantic information extracted. This new scenario enables the development of advanced applications and services to meet user requirements and needs, in particular for smart city applications.
Refactoring Assertion Roulette and Duplicate Assert test smells: a controlled experiment

Test smells can reduce the developers' ability to interact with the test code. Refactoring test code offers a safe strategy to handle test smells. However, the manual refactoring activity is not a trivial process, and it is often tedious and error-prone. This study aims to evaluate RAIDE, a tool for automatic identification and refactoring of test smells. We present an empirical assessment of RAIDE, in which we analyzed its capability at refactoring Assertion Roulette and Duplicate Assert test smells and compared the results against both manual refactoring and a state-of-the-art approach. The results show that RAIDE provides a faster and more intuitive approach for handling test smells than using an automated tool for smells detection combined with manual refactoring.
Computing groups of Hecke characters

We describe algorithms to represent and compute groups of Hecke characters. We make use of an idèlic point of view and obtain the whole family of such characters, including transcendental ones. We also show how to isolate the algebraic characters, which are of particular interest in number theory. This work has been implemented in Pari/GP, and we illustrate our work with a variety of explicit examples using our implementation.
Atmospheric light veil correction dehazing algorithm based on regional detection

To address the problems that the dehazing algorithm is prone to distortion, block artifacts, and edge pixel loss from the minimum filter in the highlighted area of an image, a dehazing algorithm with an atmospheric veil correction model under regional detection is proposed. First, the initial atmospheric light veil is obtained by using the improved dark channel, and the atmospheric light veil correction model is established for it. Then, the color distance and color distance histogram fitting curve of the haze image are used to accurately detect the region where the initial atmospheric light veil is corrected, and the correction term is established. Finally, the atmospheric light is estimated by combining the image information entropy with quad-tree subdivision algorithm. It is proved that the proposed method effectively solves the distorted problem in the highlighted region and the block artifacts produced by minimum filtering and can recover image with rich detailed information.
FACTORS OF ATTRACTION AND IMPEDIMENT FOR FOREIGN INVESTMENTS IN COUNTRIES OF COMMONWWEALTH OF INDEPENDENT STATES

FACTORS OF ATTRACTION AND IMPEDIMENT FOR FOREIGN INVESTMENTS IN COUNTRIES OF COMMONWWEALTH OF INDEPENDENT STATES Dina Malgazhdarova PhD student, Hungarian University of Agriculture and Life Sciences (MATE), Kaposvar campus, &nbsp;Hungary, Ka
Trajectory planning of sorting robot based on improved particle swarm optimization algorithm

Aiming at the low efficiency of trajectory planning and unstable operation of sorting robot, a particle swarm optimization (PSO) is proposed, which can dynamically adjust the learning factor. In this method, the trajectory of the sorting robot is fitted by piecewise polynomial interpolation, and the trajectory of the sorting robot is optimized by using the improved PSO as the fitness function of time. The piecewise polynomial interpolation function is effectively combined with PSO, and the complex process of the traditional PSO adaptation function is avoided. The problems that it is easy to fall into local extreme value in the early stage and slow convergence rate in the later stage are improved. Experiments show that this method can optimize the joint motion posture, speed and acceleration trajectory of the sorting robot, and effectively improve the operating efficiency and stability of the sorting robot.
SUMAC '22: 4th ACM International workshop on Structuring and Understanding of Multimedia heritAge Contents

SUMAC 2022 is the fourth edition of the workshop on Structuring and Understanding of Multimedia heritAge Contents. It is held in Lisboa, Portugal on October 10th, 2022 and is co-located with the 30th ACM International Conference on Multimedia. Its objective is to present and discuss the latest and most significant trends and challenges in the analysis, structuring and understanding of multimedia contents dedicated to the valorization of heritage, with the emphasis on the unlocking of and access to the big data of the past. A representative scope of Computer Science methodologies dedicated to the processing of multimedia heritage contents and their exploitation is covered by the works presented, with the ambition of advancing and raising awareness about this fully developing research field. The complete SUMAC'22 workshop proceedings are available at: https://dl.acm.org/doi/proceedings/10.1145/3552464
Study of Evaluation of Kenanga Road Drainage Channels, Karang Anyar Region, Tarakan City

