Reviewerly/exHarmony huggingface dataset:
Can Deep Learning Approaches Detect Complex Text? Case of Onomatopoeia in Comics Albums

In recent years, the use of deep learning has contributed to the development of new approaches that outperform traditional methods for many computer vision and document analysis tasks. Many text detection approaches have been proposed and target historical documents, newspapers, administrative documents but also texts in the wild. However, some documents can present complex texts where the shape, the size and the orientation can vary within each word. Onomatopoeia are an example of these complex texts often buried in graphic elements. In this article, we present a study to determine whether the deep learning based text detection methods can tackle such complex texts. First, we selected two well-known deep learning approaches of the literature and then analysed their relevance for the detection of the onomatopoeia. In a second stage, we tried to improve their performance. The experiments show that a simple transfer learning isn’t enough. The models have to consider the characteristics of the onomatopoeia. The proposed strategy show an improvement of about 40% for the detection task of the onomatopoeia.
IndRAD: A Benchmark for Anomaly Detection on Industrial Robots

Many approaches for fault detection in industrial processes has been presented in the literature, with approaches spanning from traditional univariate statistics to complex models able to encode the multivariate nature of time-series data. Although the vast corpus of works on this topic, there is no public benchmark shared among the community that can serve as a testbench for these methods, allowing researchers to evaluate their proposed approach with other state of the art approaches. In this paper we present the Industrial Robot Anomaly Detection (IndRAD) dataset as a benchmark for evaluating fault detection algorithms on industrial robots. The dataset is composed by 13 nominal trajectories and 3 trajectories with structural anomalies. We also propose a protocol to inject sensory anomalies in clean data. The dataset, code to reproduce these experiments and a leaderboard table to be used for future research are available at https://github.com/franzsetti/IndRAD .
Implementing Frame Work of Cloud Computing in Pharmaceuticals Industries of Pakistan

Cloud computing is providing cheaper, cost effective efficient automation, data management services to the organizations and this trend is gradually increasing. Using cloud based services can eliminate cost and complexity of managing the underlying application. It allows users to access and use the most sophisticated technologies without being required to pay enormous amounts of money to purchase the system or to develop it in-house. This study aimed to investigate cloud computing advantages and disadvantages, its services and implementation stages in pharmaceutical industry of Pakistan. Cloud computing practices included Cost Saving, Security &amp; Privacy, Automation, Data Integrity, and Regulatory compliance. An Adopted survey was planned and circulated over an example of 400 workers. Research hypotheses were evaluated using structured equation modelling. The outcome shows that cost savings, security and privacy, competitive pressure, automation and awareness have a positive and fundamental effect on pharmaceutical operations. Strangely, there is a negative effect of external support on cloud computing. The effects are noticeable in the pharmaceutical industry.
Attacking and Defending Printer Source Attribution Classifiers in the Physical Domain

The security of machine learning classifiers has received increasing attention in the last years. In forensic applications, guaranteeing the security of the tools investigators rely on is crucial, since the gathered evidence may be used to decide about the innocence or the guilt of a suspect. Several adversarial attacks were proposed to assess such security, with a few works focusing on transferring such attacks from the digital to the physical domain. In this work, we focus on physical domain attacks against source attribution of printed documents. We first show how a simple reprinting attack may be sufficient to fool a model trained on images that were printed and scanned only once. Then, we propose a hardened version of the classifier trained on the reprinted attacked images. Finally, we attack the hardened classifier with several attacks, including a new attack based on the Expectation Over Transformation approach, which finds the adversarial perturbations by simulating the physical transformations occurring when the image attacked in the digital domain is printed again. The results we got demonstrate a good capability of the hardened classifier to resist attacks carried out in the physical domain.
A Comparative Analysis of Credit Card Detection Models

Data mining and machine learning are gaining popularity for fraud detection due to their effective results to cater the exponentially growing card transactions that comes with the fast-growing frauds. The aim of this paper is to explore which of the many techniques are capable to detect fraudulent transactions the best. Methods such as logistic regression, decision tree, support vector machine (SVM), Naive Bayes, and random forest are evaluated on their performance based on factors such as accuracy, precision, recall, and F1-score. The results showed that the random forest performed the best among the five methods investigated for credit card fraud detection.
Gym-DC: A Distribution Centre Reinforcement Learning Environment

Distribution centres in supply chains receive shipments and forward them to transport providers for the next part of their journey to their final destinations. In some Physical Internet proposals, distribution centres will be autonomous. The decision system should choose a transport provider for each packet. Reinforcement learning is a well-established method for learning policies by acting in an environment and observing states. Coupled with Deep Learning, it has shown significant results in competitive environments like board games. To develop and evaluate Reinforcement Learning solutions for managing a distribution center on the Physical Internet, we need a simulated environment that should be as close as possible to real-world conditions. We present Gym-DC - the first framework for Reinforcement Learning research for distribution centres and Physical Internet hubs, based on the OpenAI Gym.
The Complete Study of the Movement Strategies of Trained Agents for Visual Descriptors of Advertising Signs

