Reviewerly/exHarmony huggingface dataset:
Union Buster: A Cross-Container Covert-Channel Exploiting Union Mounting

Software containers provide a light-weight counterpart to virtual machines, utilizing the native host operating system to efficiently manage virtualization. Though efficient, this sharing of resources opens a potentially exploitable communication channel between collocated containers. To this end, we present a novel class of container isolation attacks that exploit union mounting, a key infrastructural component that allows for file system resource sharing among containers. We show that these vulnerabilities enable an unprivileged attacker, running a container on a shared commercial platform, to attack vulnerable victim containers on the same platform. More precisely, we showcase two attacks: one, which gleans information from the vulnerable containers, and another which establishes a covert side channel for exfiltrating data from the victim. Our attack implementations leverage a page-cache attack (CVE-2019-5489), but the attack surface is intrinsic to the efficiency needs of container management, and they apply even to the most recently patched Linux kernels. Our results highlight the need to rethink the page cache design in the context of multi-tenant clouds, and we propose some partial mitigations in this direction.
Migration Strategies and Refactoring Methodology When Moving a Legacy ERP System to Cloud Platform

Migration to the cloud is something most enterprises are doing right now, and Enterprise Resource Planning (ERP) applications form the bulk of these projects. Essentially, the migration of critical business applications is happening because on-premise solutions are becoming very occur to move to the next generation of ERP. Also, cloud computing brings major improvements to the ERP; these improvements include mobility and its benefits, data analysis, and collaboration and sharing in government and industry. Furthermore, factors such as time, training, and extensive re-engineering activities make the migration process time consuming and error-prone. Although many research discuss Cloud ERP system adoption and advantages and disadvantages of switching to a Cloud ERP system, only a few specifically address the resolution of these constraints. This chapter presents the process of migrating applications to a different type of cloud. In addition, on the basis of a review of the literature and related emerging methodologies, we give several methodologies used to migrate from an on-premise ERP system to Cloud ERP. And based on refactoring strategy, we explain why companies need to do refactoring before moving to the cloud, we also present ERP refactoring strategies and migration feasibility. Finally, we propose one generic methodology for migrating legacy ERP systems to the cloud.
Exploring the Drivers of Cryptocurrency Adoption Intent in Africa

The evolution of technology has made cryptocurrency an alternative form of exchange that has attracted growing interest from individuals and businesses. Its adoption continues to be explosive worldwide and further motivated by the financial behaviors fostered by the COVID-19 crisis. The present study investigates the factors influencing the intention to adopt cryptocurrency in Africa through the Diffusion of Innovation Theory (DOI) and the Theory of Reasoned Action (TRA). We test a sample of 124 individuals using Partial Least Square - Structural Equation Modeling (PLS-SEM). The results show that compatibility, relative advantage, and attitude towards cryptocurrency adoption positively influence the intention. The study provides substantial implications in understanding cryptocurrency adoption in the African context and gives good guidance for developing this kind of currencies in Africa.
An Adaptive Resource Allocation Protocol for Dynamic Environments

Resource allocation in a dynamic environment deals with the problem of how participants coordinate in allocating a set of resources among each other, while the environment dynamically changes over time. In this paper, we investigate a variant of this problem in which participants perform tasks which require multiple items of different resource types, while the tasks and resources arrive over time. For example, a network of hospitals may need to coordinate in allocating resources such as blood products, while resource supply and demand change over time. We model this problem as a multi-agent coordination problem, and propose a decentralized, self-organized approach, in which all decisions are made by individual agents and there is no central control. We present an adaptive negotiation-based protocol, which enables an agent to have concurrent negotiations for different resources types, and to combine resource contributions from multiple provider agents. We evaluate our proposed protocol by developing simulation models. Our simulation results illustrate its scalability and efficiency.
An Explainable Medical Imaging Framework for Modality Classifications Trained Using Small Datasets

