Reviewerly/exHarmony huggingface dataset:
Exploiting Pragmatic Markers in Radio Advertising in Ireland

Advertisements attempt to replicate ‘normal everyday communication’ through the exploitation of language variation. More specifically, they copy how pronouns, vocatives and pragmatic markers are used in media discourse ‘to create an illusion of an interpersonal relationship between strangers by drawing on linguistic features that are normally used in casual conversation between real friends and intimates’. Focusing on pragmatic markers in the Irish context, Kallen identifies a number of words and phrases functioning as PMs, which are used in literature to index a character as Irish, and examines the distribution of specific PMs in the Ireland component of the International Corpus of English (ICE-Ireland) corpus and describes the basis on which various markers are representative of Irish English (e.g., derivation from the Irish language, frequency of occurrence in this variety, etc.). Bringing together the contexts of Irish English and intimate discourse, Clancy finds that a number of PMs are key in intimate discourse in the Irish context. These items are also notably more frequent in the intimate context types of the Limerick Corpus of Irish English (L-CIE) by comparison with the British National Corpus (BNC). This study investigates how and to what extent PMs are employed in radio advertising in the Irish context through analysing a corpus of advertisements broadcast on an Irish radio channel between 1977 and 2017. PMs are examined in terms of their frequency and functions in the distinct components of the ad, the ‘Action’ (comprised of context-based dialogic interaction, designed to imitate discourses of ‘everyday informal interaction’) and ‘Comment’ (commonly monologic, decontextualised and associated with the slogan or voice of authority). Through comparisons with ICE Ireland and L-CIE, the extent to which PMs in the ad components can: (1) simulate ‘everyday’ casual conversation to create solidarity with the audience; (2) create a pseudo-intimate relationship with the audience; and (3) serve an indexical function through their association with Irish English, is investigated.
Vigilance towards the use of artificial intelligence applications for breast cancer screening and early diagnosis

Breast cancer is a real public health problem in Morocco. It is the cause of a significant number of deaths caused by late diagnosis. Mammography plays an essential role in the detection of breast cancer and in the early management of its treatment. Despite the existence of screening programs, there are still high rates of false positives and false negatives. Indeed, women were called back for additional diagnoses based on suspicious results that eventually led to cancer. Artificial intelligence (AI) algorithms represent a promising solution to improve the accuracy of digital mammography offering, on the one hand, the possibility of better cancer detection, and, on the other hand, improved efficiency for radiologists for good decision-making. In this work, through a review of the literature on the tools used to evaluate the performance of AI systems dedicated to early detection and diagnosis of breast cancer. We set out to answer the following questions: Is the ethics relating to patient data during the development phase of this software is respected? Do these tools take into consideration the specificities of the field? What about the specification, accuracy and limitations of these applications? At the end, we show through this work recommendations to adapt these evaluation tools of AI applications for breast cancer screening for an optimized and rational consideration of the principle of health vigilance and compliance with the regulatory standards in force governing this field.
A Context-Aware Service Discovery Framework Based on Mobile Agent

Service discovery approaches, which reduce the cost of discovering services while also providing ease to users, are gaining popularity. Middleware for context-aware service discovery is necessary for more dynamic and useful service discovery. This study presents a new mobile agent-based paradigm for context-aware service discovery. We characterize the concept 'context' in two ways: one from the perspective of service requesters, such as devices, locations, preferences, and activities; and the other from the perspective of Web services, like protocols for service binding, networks, devices and platforms for service execution, and so on.
Design and implementation of a food safety traceability platform based on blockchain and Internet of Things

Aiming at the problems of untrusted food traceability information and people's insecure sense of food safety, blockchain and Internet of Things technologies are used to build an efficient food safety traceability platform. First, the blockchain technology is used to realize the problem of database decentralization and solve the problem of untrusted data; then combined with the Internet of Things RFID and other technologies to solve the problem of automatic and timely upload of traceable data, which realizes the real-time collection and intelligence of source data Judgment improves the credibility and work efficiency of food traceability.
Comparing minds and machines: implications for financial stability

Is human or artificial intelligence more conducive to a stable financial system? To answer this question, we compare human and artificial intelligence with respect to several facets of their decision-making behaviour. On that basis, we characterise possibilities and challenges in designing partnerships that combine the strengths of both minds and machines. Leveraging on those insights, we explain how the differences in human and artificial intelligence have driven the usage of new techniques in financial markets, regulation, supervision, and policy making and discuss their potential impact on financial stability. Finally, we describe how effective mind-machine partnerships might be able to reduce systemic risks.
Address behaviour vulnerabilities in the next generation of autonomous robots

