Reviewerly/exHarmony huggingface dataset:
HistoTrust: Ethereum-Based Attestation of a Data History Built with OP-TEE and TPM

Device- or user-centric system architectures allow everyone to manage their personal or confidential data. But how to provide the trust required between the stakeholders of a given ecosystem to work together, each preserving their interest and their business? HistoTrust introduces a solution to this problem. A system architecture separating the data belonging to each stakeholder and the cryptographic proofs (attestations) on their history is implemented. An Ethereum ledger is deployed to maintain the history of the attestations, thus guaranteeing their tamper-resistance, their timestamp and their order. The ledger allows these attestations to be shared between the stakeholders in order to create trust without revealing secret or critical data. In each IoT device, the root-of-trust secrets used to attest the data produced are protected at storage in a TPM ST33 and during execution within an ARM Cortex-A7 TrustZone. The designed solution aims to be resilient, robust to software attacks and to present a high level of protection against side-channel attacks and fault injections. Furthermore, the real-time constraints of an embedded industrial application are respected. The integration of the security measures does not impact the performance in use.
SingTRACeX: Navigation System to Address Wandering Behavior for Elders and Their Caregivers

The issue of an ever-increasing ageing population has been the increasing burden on caregivers to care for the elderly population. Caring for elders, especially those diagnosed with dementia, can be challenging. People living with dementia (PWD) require extra care and attention from the caregivers due to the associated behaviours that come with dementia. Wandering is a frequent behaviour exhibited by PWD, which can bring about negative outcomes on the PWD as well as increasing the stress of the caregivers. Though many technological solutions exist, they are not widely deployed. This paper introduces a technological framework, bridging the localisation technologies to the needs of elders and caregivers. The aim is to minimise or eliminate the negative outcomes of dementia wandering and to reduce the burden and stress on the caregivers, thus improving overall well-being. In this paper, we study the application, SingTRACeX, features by considering user needs from the field study with 2 focus group discussions (FGD), comprising of 14 professional caregivers and coordinators. The proposed system features Real-time Location Tracking and Indoor Localisation. The location is determined by GPS location from the Sensor module when outdoors, and estimation using data from the WiFi module, and Bluetooth module when indoors. The indoor navigation provided by the Indoor Localisation module uses an A-star search algorithm. This paper could serve as a foundation that can be built upon over time as the needs of elders and caregivers may change over time, as well as the evolution of technology that may bring about new methods to address needs.
Matching Efficiency of Admission Procedures in Online-Education

This paper empirically evaluates the relationship between the university admission process and the study success of online students. We investigate data from fully accredited higher education courses over multiple cohorts, yielding a sample of 502 students from both Bachelor- and Master-studies. We link rankings from 3 test procedures, an interview, a resume analysis, and an aptitude test, with grade achievements and successful course completion. We find that all procedures predict grades, but only resume analysis has a robust relationship with successful course completion. Crucially, the predictive power of the resume analysis depends on controlling for a student’s age. Our results thus call for nuance in evaluating and interpreting admission scores when allocating seats in online education.
On the Use of Explainable Artificial Intelligence for the Differential Diagnosis of Pigmented Skin Lesions

In the last few years, eXplainable Artificial Intelligence (XAI) has been attracting attention in data analytics, as it shows great potential in interpreting the results of complex machine learning models in the application of medical problems. The nutshell is that the outcome of the machine learning-based applications should be understood by end users, specially in medical data context where decisions have to be carefully taken. As such, many efforts have been carried out to explain the outcome of a deep learning complex model in processes where image recognition and classification are involved, as in the case of Melanoma cancer. This paper represents a first attempt (to the best of our knowledge) to experimentally and technically investigate the explainability of modern XAI methods Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive exPlanations (SHAP), in terms of reproducibility of results and execution time on a Melanoma image classification data set. This paper shows that XAI methods provide advantages on model result interpretation in Melanoma image classification. Concretely, LIME performs better than SHAP gradient explainer in terms of reproducibility and execution time.
Selection of Keypoints in 2D Images Using F-Transform

We focus on a new fast and robust algorithm for selecting keypoints in 2D images using the following techniques: image regularization, selection of spaces with closeness, and design of the corresponding graph Laplacians. Then, the representative keypoints are local extrema in the image after the Laplacian operator is applied. The convolution kernels, used for regularization, are extracted from the uniform partition of the image domain, and the graph Laplacian is constructed using the theory of $$F^0$$ -transforms. Empirically, we show that sequences of F-transform kernels that correspond to different regularization levels share the property that they do not introduce new local extrema into the image under convolution. This justifies the computation of keypoints as points where local extrema are reached and allows them to be classified according to the values of the local extrema. We show that the extracted key points are representative in the sense that they allow a good approximate reconstruction of the original image from the calculated components of the F-transform taken from different convolutions. In addition, we show that the proposed algorithm is resistant to Gaussian noise.
A New Model SATV for Sentiment Analysis of Hinglish Sentences

