Reviewerly/exHarmony huggingface dataset:
SoK: Development of Secure Smart Contracts – Lessons from a Graduate Course

Smart contracts are programs on top of blockchains and cryptocurrencies. This new technology allows parties to exchange valuable assets without mutual trust, with smart contracts controlling the interaction between the parties. Developing smart contracts, or more generally decentralized applications, is challenging. First, they run in a concurrent environment that admits race conditions; adversaries may attack smart contracts by influencing the order of transactions. Second, the required functionality is often based on roles and states. This proves to be difficult to implement in current smart contract languages. Third, as a distinctive feature, smart contracts are immutable, hence bugs cannot be corrected easily. At the same time, bugs may cause (and have already caused) tremendous losses; they are to be avoided by all means. This paper discusses our approach of teaching the development of secure smart contracts on the Ethereum platform at university level. This is a challenging task in many respects. The underlying technologies evolve rapidly and documentation lags behind. Available tools are in different stages of development, and even the most mature ones are still difficult to use. The development of secure smart contracts is not yet a well-established discipline. Our aim is to share our ideas, didactic concept, materials, insights, and lessons learned.
A Protocolized, Comparative Study of Helios Voting and Scytl/iVote

E-voting implantation is happening at a slower pace than anticipated. A plethora of technical and social challenges hinder a deeper implementation. In this article, the problem is addressed by applying a practical evaluation framework to two of the most relevant e-voting tools: Helios Voting and iVote from Scytl. The framework is strongly based on the technical requirements issued by the Council of Europe in 2017. The authors believe it can constitute a useful source of information for election officials, researchers and even voters. The final purpose is contributing to a gradual, secure and protocolized expansion of e-voting in Europe; more so in the present times, with mounting geo-political challenges and tensions.
A Modern Approach to Situation Awareness

Wikipedia states: "Situational awareness or situation awareness (SA) is the perception of environmental elements and events with respect to time or space, the comprehension of their meaning, and the projection of their future status." [1]
Immersion into Holographic Spaces to Enhance Engineering and Architecture Design Interpretations

Holograms are digital representations blended into the physical space that enable the interaction of the users with the physical environment. This research explores the effect holograms have on the users' experiences of the interpretation of representations of architectural and engineering designs with users' association of the physical and functional characteristics in construction sites. The approach builds a mixed reality (MR) environment to create holographic visualizations from representations of designs generated from building information models (BIM) and construction document data (e.g., specifications, schedules). The approach immerses users into co-existent physical and virtual worlds using head mounted display (OST-HMD). The technology maps the users' holographic experience with real-world construction context using spatial mapping and location tracking techniques.
Crowd in C

We introduce an audience participation system for a music piece, Crowd in C. A distributed musical instrument is implemented entirely on a web browser for an audience to easily participate in music making with their smartphones and to generate sound on their palms. This web-based instrument is designed to encourage an audience to play music together and to interact with other audience members. Each participant composes a short musical tune that will be a musical profile of oneself. Once a profile is submitted, they can browse other people's profiles and play a pattern they like in pairs as if one improvises with another musician. The use of musical profiles is a metaphor for online dating websites and the network created by the instrument mimics such social interaction where a user browses profiles, likes someone, and mingle with other online users.
On the End-to-End Development of a Cultural Tourism Recommender

Recommenders are systems that employ some knowledge on items and user preferences, along with sophisticated algorithms to provide personalised content and services. They have been around to tackle the information overload and personalisation demand in today's always-connected world. This technology appeared in the cultural heritage domain relatively recently, but the bibliography is already rich, as cultural tourism plays an important role for regional economies. From the technical perspective, different approaches, like collaborative filtering, content-based, knowledge-based and hybrid approaches, have been adopted. From the intuition perspective, the approaches are influenced by current conceptualisation and specific application domains and demands. The museum has been one of the main target applications, either as a part of visit support or in the context of cultural tourism initiatives. This article presents a review of the domain and draws a generic blueprint for the end-to-end development of a recommender for cultural tourism that outperforms a baseline popularity-based approach.
Characterization of Facial Expression using Deep Neural Networks

Deep learning plays a significant role in the advancement of computer vision by improving the speed and accuracy to the assigned tasks. It is opening opportunities for improvement and enhancement of processes and to initiate the human-driven tasks in an automated manner. On the basis of this growth, deep-learning algorithms are finding applications in the field CNN and RNN. The key advantage of Deep Learning algorithm is that manually extraction of features from the image is not required. The network extracts the features while training. The only input required is to provide the image to the network. The CNN's and RNN's have given state-of-the art results on numerous classification tasks. The Deep learning algorithm are designed for feature detection / extraction, classification and recognition of the object. The key advantage of a CNN is to remove or reduce the reliance on physics-based models, other processing methods by enabling complete learning directly from the input images of the object. The CNN and RNN together has given effective results in the area of face recognition, object recognition, scene understanding and facial expression recognition.
What Can We Expect from Navigating?