As service and industrial robots enter our lives, new types of cybersecurity issues emerge that involve the manipulation of a robot’s behaviour. Now is the time to develop countermeasures.
An Innovative Blockchain Based Application of the Extended Triple Diffie-Hellman Protocol for IoT

The massive use of the Internet with the increase in the number of IoT devices in the last decade is leading to explore new frontiers to ensure secure communication between people and devices. Existing solutions have severe limitations in terms of security and resistance to distributed attacks, and a Blockchain-based solution has not yet been explored. The Extended Triple Diffie-Hellman (X3DH) protocol has been used for years but it is typically based on a single trust third-party server that represents a single point of failure (SPoF) being consequently exposed to well-known Distributed Denial of Service (DDOS) attacks. The BlockChain-Based X3DH (BCB-X3DH) protocol has already been proposed to eliminate such a risk, combining the well-known X3DH security mechanisms with the intrinsic features of data non-repudiation and immutability that are typical of Smart Contracts. In this paper, we have progressed our research further to implement this protocol in order to fit Edge and IoT scenarios including low-power embedded devices with limited hardware capabilities with the purpose to guarantee complete on-chain secure communication management, even optimizing battery life-cycle at the same time.
An Analytical Study of Efficient CNNs Tuning and Scaling for Traffic Signs Recognition

Deep learning-based traffic sign recognition has been a very active area of research in autonomous driving since the appearance of Convolutional Neural Networks (CNN) as a substitute for classical machine learning algorithms. However, a good traffic sign recognition system (TSR) should inclusively fulfill accuracy, and response time compromise to be palatable in self-driving applications. Besides, the considerable computational load remains a burden to the adaptation and the design of CNN architectures for real-time applications. This paper aims to investigate the relationship between accuracy, efficiency, and computational complexity for the classification of traffic signs. MobileNetV2 and EfficientNet architectures were evaluated as they are specifically designed to be computationally efficient. When most of the contributed work in the literature focuses on accuracy, we rather focus on the choice of the most efficient model (best accuracy/model complexity ratio). The results support the intuitive idea that performance remains proportional to network size up to a given level beyond which it saturates.
Personalized Recommendation Method of Tourist Route based on User Collaborative Filtering Model

China has become the world's third largest inbound tourism host country and outbound tourism consumer country, and is forming the largest international tourism market. Tourism plays an increasingly important role in economic growth and employment. The recommender system can automatically filter out the personalized information in line with the user's interests. At the same time, it can dynamically adjust the recommended items according to the changes of user behavior. Firstly, the nearest neighbor set is selected according to the user similarity, and then the user evaluation data in the nearest neighbor set is used to infer the target item score.
Reliable Book Recommender System: An Evaluation and Comparison of Collaborative Filtering Algorithms

Nowadays, a vast number of online businesses and services use recommender systems to provide relevant product suggestions to their customers. Recommender systems can be defined as systems employing various data mining techniques to predict users’ interest in certain items or topics, among a tremendous amount of available data. While there exist varying methodologies to construct a recommender system, one of the most potent and commonly used techniques is collaborative filtering (CF), which evaluates items based on the opinions of other users. This paper examines several commonly used collaborative filtering models with the goal of creating a reliable book recommendation system. The research focuses on training, evaluating and comparing several neighborhood-based and matrix factorization-based recommender algorithms, using a combination of two book datasets: the BookCrossing dataset compiled in 2004, and the Goodbooks “most popular books” dataset from 2017. Grid search with cross-validation is used to find the best performing combination of several hyper-parameters and different CF models. Ultimately, a matrix factorization-based SVD++ approach is selected as the most performant model, with MAE of 0.57 and RMSE of 0.752, making it a considerably good model based on the provided data. Moreover, a manual comparison between a user’s book history and their recommended book titles shows that users are generally recommended books of the same or similar genres. Lastly, the paper discusses certain shortcomings of the proposed model and offers several suggestions for future improvements.
Innovative services and processes in university environment, processes of education supported by SMART technologies