With the increased use of the Internet and Social media platforms. It becomes very easy to know the opinion and thoughts of people through social media. Users may share their personal feeling in their local language. These tweets may be used to analyze the sentiments of people through Sentiment Analysis. In India, most of the people use Hinglish (Hindi words written in English) or Indian English language to share their feeling. So, in this paper, we have proposed a new model named SATV (Sentiment Analyzer Translating Vader) for Sentiment Analysis of Hinglish Language sentences. This model has been implemented and tested on Delhi University tweets taken from Twitter to examine the flow and working of the SATV model. Linear SVC and Naive Bayes Classifications have been applied for calculating the accuracy. Also, Precision, Recall, and F-measure have been calculated using a confusion matrix. Lastly, a comparison of the SATV model has been done with Vader Technique.
Human-Level Melodic Line Harmonization

This paper examines potential applicability and efficacy of Artificial Intelligence (AI) methods in automatic music generation. Specifically, we propose an Evolutionary Algorithm (EA) capable of constructing melodic line harmonization with given harmonic functions, based on the rules of music composing which are applied in the fitness function. It is expected that harmonizations constructed in accordance to these rules would be formally correct in terms of music theory and, additionally, would follow less-formalised aesthetic requirements and expectations. The fitness function is composed of several modules, with each module consisting of smaller parts. This design allows for its flexible modification and extension. The way the fitness function is constructed and tuned towards better quality harmonizations is discussed in the context of music theory and technical EA implementation. In particular, we show how could generated harmonizations be modelled by means of adjusting the relevance of particular fitness function components. The proposed method generates solutions which are technically correct (i.e. in line with music harmonization theory) and also “nice to listen to” (i.e. aesthetically plausible) as assessed by an expert - a harmony teacher.
DVT: Application of Deep Visual Transformer in Cervical Cell Image Classification

Cervical cancer is a very common cancer among women. Cytopathologists use a microscope to examine cell slides collected from the patient’s cervix to determine if it is cancerous. However, manually checking the slides to diagnose cancer is a very difficult task, not only time-consuming but also error-prone. A computer-aided diagnosis system can automatically and accurately screen cervical cell images. In this study, we propose a framework called DVT to perform classification tasks. We use SIPaKMeD and CRIC datasets together. On 11-class classification tasks, DVT achieves an accuracy of 87.35%. In the comparative experiment, DVT performs better than the CNN and VT models alone.
Functional Programming

In this chapter, we explore the programming paradigm called functional programming and how it relates to R. There are many definitions of what it means for a language to be a functional programming language, and there have been many language wars over whether any given feature is “pure” or not. I won’t go into such discussions, but some features, I think everyone would agree, are needed. You should be able to define higher-order functions, you should be able to create closures, and you probably want anonymous functions as well.
An Improved Crow Search Algorithm with Grey Wolf Optimizer for High-Dimensional Optimization Problems

Crow search algorithm (CSA) mainly solves optimization problems. In high-dimensional optimization problems, CSA searches with moves toward the wrong crows’ hiding position. Solving the problems of the CSA algorithm, this paper proposes an improved CSA with Grey Wolf Optimization (GWO) algorithms is called ICSAGWO for manipulating the high-dimensional optimization problem. The main idea is to hybrid both algorithms’ strengths that utilize the efficient exploitation ability of CSA with good performance in the exploration ability and convergence speed of GWO. By hybridizing, the authors employ an adaptive inertia weight to control exploitation and exploration capacities. ICSAGWO algorithm is tested on twenty-three benchmark functions with 30 to 500 dimensions and compared among other algorithms, such as GSA, WOA, GWO, CSA, etc. Experimental results of the proposed algorithm ICSAGWO obtain high performance in both unimodal and multimodal and not affecting the search performance even in high dimension data over other algorithms.
OntoQC: An Ontology-Infused Machine Learning Scheme for Question Classification