We provide a complete description of our investigation into specialized visual descriptors application as a part of combined classifier architecture for advertising signboards photographs classification problem. We propose novel types of descriptors (pure convolutional neural networks based and based on trainable parametrized agent movement strategies) showing the state of the art results in the extraction of visual characteristics and related semantics of text fonts presented on a sign. To provide comparisons of developed approaches and its effectiveness examination we used two datasets of commercial building facade photographs grouped by the type of presented business.
Tight security analysis of the public Permutation-based $ {{\textsf{PMAC_Plus}}} $

In CRYPTO 2011, Yasuda proposed a variable input-length PRF based on an n-bit block cipher, called PMAC Plus.PMAC Plus is a rate-1 construction and inherits the well-known PMAC parallel network with a low additional cost.However, unlike PMAC, PMAC Plus is secure roughly up to 2 2n/3 queries.Later in CRYPTO 2018, Leurent et al., and then Lee et al. in EUROCRYPT 2020 established a tight security bound of 2 3n/4 on PMAC Plus.In this paper, we propose a public permutation-based variable input-length PRF called pPMAC Plus.We show that pPMAC Plus is secure against all adversaries that make at most 2 2n/3 queries.We also show that the bound is essentially tight.It is of note here that instantiation of each block cipher of PMAC Plus with the two-round iterated Even-Mansour cipher can yield a beyond-birthday-secure PRF based on public permutations.Altogether, the solution incurs (2ℓ + 4) permutation calls, whereas our proposal requires only (ℓ + 2) permutation calls, ℓ being the maximum number of message blocks.
Exploring Cultural Learning with Vertical Chatbot: Korda

Cultural heritage is the tangible and tacit legacy of attributes of society inherited from past generations. Its preservation relies on the memory of communities and individuals, hence limiting it from widespread access. To address this issue, this paper proposes the use of a vertical chatbot that serves as the conversational interface between the general public and the expert knowledge. The chatbot is called Korda that focuses on the lost heritage of a traditional dance in Malaysia; the Kuda Kepang. The prototype of Korda is developed on the Chatfuel framework and published as a Messager bot. User acceptance testing of Korda indicated positive feedback due to lack of prior knowledge in the cultural dance. Korda is hoped to promote access to and enjoyment of cultural diversity in Johor, Malaysia, in particular the Kuda Kepang.
A Blockchain Trusted Mechanism (BTM) for Internet of Unmanned Things (IoUT) Using Comprehensive and Adaptive Schemes

The intelligent decision-making and communication among devices, allowing direct transmission among various entities and human, is the key component of Internet of Unmanned Things (IoUT). By gathering various smart devices (SDs) into clusters (Cls), the SDs within one cluster may share the information and deter malicious devices from network access by modifying and accessing the controls. The security of SDs can be provided through various security frameworks and mechanisms with and without blockchain. However, the SDs are not fully secured by spreading fake information and text alterations in the network. In this paper, we have proposed a secure and trusted communication mechanism using blockchain-based adaptive and comprehensive trust computation of each DS. The proposed mechanism ensures significant security and trust even with various malicious involvements. The simulation results are further demonstrated against various mechanisms such as probability attack, accuracy and communication delay in comparison of existing approach.
Optimization of the Computer Vision System for the Detection of Moving Objects

The main goal of the presented work is to optimize the developed intelligent system for recognizing and detecting vehicles on video data using the YOLOv3 convolutional neural network. Basic results are obtained for real work conditions with the use of graphic processors. In addition, a special performance study was made for the Intel Core i5–8500 CPU. Optimization is based not only on classical neural network methods, such as model pruning, but also modified procedures have been proposed for efficient processing of video information, in particular, optical flow and motion prediction. After the optimization, the data processing speed increased by 4 times when using the NVIDIA RTX 2080 Super GPU and amounted to about 30 frames per second. CPU acceleration was achieved using the Intel OpenVINO toolkit. Performance on the CPU reached almost the same values as on the video card, and the acceleration was almost 30 times from slowest model to fastest. It is important to note that the implementation of optical flow and motion extrapolation was not required on the CPU.
Towards Content-Aware Pixel-Wise Comic Panel Segmentation

