Reviewerly/exHarmony huggingface dataset:
Capturing Dependencies Within Machine Learning via a Formal Process Model

The development of Machine Learning (ML) models is more than just a special case of software development (SD): ML models acquire properties and fulfill requirements even without direct human interaction in a seemingly uncontrollable manner. Nonetheless, the underlying processes can be described in a formal way. We define a comprehensive SD process model for ML that encompasses most tasks and artifacts described in the literature in a consistent way. In addition to the production of the necessary artifacts, we also focus on generating and validating fitting descriptions in the form of specifications. We stress the importance of further evolving the ML model throughout its life-cycle even after initial training and testing. Thus, we provide various interaction points with standard SD processes in which ML often is an encapsulated task. Further, our SD process model allows to formulate ML as a (meta-) optimization problem. If automated rigorously, it can be used to realize self-adaptive autonomous systems. Finally, our SD process model features a description of time that allows to reason about the progress within ML development processes. This might lead to further applications of formal methods within the field of ML.
Tracking Sentiment and Topic Dynamics from Social Media

We propose a dynamic joint sentiment-topic model (dJST) which allows the detection and tracking of views of current and recurrent interests and shifts in topic and sentiment. Both topic and sentiment dynamics are captured by assuming that the current sentiment-topic specific word distributions are generated according to the word distributions at previous epochs. We derive efficient online inference procedures to sequentially update the model with newly arrived data and show the effectiveness of our proposed model on the Mozilla add-on reviews crawled between 2007 and 2011.
Secure Two-Factor Authentication for IoT Device

The development of IoT has penetrated various sectors. The development of IoT devices continues to increase and is predicted to reach 75 billion by 2025. However, the development of IoT devices is not followed by security developments. Therefore, IoT devices can become gateways for cyber attacks, including brute force and sniffing attacks. Authentication mechanisms can be used to ward off attacks. However, the implementation of authentication mechanisms on IoT devices is challenging. IoT devices are dominated by constraint devices that have limited computing. Thus, conventional authentication mechanisms are not suitable for use. Two-factor authentication using RFID and fingerprint can be a solution in providing an authentication mechanism. Previous studies have proposed a two-factor authentication mechanism using RFID and fingerprint. However, previous research did not pay attention to message exchange security issues and did not provide mutual authentication. This research proposes a secure mutual authentication protocol using two-factor RFID and fingerprint using MQTT protocol. Two processes support the authentication process: the registration process and authentication. The proposed protocol is tested based on biometric security by measuring the false acceptance rate (FAR) and false rejection rate (FRR) on the fingerprint, measuring brute force attacks, and measuring sniffing attacks. The test results obtained the most optimal FAR and FRR at the 80% threshold. Then the equal error rate (ERR) on FAR and FRR is around 59.5%. Then, testing brute force and sniffing attacks found that the proposed protocol is resistant to both attacks.
Securing the Internet of Things – Security and Privacy in a Hyperconnected World

The Internet of Things (IoT) introduces itself as a basic set of technological enablers to support the provision of innovative applications that can improve the quality of life of people and industrial productivity. IoT is increasingly supported by various stakeholders and market players that see clear business opportunities in this field. Cities have also identified the potential of IoT both for providing smart services to their citizens and for boosting the local economy by providing opportunities for new jobs and new businesses. Industry is considering IoT’s adoption to drive Industry 4.0. All these are key reasons why IoT has attracted so much attention lately in both the research and the industrial world.
DHT: Deformable Hybrid Transformer for Aerial Image Segmentation

Due to the strong ability to model global information, the transformer-based methods have shown remarkable improvements in image segmentation tasks. However, the self-attention mechanism in the transformer is computationally expensive and relies on pre-trained parameters. Moreover, the transformer method is weak in modeling local information, which is unfavorable for accurately segmenting objects from high-resolution aerial images. To this end, an efficient deformable orientational self-attention (DoA) is proposed to simultaneously extract the global information and the local information. Besides, for parameter efficiency, we design a depthwise channel self-attention (DcA) to model the contextual information among channels. Combining with the DoA and DcA, we propose the deformable hybrid transformer (DHT) to perform high-quality object segmentation on aerial images. Experiments on ISPRS Potsdam dataset and WHU building dataset illustrate that the proposed DHT can not only achieve state-of-the-art (SOTA) results but also markedly reduce the dependence of the transformer on pre-trained parameters.
From Single-Protocol to Large-Scale Multi-Protocol Quantum Networks

