Reviewerly/exHarmony huggingface dataset:
A distortion model-based pre-screening method for document image tampering localization under recapturing attack

Document images are vulnerable to tampering by image editing tools. The forgery trace can be concealed by a simple but effective counter-forensic measure, i.e., recapturing the altered document image. It is of practical need to study the tampering localization method under recapturing attack. In this work, we first study spatial and spectral distortion models in the printing and scanning process. The distortion models are then employed in extracting spectral features in both tampered and untampered regions. The proposed forensic scheme can then be established by comparing the spectral features in both the questioned document image and the reference halftone patterns (obtained by exploiting the prior knowledge of the printing device). To evaluate the performance of our approach, we gather a high-quality image database of 528 captured or recaptured documents (about 185K patches) as well as 72 tampered-and-recaptured documents (about 27K patches). The experimental results show that the proposed method can accurately classify recaptured document images with AUC as high as 0.9999 even though the training and testing samples are collected by different devices. In the tampering localization experiment, the proposed method can be combined with some generic CNN models to yield a two-stage scheme with high efficiency and accuracy, i.e., F1-score as high as 0.9. Finally, we also show that the proposed method is a practical solution even without the prior knowledge of the printer model is unavailable. To benefit the academic society, the resource of our work is online available at http://shorturl.at/jxELP.
MANAGEMENT STRATEGY: School Financial Management Strategies During the Covid-19 Pandemic

The success of financial management during the Covid-19 pandemic is the role of school strategic management to prepare planning, organizing, controlling, and directly in school financial management. The research aims to examine educational institutions' financial management strategies in upper secondary education units. This type of research is field research with qualitative methods of case studies at MAN 2 Yogyakarta. The respondents of this study are the Principal, committee, and financial manager of the school. The data collection process is carried out by making observations, interviews, and documentation. The data analysis researchers use with the Miles and Huberman Models is condensation, data presentation, and inference/verification. The results of this study show that the strategy carried out by the Principal in managing school finances during the Covid-19 pandemic is an evaluation of budget, fund efficiency, and efficiency of program implementation through the stages of the financial management process. The strategy then, in the policy in the form of a 30% cut in SPP costs and the temporary elimination of visit transportation costs, waiver of payment time extension for parents whose businesses are affected by Covid-19, a 10% cut to school activity budget.
Steady-state entanglement generation for nondegenerate qubits

We propose a scheme to dissipatively produce steady-state entanglement in a two-qubit system, via an interaction with a bosonic mode. The system is driven into a stationary entangled state, while we compensate the mode dissipation by injecting energy via a coherent pump field. We also present a scheme which allows us to adiabatically transfer all the population to the desired entangled state. The dynamics leading to the entangled state in these schemes can be understood in analogy with electromagnetically induced transparency and stimulated Raman adiabatic passage, respectively.
Open Source Assessment of Deep Learning Visual Object Detection

This paper introduces Detection Metrics, an open-source scientific software for the assessment of deep learning neural network models for visual object detection. This software provides objective performance metrics such as mean average precision and mean inference time. The most relevant international object detection datasets are supported along with the most widely used deep learning frameworks. Different network models, even those built from different frameworks, can be fairly compared in this way. This is very useful when developing deep learning applications or research. A set of tools is provided to manage and work with different datasets and models, including visualization and conversion into several common formats. Detection Metrics may also be used in automatic batch processing for large experimental tests, saving researchers time, and new domain-specific datasets can be easily created from videos or webcams. It is open-source, can be audited, extended, and adapted to particular requirements. It has been experimentally validated. The performance of the most relevant state-of-the-art neural models for object detection has been experimentally compared. In addition, it has been used in several research projects, guiding in selecting the most suitable network model architectures and training procedures. The performance of the different models and training alternatives can be easily measured, even on large datasets.
A Robust Graph Theoretic Solution of Routing in Intelligent Networks

Implementation of robust routing is very critical in network communication. Connecting devices like routers maintain databases for the whole network topology in the routing table. Each router needs to keep these tables updated with the best possible routes so that an efficient communication can always take place in nondelay tolerant intelligent networks that include military and tactical systems, vehicular communication networks, underwater acoustic networks, and intelligent sensor networks. The fast construction of shortest-path tree (SPT) is important to devise an efficient routing in a nondelay tolerant networks. That is why a simple and efficient algorithm is the need of the time. A robust routing solution SPT with <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>O</mi> <mfenced open="(" close=")"> <mrow> <mi>V</mi> <mo>+</mo> <mi>E</mi> </mrow> </mfenced> </math> time complexity is proposed to supersede the existing landmark.
Landing Reinforcement Learning onto Smart Scanning of The Internet of Things