What is it to navigate or to be navigated? How, and through what, is information communicated to us? Do our interactions with space need to be limited to when we are moving through it? This paper describes a collection of design concepts generated as part of the initial stages of a research project that combines a critical design mindset and research through design process to explore these types of questions. The project seeks to problematise and diversify the discussion and understanding around pedestrian navigation, wearable technology, crowdsourcing and human data interaction. The goal is to develop one of the concepts using research through design as part of PhD research studies, leading to possible future applications.
Phase-only Digital Encryption using a Three-pass Protocol

This paper considers an application of phase-only digital encryption to the three-pass protocol leading to a new ‘no-key-exchange algorithm’. After providing a study on the theoretical background to the method, an algorithm is presented on a step-by-step basis together with three examples of cryptanalysis. A prototype MATLAB function is provided for validation of the approach and for further development by interested readers.
Toward Better Service Performance Management via Workload Prediction

In this paper, we consider managing service performance starting from the composition time, aiming to reduce the risk of execution failures during service composition. We use ARIMA to predict workloads of the services at the time when they are likely to be invoked and subsequently predict the response time and chances that the requests for accessing the services may be declined due to admission control. The in-depth analysis can help avoid timing failures during service execution. However, these analyses may incur overhead and we introduce a two-phase composition algorithm to reduce the potential overhead. Our system also considers continuous monitoring and service recomposition to greatly increase the probability of completing the service execution within the deadline. Experimental results show that our service management approach can greatly improve the success rate for meeting the deadline.
Cross-layer fusion feature network for material defect detection

Object detection involves solving two main problems: identifying the object and its location. This transforms the problem into a classification and localization problem. Currently, research shows that a convolutional neural network (CNN) can be used to solve the classification problem, and the object detection module incorporating a region proposal method can be used to locate the object. Although a CNN based on region proposals can achieve high recall, which improves detection accuracy, its performance cannot meet the actual requirements for small-size object detection and precise localization. This is mainly due to the feature maps extracted from the CNN and the quality of the region proposals. We present a cross-layer fusion feature network (CLFF-Net) for both high-quality region proposal generation and accurate object detection. The CLFF-Net is based on the cross-layer fusion feature that extracts hierarchical feature maps and then aggregates them into a unified space. The fused feature map appropriately combines deep layer semantic information, middle layer supplemental information, and shallow layer location information for an image to build the CLFF-Net, which is shared for both generating region proposals and detecting objects via end-to-end training. Extensive experiments using a casting dataset demonstrate its promising performance compared to state-of-the-art approaches.
Developing an ontology schema for enriching and linking digital media assets

The abundance of digital media information coming from different sources, completely redefines approaches to media content production management and distribution for all contexts (i.e. technical, business and operational). Such content includes descriptive information (i.e. metadata) about an asset (e.g. a movie, song or game), as well as playable media (e.g. audio or video files). Metadata is organised following a variety of inconsistent structures and formats that are supplied by various content providers. Some challenges have been addressed in terms of standardising and enriching media assets metadata from a semantic perspective. Well known examples include Europeana and DBpedia. Nevertheless, due to the ongoing variability and evolution of digital contents, constant support and creation of new semantic representations are necessary. This article presents an ontology schema covering the requirements of users (content providers and content consumers) involved in the overall life cycle of a digital media asset, which has been designed and developed for a real scenario. The construction of this schema has been documented and evaluated following a methodology supported by quantitative and qualitative metrics. As part of the tangible results, the following outcomes were produced: (i) an RDF/XML schema available via Zenodo and GitHub; (ii) competence questions used for validation are published at GitHub; (iii) an exemplary ontology repository; and (iv) CRUD (Create, Read, Update and Delete) technologies for managing semantic repositories based on such schema. These results form an active part of the framework of a European project and other ongoing research initiatives.
Research on static image recognition of sports based on machine learning