Comic panel detection is the task of identifying panel regions from a given comic image. Many comic datasets provide the borders of the panel lines as its panel region annotations, expressed in formats such as bounding boxes. However, since such panel annotations are usually not aware of the contents of the panel, they do not capture objects that extend outside of the panels, causing such objects to be partially discarded when panels are cropped along the annotations. In such applications, a content-aware annotation that contains all of the contents in each panel is suitable. In this paper, we assess the problem of content-aware comic panel detection using two types of annotations. We first create a small dataset with bounding box annotations where each region contains the entire contents of each panel, and train a detection model. We also explore training a pixel-wise instance segmentation model using synthetic data.
Blockchain-Based Transaction Platform for Peer-to-Peer Energy Trading

Peer-to-peer (P2P) energy trading is introduced as an innovative transactive energy platform, as defined by European Energy Regulators. All intractions between various parties involved such as producers, consumers and prosumers can be encapsulated using P2P platform. In comparison with conventional market mechanisms, the P2P networking makes a flat communication between a large number of individual nodes. All interconnected participations take part in recording data and transacting information and messages with each other. In this chapter, the P2P energy trading approach is discussed. Moreover, its pros and cons are presented. Then, application of blockchain technology in P2P energy markets are provided. In addition, the security and privacy issues of the smart energy systems as well as weaknesses and vulnerabilities of the blockchain enabled P2P energy trading platform are summarized.
Research on Command and Control of Maritime Emergency Rescue Based on Big Data

With the development of big data, the maritime emergency rescue has been more and more important, which need accurate command and control in time. The big data provides an powerful tool for us to give us warning earlier in danger, design and prepare the plans for rescue. In this paper, as the data increases, we discuss some basic concepts of marine emergency rescue, and then introduce the process and the most important technologies for emergency rescue. Finally, we analyze the requirements to build the strong emergency rescue system.
Reframing and Broadening Adversarial Stylometry for Academic Integrity

Adversarial stylometry can be a form of academic fraud that involves the deceptive manipulation of text through manual, hybrid, or automatic techniques to obfuscate or impersonate with the intent to circumvent plagiarism detection; exploit or mask personal identifiers (including demographic, personality, and behavior), alter time series linguistic features, either for gain or to avoid undue discrimination. This term was reframed for academic integrity in this chapter to understand this vulnerability fully. A threat model was created that identified 29 threats classified as high, moderate, and low. Text-matching software is starting to integrate stylometry to capture one’s stylistic writing voice, particularly given the problem of contract cheating. New ways to circumvent progress come with any technical advancement, and an adversarial response is often generated. Higher education needs to be aware given its text-matching software usage. This chapter builds on the early work by Brennan, Afroz, & Greenstead (ACM Transactions on Information and System Security, 15(3), 1–22, 2011), and reviewed the literature on adversarial stylometry, emphasizing the intersection with plagiarism and contract cheating. Research examines academic integrity through the lens of forensic linguistics and looks to the field of fake news to provide new insights. This work is relevant for scholars and practitioners interested in academic integrity, text- matching software, and security.
Undoing Epistemic Violence in Educational Philosophy: Changing the Story with Donna Haraway’s SF

The aim of this introduction to the present JEP volume on epistemic violence is threefold: by linking a post- and decolonial perspective to the productively ambivalent German notion of Gewalt, I first argue why it is important to keep analysing and theorising epistemic violence across different scholarly disciplines and fields of (academic) knowledge production. Second, and based on the concept of the coloniality of power, knowledge, and being, I present a multi-disciplinary approach to the concept of epistemic violence that is rooted in multi-disciplinary efforts to work with it. Based on a multitude of approaches to the problem, readers from many disciplines can find ways to make use of it within their own terrain of knowledge. Third, I introduce the notion of Hegemonie(selbst-)kritik in order to link the heterogeneous efforts of dealing with epistemic violence, both as a phenomenon and as a concept, that are presented in this volume, with a deep reflection on our own scholarly practices across modernity’s epistemic territory and its Euro-Anglo-American epistemic monoculture. The latter has inspired the title and focus of this article. Since even critical scholarship cannot transcend the double-bind that comes along with knowledge production in colonial modernity, we should remember that our efforts to undoing epistemic violence remain entangled with the colonial condition. This is why I suggest speaking of un/doing epistemic violence instead of claiming to be able to fully undo it.
Generative Adversarial Networks in Federated Learning