With the huge expansion of artificial intelligence in medical imaging, many clinical warehouses, medical centres and research communities, have organized patients' data in well-structured datasets. These datasets are one of the key elements to train AI-enabled solutions. Additionally, the value of such datasets depends on the quality of the underlying data. To maintain the desired high-quality standard, these datasets are actively cleaned and continuously expanded. This labelling process is time-consuming and requires clinical expertise even when a simple classification task must be performed. Therefore, in this work, we propose to tackle this problem by developing a new pipeline for the modality classification of medical images. Our pipeline has the purpose to provide an initial step in organizing a large collection of data and grouping them by modality, thus reducing the involvement of costly human raters. In our experiments, we consider 4 popular deep neural networks as the core engine of the proposed system. The results show that when limited datasets are available simpler pre-trained networks achieved better results than more complex and sophisticated architectures. We demonstrate this by comparing the considered networks on the ADNI dataset and by exploiting explainable AI techniques that help us to understand our hypothesis. Still today, many medical imaging studies make use of limited datasets, therefore we believe that our contribution is particularly relevant to drive future developments of new medical imaging technologies when limited data are available.
Writer Identification and Writer Retrieval Using Vision Transformer for Forensic Documents

Writer identification and writer retrieval deal with the analysis of handwritten documents regarding the authorship and are used, for example, in forensic investigations. In this paper, we present a writer identification and writer retrieval method based on Vision Transformers. This is in contrast to the current state of the art, which mainly uses traditional Convolutional-Neural-Network-approaches. The evaluation of our self-attention-based and convolution-free method is done on two public datasets (CVL Database and dataset of the ICDAR 2013 Competition on Writer Identification) as well as a forensic dataset (WRITE dataset). The proposed system achieves a top-1 accuracy up to 99% (CVL) and 97% (ICDAR 2013). In addition, the impact of the used script (Latin and Greek) and the used writing style (cursive handwriting and block letters) on the recognition rate are analyzed and presented.
Privacy Preserving DCOP Solving by Mediation

In this study we propose a new paradigm for solving DCOPs, whereby the agents delegate the computational task to a set of external mediators who perform the computations for them in an oblivious manner, without getting access neither to the problem inputs nor to its outputs. Specifically, we propose MD-Max-Sum, a mediated implementation of the Max-Sum algorithm. MD-Max-Sum offers topology, constraint, and decision privacy, as well as partial agent privacy. Moreover, MD-Max-Sum is collusion-secure, as long as the set of mediators has an honest majority. We evaluate the performance of MD-Max-Sum on different benchmarks. In particular, we compare its performance to PC-SyncBB, the only privacy-preserving DCOP algorithm to date that is collusion-secure, and show the significant advantages of MD-Max-Sum in terms of runtime.
Two-Party E-Commerce Protocols

This chapter presents the state of the art and a review of 2-party e-commerce protocols that provide a fair exchange between the payment and digital/physical products. Also, solutions that ensure the privacy of customers in these e-commerce systems are discussed.
Entity Recommendation With Negative Feedback Memory Networks for Topic-Oriented Knowledge Graph Exploration

Knowledge graph exploration is an interactive knowledge discovery process over the knowledge graph. Entity recommendation deals with the information overflow issue when exploring the large-scale unfamiliar knowledge graphs. The traditional personalized entity recommendation methods for knowledge graph explorations rarely consider the adaptive topic-oriented long-term positive- and negative intent modeling. In this article, we propose a topic-oriented entity recommendation method during the knowledge graph exploration. We build a negative feedback memory network model for obtaining the user's long-term negative intents. We propose a transformer-based sequence encoder for the positive intents. We dynamically obtain the adaptive intents by aggregating the positive- and negative intents by the proposed intent attention mechanism. Experiments show that our method has advantages in TopK entity recommendations.
Recurrent Autoencoder Ensembles for Brake Operating Unit Anomaly Detection on Metro Vehicles

