Reviewerly/exHarmony huggingface dataset:
Hate Speech in Pixels: Detection of Offensive Memes towards Automatic Moderation

This work addresses the challenge of hate speech detection in Internet memes, and attempts using visual information to automatically detect hate speech, unlike any previous work of our knowledge. Memes are pixel-based multimedia documents that contain photos or illustrations together with phrases which, when combined, usually adopt a funny meaning. However, hate memes are also used to spread hate through social networks, so their automatic detection would help reduce their harmful societal impact. Our results indicate that the model can learn to detect some of the memes, but that the task is far from being solved with this simple architecture. While previous work focuses on linguistic hate speech, our experiments indicate how the visual modality can be much more informative for hate speech detection than the linguistic one in memes. In our experiments, we built a dataset of 5,020 memes to train and evaluate a multi-layer perceptron over the visual and language representations, whether independently or fused. The source code and mode and models are available https://github.com/imatge-upc/hate-speech-detection .
On Explainable Fuzzy Recommenders and their Performance Evaluation

Abstract This paper presents a novel approach to the design of explainable recommender systems. It is based on the Wang–Mendel algorithm of fuzzy rule generation. A method for the learning and reduction of the fuzzy recommender is proposed along with feature encoding. Three criteria, including the Akaike information criterion, are used for evaluating an optimal balance between recommender accuracy and interpretability. Simulation results verify the effectiveness of the presented recommender system and illustrate its performance on the MovieLens 10M dataset.
Fog Computing as Privacy Enabler

Despite broad discussions on privacy challenges arising from fog computing, the authors argue that privacy and security requirements might actually drive the adoption of fog computing. They present four patterns of fog computing fostering data privacy and the security of business secrets, complementing existing cryptographic approaches. Their practical application is illuminated on the basis of three case studies.
Validating Shacl Constraints over a Sparql Endpoint

shacl (Shapes Constraint Language) is a specification for describing and validating RDF graphs that has recently become a W3C recommendation. While the language is gaining traction in the industry, algorithms for shacl constraint validation are still at an early stage. A first challenge comes from the fact that RDF graphs are often exposed as sparql endpoints, and therefore only accessible via queries. Another difficulty is the absence of guidelines about the way recursive constraints should be handled. In this paper, we provide algorithms for validating a graph against a shacl schema, which can be executed over a sparql endpoint. We first investigate the possibility of validating a graph through a single query for non-recursive constraints. Then for the recursive case, since the problem has been shown to be NP-hard, we propose a strategy that consists in evaluating a small number of sparql queries over the endpoint, and using the answers to build a set of propositional formulas that are passed to a SAT solver. Finally, we show that the process can be optimized when dealing with recursive but tractable fragments of shacl, without the need for an external solver. We also present a proof-of-concept evaluation of this last approach.
Cooperative Computing in Integrated Blockchain-Based Internet of Things

In this article, we propose an energy-efficiency-aware integrated architecture of cooperative computing (CC) to support the demands of computing amount in the blockchain-based Internet of Things (IoT). Specifically, we assume that multiple computing servers are placed at each data access point (DAP). The computing servers across multiple DAPs can be virtualized to constitute a CC pool to flexibly allocate the computing resource. When the amount of received data from a DAP is accumulated to a certain length of one data block, blockchain computing will be implemented to generate a correct Nonce value meeting the threshold of hash value. After the correct Nonce has been generated, the data block will be transmitted and stored in cloud caches, where the hash value is written into blockchain to guarantee the security of data block. We maximize system energy efficiency defined by the overall power consumption per unit of throughput transmitted from DAPs to cloud caches. We formulate the system optimization model by considering the constraints of data delay to avoid data overflow in the system. In order to solve the optimization model for maximizing system energy efficiency, we employ a geometric programming method to obtain the optimal power and resource allocation in blockchain-based IoT. By extensive simulations, we verify the effectiveness of our proposed energy-efficiency-aware optimization mechanism in the blockchain-based IoT.
Design Methodology for the Implementation of Fuzzy Inference Systems Based on Boolean Relations

This paper proposes a methodology for the design of fuzzy inference systems based on Boolean relations. The approach using Boolean sets presents limited performance due to the abrupt transitions that occur during its functioning, therefore, fuzzy sets can be used aiming the improvement of the performance. In this approach, firstly, the design of a Boolean controller is performed, which is later extended into fuzzy under design guidelines proposed in this paper. The methodology uses Kleene algebra via truth tables for the fuzzy system design, allowing the simplification of the equations that implement the fuzzy system.
The Impact of Facebook on Jordanian Consumers’ Decision Process in the Hotel Selection