Many science and education organizations are undergoing a digital transformation. They focus on the implementation of products and services that are commonly available in a commercial environment. The aim of these changes is external pressures from companies - customers of products as well as the requirements of clients. These are organizations that are interested in the services of such institutions. There are naturally scientific and educational organizations that are not making progress in the process of digital transformation. They have not solved this task for a long time and thus they get into a complicated situation. Although germs of shift arise in these institutes, they encounter resistance from their employees. The digitization process is slowing down or not happening at all. Such an approach blocks the development of institutions and puts them in a complicated situation in the supply chain. Science education organizations form an ecosystem in which, in addition to key, core business processes, there are other, supportive processes. Without these processes, it is not possible to ensure the proper functioning of the institution. From the level and quality of their settings, ie the focus on the client - student or. employee can be observed readiness to deal with critical situations. In our paper, we looked at the processes and portfolio of services, both critical and supportive, that could be effectively electricized. By this term we mean a state where it is possible to monitor increased productivity as well as the quality of work. The deployment of appropriate electronic components makes it possible to increase the “will” of decision-making, which brings another dimension of the provided services.
Cryptocurrencies: Valuable Investment Instruments or Speculation Bubbles?

The purpose of this paper is to contribute insights into the existing body of literature on Cryptocurrencies by systematizing existing knowledge and deriving specific implications for the scope of future work. Looking specifically on purpose of creation of cryptocurrency and answer the questions related to the usefulness of cryptocurrency as a tool for hedging purpose. Understanding cryptocurrency as an opportunity for valuable investments or considering them as merely speculative bubbles. The literature has showed the major risks associated with the cryptocurrencies and the prevailing huge price volatility in cryptocurrency market. Also, paper discussed the most fundamental issues pertaining to cryptocurrencies.
Developing Spatial Visualization Skills with Virtual Reality and Hand Tracking

Spatial visualization skills are the cognitive skills required to mentally comprehend and manipulate 2D and 3D shapes. Spatial visualization skills are recognized as a crucial part of STEM education. Moreover, these skills have been linked to both students' capacity for self-monitored learning and GPA in STEM programs. Also, many STEM fields have reported a correlation between spatial visualization skills and the level of academic success of students. Unfortunately, many students have significantly underdeveloped spatial visualization skills. Students traditionally learn spatial visualization skills via the manipulation of 3D objects and drawings. Because Virtual Reality (VR) facilitates the manipulation of 3D virtual objects, it is an effective medium to teach spatial visualization skills. In addition, it has been found that students are more motivated to learn and perform better when taught in an immersive VR environment. Several studies have presented promising results on leveraging VR to teach spatial visualization skills. However, in most of these studies, the users are not able to naturally interact with the 3D virtual objects. Additionally, many of the current VR applications fail to implement spatial presence for the user, which would lead to more effective instruction. The level of immersivity a VR application offers can have a direct impact on the users' "first-person" experience and engagement. In light of this, a VR application integrated with hand tracking technology designed to develop spatial visualization skills of STEM students is presented. The inclusion of a user's hand in the virtual environment could increase the users' sense of spatial presence and first-person experience. In addition, this could act as an intuitive input method for beginners. The objective of this work is to introduce this VR application as well as future work on how to leverage Reinforcement Learning to automatically generate new 3D virtual objects [github.com/lopezbec/VR_SpatialVisualizationApp].
Semantic segmentation based on semantic edge optimization

Semantic segmentation based on deep learning is to extract semantic features by convolution, and then classify each pixel. In the process of feature extraction, the subsampled operation used to expand the receptive field will cause a large amount of detail information loss, resulting in the loss of small-scale objects in the image and blurred segmentation edge. In order to solve this problem, this paper proposes a segmentation model of semantic edge optimization, which uses the end-to-end semantic edge detection network to learn the semantic edge features of the image, and then fuses the semantic edge features with the semantic segmentation features, so that more edge information can be retained in the final segmentation image. On the camvid and cityscape datasets, our optimization model improve over original semantic segmentation model by 1.4% and 1.5% in terms of mean IoU.
Semantic Representation of Machine Learning and Data Mining Algorithms