In the initial stage of the quantum Internet, most of the practical quantum networks have been deployed relying on diverse individual quantum key distribution (QKD) protocols. However, this single-protocol paradigm cannot fulfill the heterogeneous requirements of the users, hence market-penetration of quantum networks is limited. Given the recent advances in QKD protocols, the next generation quantum networks are expected to support both legacy and emerging QKD protocols. However, the evolution from single-protocol to multi-protocol quantum networks poses numerous challenging problems. As a remedy, we conceive a practical protocol translation framework for supporting this migration from single-protocol to large-scale multi-protocol quantum networks. Furthermore, we propose a programmable multi-functional relay node architecture for harmonizing the components of a suitable relay node. A pair of protocol translation policies are presented for meeting the challenging security requirements of migration requests, which are compared to the single-protocol-based migration solutions both in terms of the translation success probability attained (i.e., the proportion of migration requests with successful protocol translation), and the average relaying risk probability encountered (i.e., the mean of the relaying risk probabilities of end-to-end key negotiation in a quantum network). Finally, we highlight a suite of open problems in both quantum secure direct communication and quantum teleportation for research into future multi-protocol quantum networks.
DISTANCE LEARNING AS THE NEW REALITY IN TERTIARY EDUCATION: A CASE STUDY

The year 2020 was a horrible shock for both educators and students worldwide because of the COVID-19 disease. The whole educational process had to undergo cardinal changes in that it was forced to transition from face-to-face to online mode. Distance learning (DL) became the new reality in no time. This unusual situation prompted us to conduct research on what challenges it meant for tutors to cope with the new requirements. The main goal of our case study was to get insights into the altered daily routines of tutors and understand how the system functioned. Another objective of our survey was to provide a brief synthesis of distance learning, based on the academic literature. We have applied the qualitative research design, using a semi-structured interview as a research instrument for collecting data from the respondents. The research sample consisted of thirty-four college tutors teaching language and literature (English, German, Ukrainian, and Hungarian). The participants were requested to reply to eleven questions either online or face-to-face. The results proved how unexpected and difficult it was to move from the classroom to distance education. They emphasized how crucial digital literacy is for both instructors and students. One of the essential implications is that teachers must develop their digital skills in every possible way. The next stage of our research will be to investigate what impact DL has on teachers' and students' physical and psychological well-being.
Social Media Analysis: A Tool for Popularity Prediction Using Machine Learning Classifiers

The main aim of our paper is to predict the popularity of a person using ML classifiers. We will be able to predict whether the person is popular or not by analyzing the comments about that person from his/her social media platform, such as Twitter. The task is performed out of NLTK (Natural Language Toolkit) library, Python using pandas function, NumPy, and some concepts of machine learning. The analytics was done using NLP (natural language processing) as the predictions become easier with the help of it. The methodology proposed by us is to initially clean the data, using data mining approach, take out the useful information, and make the dataset ready. After which, the prepared dataset will be fed into the classifier, and we'll fit the classifier accordingly. Now, the cleaned testing data will be used to get the desired outcome.
The Trends in Cybersecurity Maturity Models

Requirements in cyber security for organizations from critical infrastructures or specific domains are not a novelty. Industrialized and developed countries enforce strict cyber security controls for institutions from financial, healthcare, or defense domains. Theoretical or practical models that support the evaluation process for cyber security stance, as well as compliance with national or organizational controls, are becoming more common. The use of models can be driven by a number of factors, ranging from national regulations, economic benefits, up to the efficiency and standardizations aspects. From a practical standpoint it is much more efficient to use a publicly validated model developed rather that develop individually a new model. In this paper we review current models that are used for evaluating cybersecurity controls in organizations that are part of the critical infrastructure domain. Via a comparative analysis we identify the peculiarity of publicly available models. We compare the results against a model that we have proposed for assessing cybersecurity maturity in critical infrastructures. We highlight the differences and common factors between these models, as well as identify current trends in the development of such models.
Forecasting for Chaotic Time Series Based on GRP-lstmGAN Model: Application to Temperature Series of Rotary Kiln

Rotary kiln temperature forecasting plays a significant part of the automatic control of the sintering process. However, accurate forecasts are difficult owing to the complex nonlinear characteristics of rotary kiln temperature time series. With the development of chaos theory, the prediction accuracy is improved by analyzing the essential characteristics of time series. However, the existing prediction methods of chaotic time series cannot fully consider the local and global characteristics of time series at the same time. Therefore, in this study, the global recurrence plot (GRP)-based generative adversarial network (GAN) and the long short-term memory (LSTM) combination method, named GRP-lstmGAN, are proposed, which can effectively display important information about time scales. First, the data is subjected to a series of pre-processing operations, including data smoothing. Then, transforming one-dimensional time series into two-dimensional images by GRP makes full use of the global and local information of time series. Finally, the combination of LSTM and improves GAN models for temperature time series prediction. The experimental results show that our model is better than comparison models.
Applications of Blockchain Technology in the COVID-19 Era