The anomaly detection of the brake operating unit (BOU) in the brake systems on metro vehicle is critical for the safety and reliability of the trains. On the other hand, current periodic inspection and maintenance are unable to detect anomalies in an early stage. Also, building an accurate and stable system for detecting anomalies is extremely difficult. Therefore, we present an efficient model that use an ensemble of recurrent autoencoders to accurately detect the BOU abnormalities of metro trains. This is the first proposal to employ an ensemble deep learning technique to detect BOU abnormalities in metro train braking systems. One of the anomalous cases on metro vehicles is the case when the air cylinder (AC) pressures are less than the brake cylinder (BC) pressures in certain parts where the brake pressures increase before coming to a halt. Hence, in this work, we first extract the data of BC and AC pressures. Then, the extracted data of BC and AC pressures are divided into multiple subsequences that are used as an input for both bi-directional long short-term memory (biLSTM) and bi-directional gated recurrent unit (biGRU) autoencoders. The biLSTM and biGRU autoencoders are trained using training dataset that only contains normal subsequences. For detecting abnormalities from test dataset which consists of abnormal subsequences, the mean absolute errors (MAEs) between original subsequences and reconstructed subsequences from both biLSTM and biGRU autoencoders are calculated. As an ensemble step, the total error is calculated by averaging two MAEs from biLSTM and biGRU autoencoders. The subsequence with total error greater than a pre-defined threshold value is considered an abnormality. We carried out the experiments using the BOU dataset on metro vehicles in South Korea. Experimental results demonstrate that the ensemble model shows better performance than other autoencoder-based models, which shows the effectiveness of our ensemble model for detecting BOU anomalies on metro trains.
Analyzing Information-Gathering Behavioral Sequences During Game-Based Learning Using Auto-recurrence Quantification Analysis

Analyzing behavioral sequences is essential for identifying how interaction with scientific texts built into a game-based learning environment (GBLE) relates to learning. Undergraduate students (n = 82) were recruited to learn with Crystal Island, a GBLE that teaches learners about microbiology. Learners were randomly assigned to either the full agency (i.e., complete control over one's own actions) or partial agency condition (i.e., restricted control). As participants learned with Crystal Island, log-file data captured their information-gathering behaviors, defined as their interactions with scientific information—i.e., posters, books, research articles, or talking to non-player characters.. To examine how learners deployed information-gathering behaviors, this paper used auto-Recurrence Quantification Analysis (aRQA). This analysis extracts the entropy (i.e., the number of unique sequential patterns denoting behavioral novelty) of learners' information-gathering behaviors. Results found that learners with restricted agency demonstrated greater entropy, or more novel information-gathering behaviors than learners with full agency. Further, results suggested an interaction between agency and entropy on learning gains, where learners with greater entropy in the partial agency condition demonstrated greater learning gains. We conclude GBLEs should scaffold learners' actions while promoting increased diversity in information-gathering behaviors to improve learning outcomes.
Can Emotion Carriers Explain Automatic Sentiment Prediction? A Study on Personal Narratives

Deep Neural Networks (DNN) models have achieved acceptable performance in sentiment prediction of written text.However, the output of these machine learning (ML) models cannot be natively interpreted.In this paper, we study how the sentiment polarity predictions by DNNs can be explained and compare them to humans' explanations.We crowdsource a corpus of Personal Narratives and ask human judges to annotate them with polarity and select the corresponding token chunks -the Emotion Carriers (EC) -that convey narrators' emotions in the text.The interpretations of ML neural models are carried out through Integrated Gradients method and we compare them with human annotators' interpretations.The results of our comparative analysis indicate that while the ML model mostly focuses on the explicit appearance of emotions-laden words (e.g.happy, frustrated), the human annotator predominantly focuses the attention on the manifestation of emotions through ECs that denote events, persons, and objects which activate narrator's emotional state.
Android GNSS seismogeodesy