Abstract Tarakan City is one of the developing areas in North Kalimantan Province. As an effort to increase development and economic development of Tarakan City. Jalan Kenanga Karang Anyar is a road segment in Tarakan City that is still experiencing inundation due to drainage channels that are unable to accommodate or drain excess surface water. The purpose of this study was to determine the capacity of the Kenanga Karang Anyar road drainage channel, Tarakan City. In this study, the calculation of maximum rainfall data is used, for the calculation of discharge and flow velocity using the Manning formula. Based on the calculation results, it is obtained that the design discharge is 7.436 m3/second with the dimensions of the design channel, the bottom width (B) = 1.68 m, the channel height (H) = 2.30 m with the water height (hair) = 1.45 m. For the existing channel, bottom width (B) = 0.63 m, channel height (H) = 0.45 m with water height (hair) = 0.45 m, when there is a flood, the existing channel is unable to accommodate the existing water discharge.
Design of College Physical Education Teaching Resources Database Based on Computer Information Technology

In recent years, with the accelerating development of the informatization of physical education in universities in my country, the physical education teaching resource database based on computer information technology, as an informatization infrastructure construction, plays an increasingly prominent role in the development of university physical education.In this context, this article aims to study the design of university physical education resources database based on computer information technology.This paper investigates the current construction status and design goals of the university physical education resource database through a questionnaire survey method, explains the functions of the university physical education database in detail, and puts forward suggestions for the design of the university physical education resource database.
Journeymen of the Printing Office

SUMMARY. This article discusses the ethical and fiscal problems involved in the process of selecting and acquiring library materials in the current publishing climate. The authors address the impact of a number of factors, including the academic award system, on the publishing market and resulting issues to be addressed in the purchase of monographs and serials. Librarians' contributions to the publishing machine are briefly discussed. Possible solutions are outlined. A significant review of the literature is included; discussions of the problem can be found in the literature on publishing, written almost exclusively by professional writers, editors, or publishers. Librarians are just now beginning to comment.
How to Connect Language Resources, Infrastructures, and Communities

This chapter will present lessons learned from CLARIN-D, the German CLARIN national consortium.Members of the CLARIN-D communities and of the CLARIN-D consortium have been engaged in innovative, data-driven, and communitybased research, using language resources and tools in the humanities and neighbouring disciplines.We will present different use cases and users' stories that demonstrate the innovative research potential of large digital corpora and lexical resources for the study of language change and variation, for language documentation, for literary studies, and for the social sciences.We will emphasize the added value of making language resources and tools available in the CLARIN distributed research infrastructure and will discuss legal and ethical issues that need to be addressed in the use of such an infrastructure.Innovative technical solutions for accessing digital materials still under copyright and for data mining such materials will be presented.We will outline the need for close interaction with communities of interest in the areas of curriculum development, data management, and training the next generation of
Cryptanalysis of various images based on neural networks with leakage and time varying delays

Abstract The main objective of this paper is to provide an efficient image encryption for each and every single person in order to secure their own records while saving them in social networks. We have formulated the delayed fuzzy cellular neural networks (FCNNs) with suitable keys that are the values of the parameters of FCNNs and obtain the irregular dynamical signal (solution) which encrypts the images. We have utilized entirely 42 parameters as a key sensitivity in the order of 10 −15 among them three elements of initial condition parameters are sensitive to the order of 10 −14 . Lastly, comparison results are provided with the existing literature. The measurements show that the proposed algorithm is a novel overall solution for image encryption.
The CLARIN Committee for Legal and Ethical Issues and the Normative Layer of the CLARIN Infrastructure

The normative layer of CLARIN is, alongside the organizational and technical layers, an essential part of the infrastructure.It consists of the regulatory framework (statutory law, case law, authoritative guidelines, etc.), the contractual framework (licenses, terms of service, etc.), and ethical norms.Navigating the normative layer requires expertise, experience, and qualified effort.In order to advise the Board of Directors, a standing committee dedicated to legal and ethical issues, the CLIC, was created.Since its establishment in 2012, the CLIC has made considerable efforts to provide not only the BoD but also the general public with information and guidance.It has published many articles (both in proceedings of CLARIN conferences and in its own White Paper Series) and developed several LegalTech tools.It also runs a Legal Information Platform, where accessible information on various issues affecting language resources can be found.
Face Mask Detection Using Machine Learning Techniques