At present, artificial intelligence for sports static image recognition is mostly in the action judgment stage, but less analysis on the action detail stage. Based on this, based on machine learning, this study uses static images and video sequences as carriers to improve traditional algorithm rese arch and to perform motion gesture recognition. Through performance analysis, this paper explores the traditional algorithm and uses parameter analysis to improve the feature extraction and classification of traditional algorithms. Moreover, this paper uses the multi-scale feature approximation calculation method to improve the speed of the algorithm to extract features, and the algorithm is tested using the UCF motion data set and the self-created motion data set. In addition, this paper obtains representative motion video through data collection to test the effectiveness of the proposed algorithm. The research shows that the proposed algorithm has good performance and can provide theoretical reference for subsequent related research.
Human, All Too Human

Investigating the multimodal communication of Tourist Guides to implement a Virtual Tourist Guide leading tourists in three Italian Charterhouses, the paper focuses on an aspect of the human guide's speech that would be useful to create a very realistic Virtual Guide: linguistic disfluencies. On a corpus of three guided tours in S. Martino Charterhouse (Naples) an analysis is presented of the guides' pauses and their concomitant gestures.
About the development of a technological complex with a manipulator for an unmanned underwater vehicle

Abstract This paper presents solution of underwater manipulator tasks providing interaction with environment to perform tasks. Underwater manipulator is a part of unmanned underwater vehicle used to perform operations on ordered depth. Technological system based on standard and available elements is explored. Main research avenue is a remote control of modular system elements using Programmable Logic Controller. This paper describes the design and implementation of algorithms for controlling the manipulator or its hardware platform. Software architecture to control using standard programming tools is shown. Additionally, compute resources necessity depending on control system structure has been reviewed.
A Meta-Algorithm for Improving Top-<i>N</i> Prediction Efficiency of Matrix Factorization Models in Collaborative Filtering

Matrix factorization models often reveal the low-dimensional latent structure in high-dimensional spaces while bringing space efficiency to large-scale collaborative filtering problems. Improving training and prediction time efficiencies of these models are also important since an accurate model may raise practical concerns if it is slow to capture the changing dynamics of the system. For the training task, powerful improvements have been proposed especially using SGD, ALS, and their parallel versions. In this paper, we focus on the prediction task and combine matrix factorization with approximate nearest neighbor search methods to improve the efficiency of top-N prediction queries. Our efforts result in a meta-algorithm, MMFNN, which can employ various common matrix factorization models, drastically improve their prediction efficiency, and still perform comparably to standard prediction approaches or sometimes even better in terms of predictive power. Using various batch, online, and incremental matrix factorization models, we present detailed empirical analysis results on many large implicit feedback datasets from different application domains.
A Fuzzy Cognitive Map (FCM) as a Learning Model for Early Prognosis of Seasonal Related Virus Diseases in Tropical Regions

Fuzzy Cognitive Maps (FCMs) and current developments in Machine Learning have been contributing in capturing human behaviors through data and learning models, which focus on predicting, interpreting or identifying behavioral patterns on systems and their relationships. In recent years seasonal diseases caused by vectors that transport viral pathogens in tropical regions, such as the Aedes Aegypti mosquito, have caused noticeable impacts both on public health and country's economies in Latin America. This work proposes a model for early prognosis based on FCMs for making a risk assessment of potential presence of seasonal virus-related diseases in a specific region of the Ecuadorian coast. The FCM is used as a knowledge representation strategy for the cause-effect relationships; and, learning models for gaining the identification of the underlying cause of symptoms. The model aims to improve the chances of proper prognosis of seasonal diseases, which could impact not only the prescription and correct decisions, but also the actions taken for preventing the spread of seasonal diseases.
On the Personalization of Image Content in Graphical Passwords based on Users' Sociocultural Experiences

Recent works underpin the added value of considering users' past sociocultural experiences as a personalization factor for the image content used within graphical password schemes, since it has a positive impact on the security and memorability of the user-chosen passwords. This paper discusses the need for personalization of the image content used in graphical password schemes, as well as the initial steps towards the realization of an image content personalization framework that aims to achieve a better equilibrium between security and memorability. The paper also discusses emerging challenges related to the elicitation and maintenance of individual sociocultural-centered user models, the image content personalization mechanism and privacy considerations.
Comparative Study for Detecting Mobile Application's Anti-Patterns