The fundamental element for question answering is question classification. Though many papers have been presented on the following topic, this paper puts forth a powerful method for the process of question classification. The purpose of this paper is to improve the accuracy to the maximum extent for the classification of the question. In this paper, the experimentations are conducted on the Quora dataset which is preprocessed and then formalized for achieving ontology matching and mapping using concept similarity. The ontology matching is achieved using SemantoSim measure under ant colony optimization for deriving the optimal entities from a set of feasible solutions. The feature selection is achieved by encompassment of question category ontology. The ontology used in this paper is domain specific. Semantic labeling with the support of LOD cloud is realized to enhance the feature selection. The XGBoost classifier is encompassed for achieving classification of questions. An overall accuracy of 95.53% and the average precision is 96.9% has been yielded by the proposed OntoQC model.
PERACTIV: Personalized Activity Monitoring - Ask My Hands

Medication adherence is a major problem in the healthcare industry: it has a major impact on an individual’s health and is a major expense on the healthcare system. We note that much of human activity involves using our hands, often in conjunction with objects. Camera-based wearables for tracking human activities have sparked a lot of attention in the past few years. These technologies have the potential to track human behavior anytime, any place. This paper proposes a paradigm for medication adherence employing innovative wrist-worn camera technology. We discuss how the device was built, various experiments to demonstrate feasibility and how the device could be deployed to detect the micro-activities involved in pill taking so as to ensure medication adherence.
Multi-contextual Recommender Using 3D Latent Factor Models and Online Tensor Decomposition

Traditional approaches to recommendation systems involve using collaborative filtering and content-based techniques which make use of the similarities between users and items respectively. Such approaches evolved to encapsulate model-based latent factor (LF) algorithms that use matrix decomposition to ingest a user-item matrix of ratings to generate recommendations. In this paper, we propose a novel approach based on 3D LF model and tensor decomposition method for devising personalized recommendations driven from additional contextual features. We also present our stacking method for a tensor generation prior to incorporating LF models. We validate our proposed personalized recommender using two real-world datasets. Our experimental results showed that additional contextual features can help to personalize recommendations while maintaining similar or better performance compared to conventional 2D LF methods. Furthermore, our results demonstrate the importance of the quality of the contextual features to be used in 3D LF models. In addition, our experiments show the effective performance of our new stacking method on computation time and accuracy of the proposed recommender.
Anonymous Trusted Data Relocation for TEEs

Trusted Execution Environment (TEE) technology like ARM TrustZone allows protecting confidential data using cryptographic keys that are bound to a specific TEE and device. However, there are good reasons to allow relocating such data from a TEE to another TEE in another device, often in a non-interactive (offline) and anonymous manner. We propose the Trusted Relocation Extension (TRX), a TrustZone-based trusted storage service enabling backup/recovery and sharing of data between TEEs in different devices. TRX works offline, without previous key exchange, and ensures the anonymity of the sender and the receiver. We present an implementation of TRX compatible with OP-TEE and its evaluation with Raspberry Pi 3 B+ devices.
Webometric Network Analysis of Cybersecurity Cooperation

Cyberspace is slowly but surely evolving into the battlefield of the future. The rise of computational power allows not only for state-sponsored hacking groups, but for lone hackers or organizations to wreak havoc on infrastructure, either for financial or political gain. Cooperation between states, on multiple levels, is required to defend their cyber sovereignty effectively. In this paper, we will identify Cyber Emergency Response Team (CΕRT) and Cyber Security Incident Response Team (CSIRT) which are under national or governmental supervision, and we will draw comparisons between the European Union and NATO. Furthermore, we will examine whether the cooperation between European Union Member States on the aforementioned technical level is reflected on the World Wide Web. Inter-linkage and co-mention analyses have been conducted to map the organizations’ footprint on Cyberspace, using Webometric Analyst.
Analysis of Biases in Calibrated Recommendations

While recommender systems have mainly focused on the effectiveness of their results, beyond-accuracy perspectives have been recently explored. One of the most prominent is algorithmic bias, which analyzes if existing imbalances in the input data are exacerbated in the produced recommendations. On the other hand, calibrated recommendations ensure that the recommendations reflect the distribution of the original preferences of each user (e.g., in terms of item genres). In this paper, we connect these two perspectives, to analyze how the original calibration method deals with the bias in the state-of-the-art recommendation models. Our analysis on real-world data shows that the calibration effectiveness is impacted by how a recommendation model handles bias.
Virtual Try-On Using Augmented Reality

Augmented reality (AR) applications put on another aspect to the customer event by overlaying the users face or factors with virtual items. Virtual 3D take a stab that can give an instinctive and reasonable view for web based shopping and has enormous possible business esteem. The existing 3D virtual take a glance at strategies mostly depend on previous 3D human shapes and piece of clothing formats, which prevents their applications in common sense situations. With this paper we analyse the application non-linear presence and saw the type can clarify positive convincing reactions in direction of AR applications. VTO applications can likewise offer social benefit, permitting purchasers to look for their companions assessments by sharing how an outfit looks on the tweaked model. Despite its utilitarian and liberal qualities, utilizing VTO innovation isn't hazard.
Comparative Analysis of Automatic Poetry Generation Systems Based on Different Recurrent Neural Networks