The year 2020 will be remembered for a major pandemic caused by Covid-19. The studies have shown that the spread of corona virus can be slowed by using face mask and at crowded places where the transmission is high, the use of face mask is important. To determine whether the person is wearing the face mask or not plays a major role which is done using face mask detection. The main objective is to develop a system to detect whether a person is wearing face mask or not wearing the face mask and to test the performance of different CNN models like Vgg16, MobileNetV2 and Densenet121 which can be used for classification. The models were compared with respect to accuracy, AUC curve, confusion matrix and its accuracy of predicting the image if it is with face mask or without face mask. The accuracy of Vgg16, MobileNetV2 and Densenet121 was found to be 93.1%, 99.88% and 99.37% respectively. The area under the ROC curve for the densenet121 was found to be greater as compared to other models. The models were also tested with respect to the confusion matrix and predicting if a person is wearing the face mask or not wearing the face mask.
Online Multiplayer Tracking by Extracting Temporal Contexts with Transformer

Sports competition is one of the most popular programs for many audiences. Tracking the players in sports game videos from broadcasts is a nontrivial challenge for computer vision researchers. In sports videos, the direction of an athlete’s movement changes quickly and unpredictably. Mutual occlusion between athletes is also more frequent in team competitions. However, the rich temporal contexts among the adjacent frames have been excluded from consideration. To address this dilemma, we propose an online transformer-based learnable framework in an end-to-end fashion. We use a transformer architecture to extract the temporal contexts between the successive frames and add them to the network training, which is robust to occlusion and complex direction changes in multiplayer tracking. We demonstrate the effectiveness of our method on three sports video datasets by comparing them with recently advanced multiplayer trackers.
Prediction of flight delay reason based on time node data of transit flight

Flight delays are the bottleneck restricting the service quality of the civil aviation industry. Only by understanding the mechanism of flight delays can we accurately develop countermeasures. This paper is oriented to ground service phase of transit flights wherein flight delays frequently occur. The time node data of flight ground service phase was collected and thoroughly cleaned. A variety of classification models are trained on the dataset after multi-source fusion to predict different reasons of flight delay. The results show that the reasons of delay can be effectively mined by using the time node data of transit flights. The research ideas and methods put forward in this paper can be used as a reference when the official judges and counts flight delay reasons every year.
Analysis of Inventory Accounting Information System at PT. Namasindo Plas Medan

The application of an accounting information system on inventory will provide many benefits for the company and management in running their business. Inventory has an important role, namely as a physical economic resource that needs to be held and maintained to support the sales process. This study aims to determine the inventory accounting information system at PT Namasindo Plas Medan. The method used is descriptive qualitative. The results show that the administrative system has been changed from a manual system to a computerized system that produces output that is useful for each user of the information. This is evidenced by the fact that all the components needed to support a valid system are owned by PT Namasindo Plas Medan and there are elements of internal control.
Ultrasound image edge enhancement method based on Bayesian Non-Local Means and Convolution Neural Network

In order to effectively improve the speckle noise suppression ability and preserve edge structure information in medical ultrasound images, an edge enhancement method based on Bayesian Non-Local Means and Convolution Neural Network is proposed in this paper. Firstly, the method performs smoothing filtering based on Bayesian Non-Local Means. And then introduces Local Laplacian Filter to enhance edge of the denoised image to improve the edge preservation ability. Finally, the edge-enhanced images and the speckle noise images as dataset to train Convolutional Neural Network denoising model. The experimental results demonstrate that the proposed method is superior to the traditional denoising algorithms that is compared in both speckle noise suppression and edge information retention.
Haze removal using a hybrid convolutional sparse representation model

Haze removal is a challenging task in image recovery, because hazy images are always degraded by turbid media in atmosphere, showing limited visibility and low contrast. Analysis Sparse Representation (ASR) and Synthesis Sparse Representation (SSR) has been widely used to recover degraded images. But there are always unexpected noise and details loss in the recovered images, as they take relatively less account of the images’ inherent coherence between image patches. Thus, in this paper, we propose a new haze removal method based on hybrid convolutional sparse representation, with consideration of the adjacent relationship by convolution and superposition. To integrate optical model into a convolutional sparse framework, we separate transmission map by transforming it into logarithm domain. And then a structure-based constraint on transmission map is proposed to maintain piece-wise smoothness and reduce the influence brought by pseudo depth abrupt edges. Experiment results demonstrate that the proposed method can restore fine structure of hazy images and suppress boosted noise.