This study aims to explore the impact of Facebook on consumers' decision process in the hotel selection.The research population is customers who use social media; as a sample of 610 was chosen randomly from Jordanian customers in the hotel industry.The study used quantitative methodology to examine the research questions, and we analyzed quantitatively using structural equation modeling-partial least squares (SEM-PLS) method.This study reveals the importance of social media platform and its influence on customers' decision process in the hotel selection.The finding revealed that consumers have been exposed to digital information through Facebook; this information has influenced consumers' decision.The study found that Facebook has the greatest influence on customers' decision process.In addition, the finding revealed that the way customers search and book hotel have changed.The results represent novel findings that hold important implications and recommendations for future social media marketing research and practice.
Licensing FAIR Data for Reuse

The last letter of the FAIR acronym stands for Reusability. Data and metadata should be made available with a clear and accessible usage license. But, what are the choices? How can researchers share data and allow reusability? Are all the licenses available for sharing content suitable for data? Data can be covered by different layers of copyright protection making the relationship between data and copyright particularly complex. Some research data can be considered as a work and therefore covered by full copyright while other data can be in the public domain due to their lack of originality. Moreover, a collection of data can be protected by special rights in Europe to acknowledge the investment in time and money in obtaining, presenting, arranging or verifying the data. The need of using a license when sharing data comes from the fact that, under current copyright laws, when rights exist, the absence of any legal notice must be understood as the default “all rights reserved” regime. Unless an exception applies, the authorisation of right holders is necessary for reuse. Right holders could use any text to state the reusability of data but it is advisable to use some of the existing licenses, and especially the ones that are suitable for data and databases. We hope that with this paper we can bring some clarity in relation to the rights involved when sharing research data.
Post-pedagogical Syndrome of the Digimodernism Age

The article discusses the influence of Metamodern culture and digitalization of education on the transformation of the pedagogical understanding of learning process. The author relies on the post-postmodern age interpretations presented in the works of S. Abramson, M. Epstein, R. Eschelman, A. Kirby, J. Mensch, P. Samuels, L. Turner, T. Vermeulen and R. Van den Acker; the transdisciplinary (B. Nicholescu) and connectivist (D. Cormier, G. Siemens) approaches and international documents about the education development in a complex society. In the Metamodern epoch under the influence of the universal digitalization there have been signs indicating that the theory and practice of learning has gone beyond the enduring some pedagogical meanings. This is being manifested at the level of the spontaneous transformation of the main components of learning process; changes in the pedagogical thesaurus, the emergence of “hybrid” didactic theories. The author defines this phenomenon as the post-pedagogical syndrome of the digital age which is capable to disrupt the historical continuity of educational culture. The article talks about the lack of resources in modern pedagogical science to explain the realities of lifelong education as a part of the Digimodernism culture and to develop the truly innovative ways of teaching. The author has suggested that the creation of effective methods of teaching and self-education in the digital age is possible only in the case of rethinking and reassessment of basic didactic categories and concepts on a transdisciplinary basis, taking into account the pedagogical experience of the analogical era and preserving the continuity of humanitarian values and meanings. The author’s arguments and conclusions are addressed to supporters of the study of education on an interdisciplinary and transdisciplinary basis.
Towards Answer-unaware Conversational Question Generation

Conversational question generation is a novel area of NLP research which has a range of potential applications. This paper is first to presents a framework for conversational question generation that is unaware of the corresponding answers. To properly generate a question coherent to the grounding text and the current conversation history, the proposed framework first locates the focus of a question in the text passage, and then identifies the question pattern that leads the sequential generation of the words in a question. The experiments using the CoQA dataset demonstrate that the quality of generated questions greatly improves if the question foci and the question patterns are correctly identified. In addition, it was shown that the question foci, even estimated with a reasonable accuracy, could contribute to the quality improvement. These results established that our research direction may be promising, but at the same time revealed that the identification of question patterns is a challenging issue, and it has to be largely refined to achieve a better quality in the end-to-end automatic question generation.
The Semantic Asset Administration Shell