Cyber search engines, such as Shodan and Censys, have gained popularity due to their strong capability of indexing the Internet of Things (IoT). They actively scan and fingerprint IoT devices for unearthing IP-device mapping. Because of the large address space of the Internet and the mapping’s mutative nature, efficiently tracking the evolution of IP-device mapping with a limited budget of scans is essential for building timely cyber search engines. An intuitive solution is to use reinforcement learning to schedule more scans to networks with high churn rates of IP-device mapping. However, such an intuitive solution has never been systematically studied. In this paper, we take the first step toward demystifying this problem based on our experiences in maintaining a global IoT scanning platform. Inspired by the measurement study of large-scale real-world IoT scan records, we land reinforcement learning onto a system capable of smartly scanning IoT devices in a principled way. We disclose key parameters affecting the effectiveness of different scanning strategies, and find that our system would achieve growing advantages with the proliferation of IoT devices.
Telemedicine Medical Ultrasonic System Based on Robotic Arm

In traditional medical ultrasound treatment, the doctor's hand needs to take the ultrasonic scanner and move it around the wound location.In this study, the ultrasonic scanner is grasped by a robotic arm, and the doctor uses the remote control robotic arm system to move the ultrasonic scanner around the wound location.In addition, a force sensor is installed on the robotic arm to sense the force feedback of the robotic arm touching the patient's body.The doctor uses the robotic arm to operate the ultrasonic scanner as if they were operating it themselves.The results show that through our developed long-distance robotic arm ultrasound system, the doctor can treat patients without the need for contact with them.
Tracking the location of a road-constrained radioactive source with a network of detectors

Data collected from a network of detectors and analyzed together has the opportunity to provide a more complete picture than when the data from each individual detector are analyzed independently. However, even with a dense array of detectors, the network data will not provide a complete picture and constraints will need to be added to the model in order to maximize the usefulness of the conclusions that can be drawn. In this work, we demonstrate this concept by considering the task of tracking a moving radioactive source of special nuclear material in a structured environment with data from a network of radiation detectors. Our approach uses a Bayesian model and analysis that naturally provides uncertainty in the estimate of the source’s dynamic location. We find that adding domain aware constraints to a Bayesian model (e.g., the location of the road) can improve both location inference and do so with diminished uncertainty even though the fit to gamma count data is largely unchanged.
Implementation of Improved Cryptography Algorithm

A network is an interconnected group of independent computing devices which uses a different set of protocols to communicate with each other independently and meaningfully. This communication should be carried out securely. Due to different attacks, this security sometimes gets compromised. So, to communicate securely different cryptography algorithms are used i.e., symmetric and asymmetric algorithms. Cryptography helps to achieve authentication, confidentiality, integrity, non-repudiation, and availability of data. Nowadays many algorithms provide security to data but these algorithms have various security flaws. To improve the strength of these algorithms, a new security protocol is designed using features of symmetric key and asymmetric key algorithms. The security principles can be achieved by AES and RSA algorithms. The main purpose of designing this algorithm is to provide better security to data in transit against passive as well as from active attacks. The new proposed hybrid algorithm is implemented in MATLAB R2019a. This algorithm will be analysed and compared on three parameters like avalanche effect, performance, and security against attacks. The proposed model will contribute towards improving the excellence of educators and academics, as well as increase competitiveness of educational programmes on cybersecurity among similar institutions in the EU countries.
Automatic Sentiment Classification of News Using Machine Learning Methods

With the rapid development of social economy, the society has entered into a new stage of development, especially in new media under the background of rapid development, makes the importance of news and information to get the comprehensive promotion, and in order to further identify the positive and negative news, should be fully using machine learning methods, based on the emotion to realize the automatic classifying of news, in order to improve the efficiency of news classification. Therefore, the article first makes clear the basic outline of news sentiment classification. Secondly, the specific way of automatic classification of news emotion is deeply analyzed. On the basis of this, the paper puts forward the concrete measures of automatic classification of news emotion by using machine learning.
Memory-Efficient Learned Image Compression with Pruned Hyperprior Module

