Reviewerly/exHarmony huggingface dataset:
Empathic dialogue system based on emotions extracted from tweets

Empathic conversations have increasingly been important for dialogue systems to improve the users' experience, and increase their engagement with the system, which is difficult for many existing monotonous systems. Existing empathic dialogue systems are designed for limited domain dialogues. They respond fixed phrases toward observed user emotions. In open domain conversations, however, generating empathic responses for a wide variety of topics is required. In this paper, we draw on psychological studies about empathy, and propose an empathic dialogue system in open domain conversations. The proposed system generates empathic utterances based on observed emotions in user utterances, thus is able to build empathy with users. Our experiments have proven that users were able to feel more empathy from the proposed system, especially when their emotions were explicitly expressed in their utterances.
Aspect-based summarisation using distributed clustering and single-objective optimisation

In the user reviews of various domains, there is an increase in the accumulation of reviews in the web that presents a lot of difficulties to the readers. So it becomes necessary to generate a summary which represents the entire review in a concise manner. It is required for each feature or aspect in the reviews for the ease of users. The aspect-based summarisation plays a vital role in the field of opinion mining. This article proposes an aspect summarisation framework using sentence scoring clustering and weight-based single-objective optimisation technique by utilising evolutionary algorithm. The system uses MapReduce framework to incorporate the proposed combiner–based optimised clustering approach. Then a novel single-objective optimisation with genetic algorithm is developed. Its purpose is to retrieve top sentences from each cluster to generate feature-based summary. The accuracy of the system-generated summary is evaluated using the Recall Oriented Understanding for Gisting Evaluation tool kit using human standard reference summaries. The system is able to achieve more promising results when compared with other standard feature–based summarisation systems.
Characterizing safe and partially safe evolution scenarios in product lines

Evolving software product lines is often error-prone. Previous works have proposed classifying product line evolution into safe or partially safe, depending on the number of products that have their behavior preserved after evolution. Based on these notions, it is possible to derive transformation templates that abstract common evolution scenarios, such as adding an optional feature. However, existing works are focused on evaluating either safe or partially safe templates. Hence, in this work we aim to characterize product line evolution as a whole, measuring to what extent the evolution history is safe compared to partially safe, to better understand how product lines evolve. We measure how often existing templates happen using 2,300 commits from an open-source product line. According to our study, 91.7% of the commits represent partially safe evolution scenarios. Our results also show that 1,800 of these commits can automatically be classified as instances of existing templates. Among these, commits that do not modify other variability-aware models, are the most frequent, accounting for 72.3% out of the total of commits. For the remaining 500 commits, we identify that 24.4% are related to changes in the configuration knowledge, that is, the file responsible for the mapping between features and code.
The Genesis of EngMeta - A Metadata Model for Research Data in Computational Engineering

In computational engineering, numerical simulations produce huge amounts of data. To keep this research data findable, accessible, inter-operable and reusable, a structured description of the data is indispensable. This paper outlines the genesis of EngMeta – a metadata model designed to describe engineering simulation data with a focus on thermodynamics and aerodynamics. The metadata model, developed in close collaboration with engineers, is based on existing standards and adds discipline-specific information as the main contribution. Characteristics of the observed system offer researchers important search criteria. Information on the hardware and software used and the processing steps involved helps to understand and replicate the data. Such metadata are crucial to keeping the data FAIR and bridging the gap to a sustainable research data management in computational engineering.
Mirrored Orthogonal Sampling for Covariance Matrix Adaptation Evolution Strategies

Generating more evenly distributed samples in high dimensional search spaces is the major purpose of the recently proposed mirrored sampling technique for evolution strategies. The diversity of the mutation samples is enlarged and the convergence rate is therefore improved by the mirrored sampling. Motivated by the mirrored sampling technique, this article introduces a new derandomized sampling technique called mirrored orthogonal sampling. The performance of this new technique is both theoretically analyzed and empirically studied on the sphere function. In particular, the mirrored orthogonal sampling technique is applied to the well-known Covariance Matrix Adaptation Evolution Strategy (CMA-ES). The resulting algorithm is experimentally tested on the well-known Black-Box Optimization Benchmark (BBOB). By comparing the results from the benchmark, mirrored orthogonal sampling is found to outperform both the standard CMA-ES and its variant using mirrored sampling.
A framework for intelligent question answering system using semantic context-specific document clustering and Wordnet