In this paper, we describe an extension of the ontology of core data mining entities (OntoDM-core) that will improve the semantic representation of machine learning and data mining algorithms. The OntoDM-core acknowledges the multi-faceted aspect of algorithms and accordingly provides entities, such as algorithm specification, algorithm implementation, and algorithm execution. We build upon this representation and include a more detailed representation of algorithms, including terms such as hyperparameter, optimization problem, complexity function, etc. Furthermore, we discuss the potential applications of the ontology. It can be used as a backbone of a repository and knowledge base for storing semantic annotations of algorithms and for assisting algorithm developers and domain experts with the task of manual semantic annotation. Ultimately, the corpus of manually annotated algorithms using the ontology vocabulary will serve as a foundation for automating the process of semantic annotation of algorithms from text using natural language processing techniques.
BayesTuner: Leveraging Bayesian Optimization For DNN Inference Configuration Selection

Deep learning sits at the core of many applications and products deployed on large-scale infrastructures such as data centers. Since the power consumption of data centers contributes significantly to operational costs and carbon footprint, it is essential to improve their power efficiency. To this end, both the hardware platform and application should be configured properly. However, identifying the best configuration automatically for a wide range of available options with affordable search cost is challenging (e.g., DNN batch size, number of cores, and amount of memory allocated to the application). Employing an exhaustive approach to test all the possible configurations is unfeasible. To tackle this challenge, we introduce <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BayesTuner</i> that employs Bayesian Optimization to estimate the performance model of deep neural network inference applications under different configurations with a few test runs. Having these models, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BayesTuner</i> is able to differentiate the optimal or near-optimal configurations from the rest of options. Using a realistic setup with various DNNs, we show how efficiently BayesTuner can explore the huge state space of possible control configurations, and minimize the power consumption of the system, while meeting the throughput constraint of different DNNs.
Access Pattern Hiding in Searchable Encryption

Cloud computing is a technology that provides users with a large storage space and an enormous computing power. For privacy purpose, the sensitive data should be encrypted before being outsourced to the cloud. To search over the outsourced data, searchable encryption (SE) schemes have been proposed in the literature. An SE scheme should perform searches over encrypted data without causing any sensitive information leakage. To this end, a few security constraints were elaborated to guarantee the security of the SE schemes, namely, the keyword privacy, the trapdoor unlinkability, and the access pattern. The latter is very hard to be respected and most approaches fail to guarantee the access pattern constraint when performing a search. This constraint consists in hiding from the server the search result returned to the user. The non respect of this constraint may cause sensitive information leakage as demonstrated in the literature. To fix this security lack, we propose a method that allows to securely request and receive the needed documents from the server after performing a search. The proposed method that we call the access pattern hiding (APH) technique allows to respect the access pattern constraint. An experimental study is conducted to validate the APH technique.
R-CASS: Using Algorithm Selection for Self-Adaptive Service Oriented Systems

In service composition, complex applications are built by combining web services to fulfill user Quality of Service (QoS) and business requirements. To meet these requirements, applications are composed by evaluating all possible web service combinations using search algorithms. These algorithms need to be accurate and inexpensive to evaluate a large number of possible service combinations and services' fluctuating QoS attributes while meeting the constraints of limited computational resources. Recent research has shown that different search algorithms can outperform others on specific instances of a problem domain, in terms of solution quality and computational resource usage. Problematically, current service composition approaches ignore this property, leading to inefficient compositions. To address these limitations, we propose a composition algorithm selection framework which selects an algorithm per composition task at runtime, R-CASS. Our evaluations demonstrate that R-CASS leads to more efficient compositions, reducing composition time by 55.1% and memory by 37.5%.
Online Knowledge Distillation Based on Multi-stage Multi-generative Adversarial Network

Knowledge distillation is one of the model compression techniques, where the key challenge is how to extract rich and general knowledge from teacher models, thus reducing the performance gap between the student models and the teacher models. The feature representation of the strong convergence of the teacher models has a heavy constraint on the student models. which brings certain influence on the quality of the student models. This paper designs an online knowledge distillation based on multi-stage multi-generative adversarial network (MS-MGAN), which enables the student model and the teacher model to learn together in multiple stages. First, the teacher model training is divided into several stages, and the training results of each stage are used to guide the student model training. Second, the student model is based on the convolutional block for layer-wise greedy training. Then, the generative adversarial network is integrated into the multi-stage training, with each convolutional block of the student model as the generator and the corresponding block of the teacher model as the discriminator, so that the student model can better follow and even surpass the performance of the teacher model. As demonstrated on several benchmarks, the proposed method can achieves surprising satisfied results, even outperforming the teacher model on some tasks.