The disruptive potential of the upcoming digital transformations for the industrial manufacturing domain have led to several reference frameworks and numerous standardization approaches. On the other hand, the Semantic Web community has made significant contributions in the field, for instance on data and service description, integration of heterogeneous sources and devices, and AI techniques in distributed systems. These two streams of work are, however, mostly unrelated and only briefly regard the each others requirements, practices and terminology. We contribute to this gap by providing the Semantic Asset Administration Shell, an RDF-based representation of the Industrie 4.0 Component. We provide an ontology for the latest data model specification, created a RML mapping, supply resources to validate the RDF entities and introduce basic reasoning on the Asset Administration Shell data model. Furthermore, we discuss the different assumptions and presentation patterns, and analyze the implications of a semantic representation on the original data. We evaluate the thereby created overheads, and conclude that the semantic lifting is manageable, also for restricted or embedded devices, and therefore meets the conditions of Industrie 4.0 scenarios.
Optimal Trajectory Generation for Autonomous Vehicles Under Centripetal Acceleration Constraints for In-lane Driving Scenarios

This paper presents a noval method that generates optimal trajectories for autonomous vehicles for in-lane driving scenarios. The method computes a trajectory using a two-phase optimization procedure. In the first phase, the optimization procedure generates a close-form driving guide line with differetiable curvatures. In the second phase, the procedure takes the driving guide line as input, and outputs dynamically feasible, jerk and time optimal trajectories for vehicles driving along the guide line. This method is especially useful for generating trajectories at curvy road where the vehicles need to apply frequent accelerations and decelerations to accommodate centripetal acceleration limits.
Efficient payload communications for IoT-enabled ViSAR vehicles using discrete cosine transform-based quasi-sparse bit injection

Abstract High-performance remote sensing payload communication is a vital problem in air-borne and space-borne surveillance systems. Among different remote sensing imaging systems, video synthetic aperture radar (ViSAR) is a new technology with lots of principal and managerial data which should be compressed, aggregated, and communicated from a radar platform (or a network of radars) to a ground station through wireless links. In this paper, a new data aggregation technique is proposed towards efficient payload transmission in a network of aerial ViSAR vehicles. Our proposed method is a combination of a recent interpolation-based data hiding (IBDH) technique and visual data transformation process using discrete cosine transform (DCT) which is able to outperform the reference method in terms of data aggregation ability.
A Generalized Algorithm for Multi-Objective Reinforcement Learning and Policy Adaptation

We introduce a new algorithm for multi-objective reinforcement learning (MORL) with linear preferences, with the goal of enabling few-shot adaptation to new tasks. In MORL, the aim is to learn policies over multiple competing objectives whose relative importance (preferences) is unknown to the agent. While this alleviates dependence on scalar reward design, the expected return of a policy can change significantly with varying preferences, making it challenging to learn a single model to produce optimal policies under different preference conditions. We propose a generalized version of the Bellman equation to learn a single parametric representation for optimal policies over the space of all possible preferences. After an initial learning phase, our agent can execute the optimal policy under any given preference, or automatically infer an underlying preference with very few samples. Experiments across four different domains demonstrate the effectiveness of our approach.
Design Friction

This study evaluates if intentionally added design friction affects users level of satisfaction when using a mobile application. Today most applications are designed to have as little friction as possible. An interesting question is if a more mindful interaction will lead to more satisfied users. In this study two prototypes inspired by the Headspace application where tested. One prototype had added design friction and the other had none. The participants were asked to rate their experience and to choose which prototype they preferred. The result shows that most participants of the test would choose the mobile application with added design friction and that they felt more satisfied when they had a clear understanding of the goal of the task.
Dynamic Prosody Generation for Speech Synthesis Using Linguistics-Driven Acoustic Embedding Selection

Recent advances in Text-to-Speech (TTS) have improved quality and naturalness to near-human capabilities when considering isolated sentences. But something which is still lacking in order to achieve human-like communication is the dynamic variations and adaptability of human speech. This work attempts to solve the problem of achieving a more dynamic and natural intonation in TTS systems, particularly for stylistic speech such as the newscaster speaking style. We propose a novel embedding selection approach which exploits linguistic information, leveraging the speech variability present in the training dataset. We analyze the contribution of both semantic and syntactic features. Our results show that the approach improves the prosody and naturalness for complex utterances as well as in Long Form Reading (LFR).
A New Method of Improving BERT for Text Classification