Software design has a main impact in the quality of the software systems. Anti-patterns are shortcomings exist in the software designs and impact negatively software quality. Mobile applications (apps) with anti-patterns have bad quality and short lifetime. Many empirical studies have assessed that the anti-patterns have a negative impact on change-proneness, fault-proneness, memory consumption and energy efficiency. In addition to that, many studies showed that there was an improvement in the user interface and memory performance of mobile apps when correcting Android anti-patterns. The aim of our research is choosing the suitable UML modeling environment to detect Mobile applications' anti-patterns via reverse engineering. So, in this research, first we present a comparative study between nine UML tools for determining the tools that have the functionality for (reverse, forward) engineering and have the ability for validating the model against the anti-patterns. Second, we apply our proposed method to generate the class diagram model of the apps through decoding the Java source code and detects the design anti-patterns in the model. For validating the proposed method, we applied it in twenty-nine Mobile apps which were downloaded from APKmirror. The proposed method detects and treats ten anti-patterns which have appeared 749 times in the twenty-nine apps.
Lifted Temporal Most Probable Explanation

The lifted dynamic junction tree algorithm (LDJT) answers filtering and prediction queries efficiently for temporal probabilistic relational models by building and then reusing a first-order cluster representation of a knowledge base for multiple queries and time steps. Another type of query asks for a most probable explanation (MPE) for given events. Specifically, this paper contributes (i) LDJT $$^{mpe}$$ to efficiently solve the temporal MPE problem for temporal probabilistic relational models and (ii) a combination of LDJT and LDJT $$^{mpe}$$ to efficiently answer assignment queries for a given number of time steps.
A Holographic Checklist Assistant for the Single Pilot

Single Pilot Operations is a topic with the potential to significantly affect the future of commercial aviation. While financially attractive for airlines, Single Pilot Operations bring forth important safety concerns, especially regarding the lack of human redundancy in the flight deck, an increased workload for the single pilot, reduced situational awareness and a higher risk of human error. It is assumed that potential problems affecting Single Pilot Operations could be addressed by implementing an Augmented Reality (AR) device in the flight deck, by presenting additional information and supporting hints within the pilot's field of view. This paper sets out to demonstrate the conduction of checklists aided by the Microsoft HoloLens. A system, called Pilot Assist, was developed that allows the pilot to conduct checklists interactively with a Microsoft HoloLens. Pilot Assist was developed and demonstrated with a fixed based Airbus A320 simulator at the Technical University of Wildau. By using the HoloLens' speech recognition and spatial mapping capabilities - scanning and recognizing the environment around the user - it is possible to trigger the execution of checklists. The user is then prompted towards the location of each checklist item in the cockpit, where information regarding necessary actions is projected. Pilot Assist is integrated with the aircraft systems, which allows to obtain aircraft status data in real time and automate the progress through the checklists. Pilots, researchers and engineers had the chance to qualitatively assess the Pilot Assist tool. the experts agreed Pilot Assist provides beneficial support during single pilot operation considering the current prototypical nature of the system. They pointed to limitations of both Pilot Assist and the HoloLens itself, but shared optimism as to how this technology and similar applications could indeed impact the future of flight operations.
Substructural Propositional Dynamic Logics

We prove completeness and decidability of a version of Propositional Dynamic Logic where the underlying non-modal propositional logic is a substructural logic in the vicinity of the Full Distributive Non-associative Lambek Calculus. Extensions of the result to stronger substructural logics are briefly discussed.
Automatic metrics learning with low‐noise embedding for zero‐shot learning

This Letter presents a new improved zero-shot learning (ZSL) method based on meta-learning that automatically adjusts metrics by multi-layer perceptrons instead of using traditional Euclidean distance, cosine distance and other fixed metrics. In this way, the authors directly train the visual and semantic joint information of the images without adding additional auxiliary, which simply and effectively avoids the error derivation of unseen classes due to improper fixed metrics. Smooth one-hot encoding rule is used to make the calculation of loss more reasonable. They also discuss the embedding methods to find a suitable low-noise embedding space and analyse the model performance under the conventional setting and popular generalised ZSL setting. Experimental results show that the proposed automatic metrics method outperforms existing state-of-the-art approaches in some parts of AwA2 and CUB datasets.
Quantum-inspired evolutionary multi-objective fuzzy classifier with real and categorical representation

This work presents a new model for the automatic synthesis of fuzzy classifiers, based on quantum-inspired evolutionary algorithms, which overcomes the difficulties inherent to the use of hybrid representations and the treatment of multiple objectives, both necessary for the synthesis of these type s of systems. Without any a priori information about the classifier rules or any initial adjustment of individuals, the results obtained are comparable to those of other techniques that start from classifier populations previously adjusted to obtain good performance. According to the current trend, the aim was to build classifiers with good accuracy and simultaneous high interpretability of their fuzzy rule base.