Learned Image Compression (LIC) gradually became more and more famous in these years. The hyperprior-module-based LIC models have achieved remarkable rate-distortion performance. However, the memory cost of these LIC models is too large to actually apply them to various devices, especially to portable or edge devices. The parameter scale is directly linked with memory cost. In our research, we found the hyperprior module is not only highly over-parameterized, but also its latent representation contains redundant information. Therefore, we propose a novel pruning method named ERHP in this paper to efficiently reduce the memory cost of hyperprior module, while improving the network performance. The experiments show our method is effective, reducing at least 22.6% parameters in the whole model while achieving better rate-distortion performance.
Towards Fully Autonomous Negative Obstacle Traversal via Imitation Learning Based Control

Current research in experimental robotics has had a focus on traditional, cost-based, navigation methods. These methods ascribe a value of utility for occupying certain locations in the environment. A path planning algorithm then uses this cost function to compute an optimal path relative to obstacle positions based on proximity, visibility, and work efficiency. However, tuning this function to induce more complex navigation behaviors in the robot is not straightforward. For example, this cost-based scheme tends to be pessimistic when assigning traversal cost to negative obstacles. Its often simpler to ascribe high traversal costs to costmap cells based on elevation. This forces the planning algorithm to plan around uneven terrain rather than exploring techniques that understand if and how to safely traverse through them. In this paper, imitation learning is applied to the task of negative obstacle traversal with Unmanned Ground Vehicles (UGVs). Specifically, this work introduces a novel point cloud-based state representation of the local terrain shape and employs imitation learning to train a reactive motion controller for negative obstacle detection and traversal. This method is compared to a classical motion planner that uses the dynamic window approach (DWA) to assign traversal cost based on the terrain slope local to the robots current pose.
Detecting Arabic Offensive Language in Microblogs Using Domain-Specific Word Embeddings and Deep Learning

In recent years, social media networks are emerging as a key player by providing platforms for opinions expression, communication, and content distribution. However, users often take advantage of perceived anonymity on social media platforms to share offensive or hateful content. Thus, offensive language has grown as a significant issue with the increase in online communication and the popularity of social media platforms. This problem has attracted significant attention for devising methods for detecting offensive content and preventing its spread on online social networks. Therefore, this paper aims to develop an effective Arabic offensive language detection model by employing deep learning and semantic and contextual features. This paper proposes a deep learning approach that utilizes the bidirectional long short-term memory (BiLSTM) model and domain-specific word embeddings extracted from an Arabic offensive dataset. The detection approach was evaluated on an Arabic dataset collected from Twitter. The results showed the highest performance accuracy of 0.93% with the BiLSTM model trained using a combination of domain-specific and agnostic-domain word embeddings.
Towards Participatory Worldview in Education and Research: A Philosophical Inquiry on the Question- Why Participatory?

This article portrays the author’s philosophical inquiry on the prospects of a participatory worldview in education and research. To this, the author begins his inquiry process, reflecting upon his own personal/ professional shift from rational (analytical) academician to the transformative educational practitioner. The reflection further depicts his continuous growth as a participatory researcher. Reflecting upon his personal/ professional journey, which is parallel to the spiral of human civilization from participatory to (post)positivist to constructivist to transformative to participatory again, the author makes philosophical observation at genealogical progression (spiral) of participatory worldview both in the East and the West. Also, the author observes current developments in the field of quantum science and renders how such developments have paved manifold ways for a participatory worldview in Social Science, particularly in education and research.
A Combined Approach for Multi-Label Text Data Classification

Automated data analysis solutions are very dependent on data and its quality. The possibility of assigning more than one class to the same data item is one of the specificities that need to be taken into account. There are no solutions, dedicated to Lithuanian text data classification that helps to assign more than one class to data item. In this paper, a new combined approach has been proposed for multilabel text data classification for text analysis. The main aim of the proposed approach is to improve the accuracy of traditional classification algorithms by incorporating the results obtained using similarity measures. The experimental investigation has been performed using the financial news multilabel text data in the Lithuanian language. Data have been collected from four public websites and classified by experts into ten classes manually, where each of the data items has no more than two classes. The results of five commonly used algorithms have been compared for dataset classification: the support vector machine, multinomial naive Bayes, k-nearest neighbours, decision trees, linear and discriminant analysis. In addition, two similarity measures have been compared: the cosine distance and the dice coefficient. Research has shown that the best results have been obtained using the cosine similarity distance and the multinomial naive Bayes classifier. The proposed approach combines the results of these two methods. Research on different cases of the proposed approach indicated the peculiarities of its application. At the same time, the combined approach allowed us to obtain a statistically significant increase in global accuracy.
THE MEDIATING ROLE OF ORGANIZATIONAL CULTURE IN THE RELATIONSHIP BETWEEN INTELLECTUAL STIMULATION, INDIVIDUALIZED CONSIDERATION, AND ORGANIZATIONAL PERFORMANCES