Federated learning (FL), a decentralized approach, has been applied in many scenarios. However, many challenges remain, one of which is that the performance of the global model is significantly affected when training by datasets with non-independent and identical distribution (non-IID) data. In this paper, we report on an experiment to simulate a non-IID MNIST data scenario to measure the performance changes of Generative Adversarial Networks (GAN) in FL. We used asynchronous aggregation for the global model, evaluated the global model in FL, and compared the local models by different evaluation methods: manual evaluation and extrinsic evaluation—a pre-trained image classifier evaluation and inception score (IS) based on MNIST data. The experiments show that the recognition rate of MNIST images generated by the initial global model trained is 78% and IS is 6.53. Meanwhile, the local models on node1 and node2 have recognition rates of 71.2% and 70.6%, IS were 4.11 and 4.57, respectively. The new aggregated global model based on node1 and node2 has a recognition rate of 77%, IS is 5.74. The experimental results illustrate that the fused model generated using the FedAvg algorithm still has a significant improvement over the local models, but still has a slightly reduced efficacy compared to the initial global model. Analysis of the distribution of the generated images shows that the fused global model is biased for the data appearing on node1 and node2. Our experiment shows that an FL approach to GAN can still have effective global models that respect the privacy of local nodes.
Do the Bitcoin’s Scaling Solutions Work? An Empirical Analysis of Segwit Policy Change

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Manifold Learning by a Deep Gaussian Process Variational Autoencoder

This paper presents a Deep Gaussian Process Variational Autoencoder (DGPVA), i.e., a novel Manifold Learning algorithm based on Deep Gaussian Processes. Besides, in order to assess quantitatively the performance of the proposed algorithm, an experimental validation protocol has been proposed. Experimental tests on synthetic and real data sets show that DGPVA compares favourably with state-of-the-art Manifold Learning algorithms.
Testing the Academic Information System Application of SDN 192 Salobongko with the Boundary Value Analysis Technique

Each software is developed with a series of structured stages based on user needs. These stages are formed from a framework that forms the basis of well-planned and controlled software development. Software testing is a stage that ensures that software development that has been developed is free from errors or errors before being distributed to users. This study aims to find errors in input by using black box testing with boundary value analysis techniques in SDN 192 Salobongko academic information system applications. From the results of the test cases performed, it shows that the test was successfully carried out. The findings in this study are that the testing of the academic information system application at SDN 192 Salobongko has shown successful test results based on the test case scenarios that have been designed.
PapyTwin net: a Twin network for Greek letters detection on ancient Papyri

Ancient historical documents, such as Greek papyri, are crucial for understanding human knowledge and history. However, transcribing and translating these documents manually is a difficult and time-consuming process. As a result, an automatic algorithm is required to identify and interpret the writing on these ancient historical documents with accuracy and dependability. In this work, we introduce PapyTwin, a deep neural network which consists of two subnetworks, the first and second twin, that cooperate together to address the challenge of detecting Greek letters on ancient papyri. While the first twin network aims at uniforming the letter size across the images, the second twin network predicts letter bounding boxes based on these letter-uniformed images. Experiment results show that our proposing approach outperformed the baseline model by a large margin, suggesting that uniform letter size across images is a crucial factor in enhancing the performance of detection networks on ancient documents such as Greek papyri.
DocLangID: Improving Few-Shot Training to Identify the Language of Historical Documents

In this work, we propose DocLangID, a transfer learning approach to identify the language of unlabeled historical documents. We achieve this by first leveraging labeled data from a different but related domain of historical documents. Secondly, we implement a distance-based few-shot learning approach to adapt a convolutional neural network to new languages of the unlabeled dataset. By introducing small amounts of manually labeled examples from the set of unlabeled images, our feature extractor develops a better adaptability towards new and different data distributions of historical documents. We show that such a model can be effectively fine-tuned for the unlabeled set of images by only reusing the same few-shot examples. We showcase our work across 10 languages that mostly use the Latin script. Our experiments on historical documents demonstrate that our combined approach improves the language identification performance, achieving 74% recognition accuracy on the four unseen languages of the unlabeled dataset.
Enhancing Named Entity Recognition for Holocaust Testimonies through Pseudo Labelling and Transformer-based Models

The Holocaust was a tragic and catastrophic event in World War II (WWII) history that resulted in the loss of millions of lives. In recent years, the emergence of the field of digital humanities has made the study of Holocaust testimonies an important area of research for historians, Holocaust educators, social scientists, and linguists. One of the challenges in analysing Holocaust testimonies is the recognition and categorisation of named entities such as concentration camps, military officers, ships, and ghettos, due to the scarcity of annotated data.
The money tree: Exploring central bank digital currency blockchain imaginaries

Central banks worldwide are developing, piloting and launching new central bank digital currencies (CBDCs). As the hub for the central banking community, the Bank for International Settlements (BIS) promotes a curiously botanical CBDC imaginary. From ‘money flowers’ to ‘tree trunks’ and a ‘strong canopy’, This helps to naturalize CBDC without clarifying its sociopolitical implications or envisioned monetary future, such as geopolitical tensions and financial fragmentation, new modes of financial interaction or the strengthened role of central banks. While omitting the paradoxes and ambivalences of CBDC, the imaginary of the BIS structures the enfolding discourses and allows the bank to function as a think tank for financial policy‐making.