The low-cost Android devices producing raw GNSS and accelerometer data are tightly coupled to compute seismogeodetic solutions, such as crowdsourcing earthquake sensors. Smart devices' embedded GNSS antennas are passive and omnidirectional with linear polarizations, of which the received carrier-phase signals are quite susceptible to multipath effects. Receiver-dependent errors from the smart devices' embedded GNSS chipset are also a significant factor in the deteriorating performance of Android GNSS positioning. Consequently, the implementation of Android GNSS high-precision positioning requires evaluating measurement noise affected by multipath and duty cycle, and investigating adverse phenomena present in smartphone-derived observables, such as receiver-dependent phase biases. In response to this, improvements in terms of algorithms and strategies, which address the features of Android GNSS observations, need to be developed. The feasibility of integrating Android GNSS/accelerometer sensors to capture higher-precision broadband vibrations is also verified.
Enriching Contextualized Representations with Biomedical Ontologies: Extending KnowBert to UMLS

Currently, biomedical document processing is mostly human work. Software solutions which attempt to alleviate this burden exist but generally do not perform well enough to be helpful in many applications. Concurrently, there exist projects which organize concepts in the biomedical field. Therefore, we seek to leverage existing structured knowledge resources to improve biomedical language modeling. In this paper, we provide an implementation integrating the UMLS knowledgebase into a BERT-based language model, aiming to improve its performance in biomedical Named Entity Recognition. To achieve this, we extend KnowBert, a recently developed technique for integrating knowledge into language models. Preliminary results reveal the challenges of applying KnowBert to the biomedical domain given the number and subtlety of different concepts in UMLS. Going forward, addressing these challenges and combining this with other approaches such as BioBERT may help expand the range of usefully automatable biomedical language processing tasks.
Weakly Supervised Attended Object Detection Using Gaze Data as Annotations

We consider the problem of detecting and recognizing the objects observed by visitors (i.e., attended objects) in cultural sites from egocentric vision. A standard approach to the problem involves detecting all objects and selecting the one which best overlaps with the gaze of the visitor, measured through a gaze tracker. Since labeling large amounts of data to train a standard object detector is expensive in terms of costs and time, we propose a weakly supervised version of the task which leans only on gaze data and a frame-level label indicating the class of the attended object. To study the problem, we present a new dataset composed of egocentric videos and gaze coordinates of subjects visiting a museum. We hence compare three different baselines for weakly supervised attended object detection on the collected data. Results show that the considered approaches achieve satisfactory performance in a weakly supervised manner, which allows for significant time savings with respect to a fully supervised detector based on Faster R-CNN. To encourage research on the topic, we publicly release the code and the dataset at the following url: https://iplab.dmi.unict.it/WS_OBJ_DET/ .
Opinion Mining for Breast Cancer Disease Using Apriori and K-Modes Clustering Algorithm

Data mining procedures have been broadly used to mine learned data from medicinal information bases. Sentiment Mining is a procedure of programmed extraction of learning by method for the conclusion of others about some specific item, theme or Issue. Sentiment analysis implies deciding the subjectivity, extremity (positive/negative) and extremity quality and so forth, with a bit of text. Clustering is the methodology of making a get together of dynamic things into classes of near articles. In this paper, we are proposing a two way clustering algorithm for breast cancer disease. Apriori hybrid algorithm and K-Modes Algorithm is used to cluster the opinions effectively and to improve the performance in the online data set. Apriori-Hybrid is the mix of count Apriori and Apriori-TID, which can mastermind the huge item sets and improve the accuracy of collection of dangerous development. It can moreover uncover understanding into the basic part that enables each malady type to suffer and thrive, which in this way help in the early revelation of the threatening development. We propose Apriori-Hybrid as an extemporized calculation for tumour characterization.
On Grain-Like Small State Stream Ciphers Against Fast Correlation Attacks: Cryptanalysis of Plantlet, Fruit-v2 and Fruit-80