On March 11, 2020 the WHO declared a globalGlobal pandemicPandemic state and the virus is currently accountable for millions of infectionsInfection and hundreds of thousands of deceases. The pandemicPandemic has aggravated the crisisCrisis of confidence in many health organizations and government institutions, and has generated a number of needs. New technologiesTechnology can be used to meet these needs and mitigate some of the problems associated with the pandemicPandemic. BlockchainBlockchain technologyTechnology (BCT) is characterized by being decentralizedDecentralized, safe, independent of trusted third partiesThird party and maintaining the privacyPrivacy of the informationInformation it storesStore, which has augmented its interest in times of pandemicPandemic. From an epidemiological point of view, it can help to rapidly detect the sources of infectionInfection, processing the informationInformation by individualsIndividual, institutions and governments. In addition, it permits dataData to be collected efficiently by ensuring its origin, storing the informationInformation immutably and making an auditable and traceableTraceable recordRecord over time of any modifications to the dataData. Furthermore, it is ideal for facilitating the traceabilityTraceability of supply chainsSupply chain, calculating inventoryInventory, and being able to establish an appropriate balance between suppliers and consumers. On the other hand, BCT could ameliorate the whole processProcess of data processing necessary in scientific researchResearch, since it generates a distributed ledgerDistributed ledger that guarantees the immutability, transparencyTransparency and traceabilityTraceability of the dataData even between two or more distant parties with no mutual trustTrust. New working models, accessAccess to intelligent contracts and financingFinancing circuits, can also be ameliorated with BCT, helping to reduce mistakes and diminish fraudFraud.
<scp>FaithDial</scp>: A Faithful Benchmark for Information-Seeking Dialogue

Abstract The goal of information-seeking dialogue is to respond to seeker queries with natural language utterances that are grounded on knowledge sources. However, dialogue systems often produce unsupported utterances, a phenomenon known as hallucination. To mitigate this behavior, we adopt a data-centric solution and create FaithDial, a new benchmark for hallucination-free dialogues, by editing hallucinated responses in the Wizard of Wikipedia (WoW) benchmark. We observe that FaithDial is more faithful than WoW while also maintaining engaging conversations. We show that FaithDial can serve as training signal for: i) a hallucination critic, which discriminates whether an utterance is faithful or not, and boosts the performance by 12.8 F1 score on the BEGIN benchmark compared to existing datasets for dialogue coherence; ii) high-quality dialogue generation. We benchmark a series of state-of-the-art models and propose an auxiliary contrastive objective that achieves the highest level of faithfulness and abstractiveness based on several automated metrics. Further, we find that the benefits of FaithDial generalize to zero-shot transfer on other datasets, such as CMU-Dog and TopicalChat. Finally, human evaluation reveals that responses generated by models trained on FaithDial are perceived as more interpretable, cooperative, and engaging.
Graph Learning-Based Blockchain Phishing Account Detection with a Heterogeneous Transaction Graph

Recently, cybercrimes that exploit the anonymity of blockchain are increasing. They steal blockchain users' assets, threaten the network's reliability, and destabilize the blockchain network. Therefore, it is necessary to detect blockchain cybercriminal accounts to protect users' assets and sustain the blockchain ecosystem. Many studies have been conducted to detect cybercriminal accounts in the blockchain network. They represented blockchain transaction records as homogeneous transaction graphs that have a multi-edge. They also adopted graph learning algorithms to analyze transaction graphs. However, most graph learning algorithms are not efficient in multi-edge graphs, and homogeneous graphs ignore the heterogeneity of the blockchain network. In this paper, we propose a novel heterogeneous graph structure called an account-transaction graph, ATGraph. ATGraph represents a multi-edge as single edges by considering transactions as nodes. It allows graph learning more efficiently by eliminating multi-edges. Moreover, we compare the performance of ATGraph with homogeneous transaction graphs in various graph learning algorithms. The experimental results demonstrate that the detection performance using ATGraph as input outperforms that using homogeneous graphs as the input by up to 0.2 AUROC.
ODIN112–AI-Assisted Emergency Services in Romania

The evolution of Natural Language Processing technologies transformed them into viable choices for various accessibility features and for facilitating interactions between humans and computers. A subset of them consists of speech processing systems, such as Automatic Speech Recognition, which became more accurate and more popular as a result. In this article, we introduce an architecture built around various speech processing systems to enhance Romanian emergency services. Our system is designed to help the operator evaluate various situations with the end goal of reducing the response times of emergency services. We also release the largest high-quality speech dataset of more than 150 h for Romanian. Our architecture includes an Automatic Speech Recognition model to transcribe calls automatically and augment the operator’s notes, as well as a Speech Recognition model to classify the caller’s emotions. We achieve state-of-the-art results on both tasks, while our demonstrator is designed to be integrated with the Romanian emergency system.
Image Geo-Site Estimation Using Convolutional Auto-Encoder and Multi-Label Support Vector Machine