This quantitative study aimed to determine the impacts of intellectual stimulation, individualized consideration on organizational culture utilizing transformational leadership theory and perspective theory. Data were obtained from three levels of managers of Information Technology companies in Jordan. Partial least squares structural equation modeling (PLS-SEM) was used for data analysis. Intellectual stimulation and individualized consideration were found to impact both organizational culture and organizational performances positively. Organizational culture was found to mediate the relationship between intellectual stimulation, individualized consideration, and organizational performances. Interesting findings relating to Information Technology companies in Jordan were identified. Institutions with a comparable situation could peruse this study to improve their organizational culture and performances. Finally, this study fills in the literature gaps, combined the transformational leadership theory and Harrison and Stokes model by providing insights about the mediating role of organizational culture in the relationship between transformational leadership styles and organizational performance.
SiamRDT: An Object Tracking Algorithm Based on a Reliable Dynamic Template

Most trackers are only dependent on the first frame as a template to search for and locate the target location in subsequent videos. However, objects may undergo occlusions and deformation over time, and the original snapshot of the object can no longer accurately reflect the current appearance of the object, which greatly limits the performance improvement of the tracker. In this paper, we propose a novel Siamese tracking algorithm with symmetric structure called SiamRDT, which reflects the latest appearance and motion states of objects through additional reliable dynamic templates. The model decides whether to update the dynamic template according to the quality estimation score and employs the attention mechanism to enhance the reliability of the dynamic template, adopting the depth-wise correlation algorithm to integrate the initial template and the dynamic template and the search area. Through reliable dynamic templates and credible initial templates, the model can fuse initial-state information and the latest-state information of objects. We conduct sufficient ablation experiments to illustrate the effectiveness of the proposed key components, and the tracker achieves very competitive results on four large-scale tracking benchmarks, namely OTB100, GOT-10k, LaSOT, and TrackingNet. Our tracker achieves an AO score of 61.3 on GOT-10k, a precision score of 56.5 on LaSOT, a precision score of 69.3 on TrackingNet, and a precision score of 90.5 on OTB100.
Movie Recommendation System Using Content-Based Filtering with Heroku Deployment

Abstract: Now days peoples watching movies to make their busy life calm , watching movie is not that easy as it takes a lot of time to select a movie due to large data set of movies exist in the world and it is very difficult and time consuming process to people to select a movie due to large data set but we can achieve this with the help of movie recommendation system using content based filtering ,where this recommendation system recommend a movie to the user based on the content he/she is watching. In this paper I am building a recommendation system using content- based filtering with Heroku deployment. Generally, recommendation systems are build using content -based filtering approach or collaborative filtering approach here in this paper I developed recommendation system using content-based filtering approach with Heroku Deployment. Heroku Deployment enables easy deployment of application.
Learning Hierarchical Semantic Correspondences for Cross-Modal Image-Text Retrieval

Cross-modal image-text retrieval is a fundamental task in information retrieval. The key to this task is to address both heterogeneity and cross-modal semantic correlation between data of different modalities. Fine-grained matching methods can nicely model local semantic correlations between image and text but face two challenges. First, images may contain redundant information while text sentences often contain words without semantic meaning. Such redundancy interferes with the local matching between textual words and image regions. Furthermore, the retrieval shall consider not only low-level semantic correspondence between image regions and textual words but also a higher semantic correlation between different intra-modal relationships. We propose a multi-layer graph convolutional network with object-level, object-relational-level, and higher-level learning sub-networks. Our method learns hierarchical semantic correspondences by both local and global alignment. We further introduce a self-attention mechanism after the word embedding to weaken insignificant words in the sentence and a cross-attention mechanism to guide the learning of image features. Extensive experiments on Flickr30K and MS-COCO datasets demonstrate the effectiveness and superiority of our proposed method.
Regulating hate speech and disinformation online while protecting freedom of speech as an equal and positive right – comparing Germany, Europe and the United States

When regulating hate speech and disinformation online, first, do not suppress ideas or viewpoints as such, second, protect speech and other fundamental rights as positive freedoms, not only vertically but horizontally too, and, third, counteract private disinformation as well as government disinformation.