Text classification is a basic task in natural language processing. Recently, pre-training models such as BERT have achieved outstanding results compared with previous methods. However, BERT fails to take into account local information in the text such as a sentence and a phrase. In this paper, we present a BERT-CNN model for text classification. By adding CNN to the task-specific layers of BERT model, our model can get the information of important fragments in the text. In addition, we input the local representation along with the output of the BERT into the transformer encoder in order to take advantage of the self-attention mechanism and finally get the representation of the whole text through transformer layer. Extensive experiments demonstrate that our model obtains competitive performance against state-of-the-art baselines on four benchmark datasets.
An Improved Flower Pollination Optimizer Algorithm for Multilevel Image Thresholding

Multilevel image thresholding is an important technique for image processing. However, the computational complexity of multilevel image thresholding grows exponentially with the increase in the number of thresholds when using the exhaustive searching method. To address this problem, a plenty of heuristic algorithms are applied to search the optimal thresholds. In this paper, an improved flower pollination algorithm (IFPA) using Tsallis entropy as its objective function is presented to find the optimal multilevel thresholding. In the IFPA, three modifications are utilized to enhance the flower pollination algorithm (FPA). First, an adaptive switch probability method is used to balance the local and global pollination. Second, a new local pollination strategy is adopted to avoid the population falling into local optimum. Third, an crossover and selection operations are applied to the FPA which can increase the diversity of the population, then enhancing the performance of the FPA. Subsequently, three different algorithms such as FPA, GSA and DE are introduced to compare with the IFPA in the experiments. The experimental results demonstrated that the IFPA can search out the optimal thresholds effectively, accurately and can obtain the best image segmentation quality.
Discontinuity at fixed points with applications

In this paper, we study new contractive conditions which are strong enough to generate fixed points but which do not force the map to be continuous at fixed points. In this context, we give new results on the fixed-circle problem. We investigate some applications to complex-valued metric spaces and to discontinuous activation functions in real and complex valued neural networks.
Hierarchical Data Augmentation and the Application in Text Classification

The applications of data augmentation in natural language processing have been limited.In this paper, we propose a novel method named Hierarchical Data Augmentation (HDA) which applied for text classification.Firstly, inspired by the hierarchical structure of texts, as words form a sentence and sentences form a document, HDA implements a hierarchical data augmentation strategy by augmenting texts at word-level and sentence level respectively.Secondly, inspired by the cropping, a popular method of data augmentation in computer vision, at each augmenting level, HDA utilizes attention mechanism to distill (crop) important contents from texts hierarchically as summaries of texts.Specifically, we use a trained Hierarchical Attention Networks (HAN) model to obtain attention values of all documents in training sets at both levels respectively, which are further used to extract the most important part of words/sentences and generate new samples by concatenating them in order.Then we gain two levels of augmented datasets, WordSet and SentSet.Finally, extending training set with certain amount of HDA-generated samples and we evaluate models' performance with new training set.The results reveal HDA can generate massive and high-quality augmented samples at both levels, and models using these samples can obtain significant improvements.Compared with the existing methods, HDA enjoys the simplicity both on theory and implementation, and it can augment texts at two levels for the diversity of data.
Effective resource management in digital forensics

Purpose Building on the findings of a British Academy-funded project on the development of digital forensics (DF) in England and Wales, the purpose of this paper is to explore how triage, a process that helps prioritise digital devices for in-depth forensic analysis, is experienced by DF examiners and police officers in four English police forces. It is argued that while as a strategy triage can address the increasing demand in the examination of digital exhibits, careful consideration needs to be paid to the ways in which its set-up, undertaking and outcomes impact on the ability of law enforcement agencies to solve cases. Design/methodology/approach The methodological approach adopted here builds on the ethnographic turn in criminology. The analysis draws on 120 h of ethnographic observations and 43 semi-structured interviews. Observational data of the working DF environment at each location and a systematic evaluation of internal documents, organisational settings and police priorities helped refine emergent analysis threads, which were analytically compared between sites and against the testimonies of members of different occupational groups to identify similarities and differences between accounts. Findings The findings emphasise the challenges in the triage of digital exhibits as they are encountered in everyday practice. The discussion focusses on the tensions between the delivery of timely and accurate investigation results and current gaps in the infrastructural arrangements. It also emphasises the need to provide police officers with a baseline understanding of the role of DF and the importance of clearly defined strategies in the examination of digital devices. Originality/value This paper aims to bridge policy and practice through an analysis of the ways in which DF practitioners and police officers in four English constabularies reflect on the uses of triage in DF to address backlogs and investigative demands. Highlighting the importance of digital awareness beyond the technical remit of DF units, it offers new insights into the ways in which police forces seek to improve the evidential trail with limited resources.