The work concerning with natural language processing covers a wide range of topics, including machine translation, sentiment analysis, and automatic text generation. Among them, automatic text generation is very important for specific projects such as automatic summarization and automatic question and answer. In addition, different from the general automatic text generation work, poems have certain requirements in semantic expression and syllable coordination. Completing this work can further improve the semantic expression ability of automatic text generation projects and render the generated text an aesthetic feeling. This article wants to explore the effect of various recurrent neural network structures, such as basic recurrent network (RNN), long short-term memory (LSTM), gated recurrent units (GRU), in handling this work. Within the model, a word embedding layer, a recurrent neural network layer and a fully connected layer are used. Comparing the loss value decline curve and the generated verses under the same data set, loss function and parameter update method finally came to the conclusion: in the training process, the models with LSTM and GRU as the core module can effectively reduce the loss value and generate verses with rhyme satisfying the poetry requirement, and the semantics is basically reasonable; as for the model with RNN as the core module, the loss value fluctuates, and the generated verses elementarily meet the standard of rhyme and semantics.
Cloud-Aided Scalable Revocable Identity-Based Encryption with Ciphertext Update from Lattices

Cloud-aided scalable revocable identity-based encryption with ciphertext update (CA-RIBE-CU), introduced by Wang et al. in 2017 and revocable identity-based encryption with server-aided ciphertext evolution (RIBE-CE), proposed by Sun et al. in 2020, both offer significant advantages over previous identity revocation mechanisms when considering the scenario of a secure data sharing in the cloud setting. In these two primitives, the user (i.e., receiver) can utilize the short-term decryption keys to decrypt all encrypted data (i.e., ciphertexts) sent to him or her, meanwhile, the ciphertexts stored in the cloud will update to new ones with the aided of the cloud service provider (CSP) and without any interaction with data owners, and the old ones are completely deleted, and thus, the revoked users cannot access to both the previously and subsequently shared data, i.e., to achieve both identity revocation and ciphertext update simultaneously for IBE. In this paper, inspired by Wang et al.’s work and the current first constructions of lattice-based RIBE-CE by Zhang et al. in Inscrypt 2021, we propose the first lattice-based CA-RIBE-CU scheme. Our scheme is more efficient and secure than existing constructions of lattice-based RIBE. At the heart of our new design are two tools called “hybrid ciphertexts" and “hybrid short-term decryption keys" that enables constant ciphertexts and simplified ciphertexts update, not linear in the length of identities and without burdensome double encryption mechanism, which serves as one startlingly solution to the challenges proposed by Zhang et al., and based on the hardness of learning with errors (LWE) problem, we prove that our scheme is selectively secure in the standard model.
Software and Systems

The monitoring and control system (MCS) is the heart of a control center. In this chapter, we focus on the description of this system, since other important software (SW) systems of a control center are described in other chapters of this book. After clarifying some basic MCS terms, the data stream exchanged between the spacecraft and the MCS is explained in more detail. Using the German Space Operations Center (GSOC) as an example, it is shown which SW modules make up the MCS and which SW modules can usefully supplement the operations. An outlook on SW development and maintenance within a control center concludes the chapter.
MultiPosits: Universal Coding of $$\mathbb {R}^n$$

Recently proposed real-number representations like Posits and Elias codes provide attractive alternatives to IEEE floating point for representing real numbers in science and engineering applications. Many of these applications represent fields on structured grids that exhibit smoothness, where adjacent scalar values are similar and often accessed together in stencil or vector computations. This similarity results in redundancy in representation, where several leading bits in the representation of adjacent values are shared. We propose a generalization of scalar “universal codes” to small, multidimensional blocks of values that exploit their similarity and underlying dimensionality. Drawing upon ideas from multimedia and floating-point compression, our approach combines a decorrelating transform with adaptive, error-optimal interleaving of coefficient bits, which allows increasing accuracy per bit stored by orders of magnitude. Our solution accommodates both a fixed-length representation of blocks—facilitating random access—and variable-length storage to within a user-prescribed tolerance—e.g., for I/O, communication, and streaming computations. Our approach generalizes universal coding of the reals to vectors and tensors, and is straightforward to implement for several known number systems by extending a previously published framework for universal coding based on simple refinement rules.