The estimation of an image geo-site solely based on its contents is a promising task. Compelling image labelling relies heavily on contextual information, which is not as simple as recognizing a single object in an image. An Auto-Encode-based support vector machine approach is proposed in this work to estimate the image geo-site to address the issue of misclassifying the estimations. The proposed method for geo-site estimation is conducted using a dataset consisting of 125 classes of various images captured within 125 countries. The proposed work uses a convolutional Auto-Encode for training and dimensionality reduction. After that, the acquired preprocessed input dataset is further processed by a multi-label support vector machine. The performance assessment of the proposed approach has been accomplished using accuracy, sensitivity, specificity, and F1-score as evaluation parameters. Eventually, the proposed approach for image geo-site estimation presented in this article outperforms Auto-Encode-based K-Nearest Neighbor and Auto-Encode-Random Forest methods.
Location-Guided Token Pair Tagger for Joint Biomedical Entity and Relation Extraction

Automatic extraction of biomedical relations is important for many tasks, such as drug discovery, protein prediction. However, since the biomedical corpus contains a large number of complex long sentences and overlapping triples, most general domain joint modeling methods are unapplicable to the biomedical domain. Aiming at the problem of overlapping triples in the biomedical field, we design a token pair tagger to achieve single-stage joint biomedical entity and relation extraction. In addition, in order to make the tagger better understand the semantic information and structural information, we design both global-aware module and distance-aware module to introduce location information. Compared with previous methods, our model makes better use of location information, enhances the understanding of complex long sentences, and improves the ability to extract overlapping triples. We evaluated our model on the CHEMPROT and DDIExtraction2013 datasets. Experimental results show that our model significantly outperforms a range of baseline models, achieving the state-of-the-art performance.
Generating textual emergency plans for unconventional emergencies — A natural language processing approach

An emergency plan is an emergency administrative document that specifies the course of actions taken to minimize the effects of a crisis or incident. Establishing high-quality emergency plans has been a fundamental task for various emergency administrative agencies. Traditionally, emergency plans are developed based on the experiences of handling past emergencies, thus may not be well applied to unconventional emergencies that arise in an unrepeatable and unpredictable manner. This work proposes a novel emergency plan generation approach to assist decision-making under unconventional emergent situations. This goal is achieved by leveraging deep-learning-based natural language techniques to explore the interrelationship between existing emergency plans developed for common emergencies and the target unconventional emergency. In particular, an emergency response knowledge base is constructed based on a large number of existing emergency plans, and the relevant part with respect to the target unconventional emergency is retrieved. Then the new emergency plan is formed by organizing the relevant knowledge guided by a pre-defined emergency plan template. Furthermore, a novel emergency plan evaluation approach is proposed to perform a comprehensive evaluation of the quality of generated emergency plans. Empirical results on a real-world unconventional emergency case verify the feasibility of our emergency plan generation approach.
The Integration of Augmented Reality (AR) in Education

New teaching techniques have been adopted as a result of the emergence of innovative technological tools in the field of Augmented Reality (AR), as investigated in several articles to date (Hsu 2017; Forsythe &amp; Raine 2019). The availability of AR applications, both vision, and location-based, allows students today to learn foreign languages in a more contextualized and immersive manner, thanks to the use of smartphones and other electronic devices. Thus, this study seeks to analyze and review the recent trends in AR implementation in education and to present the main projects aimed at teaching with AR-based projects that have been published in the latest years in WOS and Scopus.
Short-Term Load Forecasting Based on Spiking Neural P Systems

Short-term load forecasting is a significant component of safe and stable operations and economical and reliable dispatching of power grids. Precise load forecasting can help to formulate reasonable and effective coordination plans and implementation strategies. Inspired by the spiking mechanism of neurons, a nonlinear spiking neural P (NSNP) system, a parallel computing model, was proposed. On the basis of SNP systems, this study exploits a fresh short-term load forecasting model, termed as the LF-NSNP model. The LF-NSNP model is essentially a recurrent-like model, which can effectively capture the correlation between the temporal features of the electric load sequence. In an effort to validate the effectiveness and superiority of the proposed LF-NSNP model in short-term load forecasting tasks, tests were conducted on datasets of different time and different variable types, and the predictive competence of various baseline models was compared.