Abstract The fast correlation attack (FCA) is one of the most important cryptanalytic techniques against LFSR-based stream ciphers. In CRYPTO 2018, Todo et al. found a new property for the FCA and proposed a novel algorithm which was successfully applied to the Grain family of stream ciphers. Nevertheless, these techniques cannot be directly applied to Grain-like small state stream ciphers with keyed update, such as Plantlet, Fruit-v2 and Fruit80. In this paper, we study the security of Grain-like small state stream ciphers by the FCA. We first observe that the number of required parity-check equations can be reduced when there are multiple different parity-check equations. With exploiting the Skellam distribution, we introduce a sufficient condition to identify the correct LFSR initial state and derive a new relationship between the number and bias of the required parity-check equations. Then, a modified algorithm is presented based on this new relationship, which can recover the LFSR initial state no matter what the round key bits are. Under the condition that the LFSR initial state is known, an algorithm is given against the degraded system and to recover the NFSR state at some time instant, along with the round key bits. As cases study, we apply our cryptanalytic techniques to Plantlet, Fruit-v2 and Fruit-80. As a result, for Plantlet, our attack takes $ 2^{73.75} $ time complexity and $ 2^{73.06} $ keystream bits to recover the full 80-bit key. Regarding Fruit-v2, $ 2^{55.34} $ time complexity and $ 2^{55.62} $ keystream bits are needed to determine the secret key. As for Fruit-80, $2^{64.47}$ time complexity and $2^{62.82}$ keystream bits are required to recover the secret key. More flexible attacks can be obtained with lower data complexity at the cost of increasing the attack time. Especially, for Fruit-v2, a key recovery attack can be launched with data complexity of $2^{42.38}$ and time complexity of $2^{73.31}$. Moreover, we have implemented our attack methods on a toy version of Fruit-v2. The attack matches the expected complexities predicted by our theoretical analysis quite well, which proves the validity of our cryptanalytic techniques.
A comparative analysis of the state-of-the-art lossless image compression techniques

Lossless data reduction is essential for data transmission over the Internet and the storage of data in a digital device when data loss is not permitted. The application of image compression is essential for image storing, image classification, and image recognition, and image compression techniques compress an image by reducing redundancy in the image. Many image compression standards have already been developed. This article compares the most popular state-of-the-art lossless image compression techniques, and the methods are evaluated based on the bits per pixel or compression ratio. Finally, we recommend which of the algorithms is better for a few different datasets.
Blockchain-based privacy approaches for 5G healthcare informatics

With recent advances in digitization, the Internet of Things (IoT), cloud computing, and blockchain, the healthcare industry is moving forward with efficiency, improved security, and privacy. Computational learning and artificial intelligence have given dispersed healthcare infrastructures a significant advantage as they had previously been resistant to technological advancements. Regrettably, in the healthcare industry, there are problems with protecting data privacy and integrity as a result of information exchange. Because of the absence of data security and privacy, the data might potentially be abused, creating a significant privacy breach. Blockchain technology currently offers an extensive array of use cases, including the administration of public healthcare, storing medical records, the creation of digital medical insurance claims, connecting patients via remote care, exchanging patient details for a nominal fee, user-friendly medical studies, tracking precision medicine, and medication forgery. One of the critical tasks is to extend the telecommunications network while connecting key stakeholders and medical institutions with 5G, all while maintaining patient privacy and confidentiality. The solutions aim to secure data in smart healthcare systems and prevent data counterfeiting by implementing blockchain in 5G networks. This chapter provides a brief description of the healthcare ecosystem in which 5G technology plays a prominent role and provides information on how 5G may assist with blockchain-based healthcare informatics. It also includes an exploration of privacy issues in healthcare informatics and the realm of blockchain-based healthcare research that utilizes 5G network connectivity.