The question answering system plays an important role in information retrieval field, where the user is in need of getting a precise answer instead of large collections of documents. The aim of this paper is to investigate techniques for improving sentence-based question answering system. To achieve this, a POS-Tagger-based question pattern analysis model is proposed to identify question type based on pattern template for the user-submitted query. Next, the knowledge base is created from a large corpus by clustering the documents by grouping on domain context. The proposed semantic-word-based answer generator model deals with the user query mapping with an appropriate sentence in the knowledge base. By the proposed models, the system reduces the search gap among user queries and answer sentences using Wordnet. It considers word order, overlap, sentence similarity, string distance, unambiguous words and semantic similarity of words. The proposed algorithm evaluates with benchmark datasets such as 20Newsgroup and TREC-9 QA, and proves its efficiency by statistical test for significance.
The map is not the territory

This forum addresses conceptual, methodological, and professional issues that arise in UX's continuing effort to have a positive impact by contributing robust information about users to product planning and design. --- David Siegel and Susan Dray, Editors
A personalized network-based recommendation approach via distinguishing user’s preference

With the rapid growth of commerce and development of Internet technology, a large number of user consumption preferences become available for online market intelligence analysis. A critical demand is to reduce the impact of information overload by using recommendation algorithms. In physical dynamics, network-based recommendation algorithms based on mass-diffusion have been popular for its simplicity and efficiency. In this paper, to solve the problem that most network-based recommendation algorithms cannot distinguish how much the user likes collected items and make resource configuration more reasonable, we propose a novel method called biased network-based inference (BNBI). The proposed method treats rating systems and nonrating systems differently and measures user’s preference for items by means of item similarity. The proposed method is evaluated in real datasets (MovieLens and Last.FM) and compared with some existing classic recommendation algorithms. Experimental results show that the proposed method is more effective and it can reduce the impact of item diversity and discover the real interest of users.
A Multilingual and Multidomain Study on Dialog Act Recognition Using Character-Level Tokenization

Automatic dialog act recognition is an important step for dialog systems since it reveals the intention behind the words uttered by its conversational partners. Although most approaches on the task use word-level tokenization, there is information at the sub-word level that is related to the function of the words and, consequently, their intention. Thus, in this study, we explored the use of character-level tokenization to capture that information. We explored the use of multiple character windows of different sizes to capture morphological aspects, such as affixes and lemmas, as well as inter-word information. Furthermore, we assessed the importance of punctuation and capitalization for the task. To broaden the conclusions of our study, we performed experiments on dialogs in three languages—English, Spanish, and German—which have different morphological characteristics. Furthermore, the dialogs cover multiple domains and are annotated with both domain-dependent and domain-independent dialog act labels. The achieved results not only show that the character-level approach leads to similar or better performance than the state-of-the-art word-level approaches on the task, but also that both approaches are able to capture complementary information. Thus, the best results are achieved by combining tokenization at both levels.
Collision Avoidance in Cooperative Missions: Bézier Surfaces for Circumnavigating Uncertain Speed Profiles

This paper presents a collision-prediction and avoidance algorithm for multivehicle cooperative missions. Using Bézier surfaces, the algorithm avoids time discretization of trajectories and is capable of considering uncertainties in speed profiles. The algorithm runs independently onboard each vehicle and, upon detection of a possible collision, replans its trajectory. Under a few assumptions, the modified trajectory is guaranteed to avoid the predicted collision as well as satisfy mission specific constraints. The deviations in position, velocity, and acceleration caused by the avoidance maneuver are small and respect bounds that can be computed offline. These bounds can be used during the mission-planning phase to guarantee satisfaction of vehicle dynamic constraints and intervehicle safety distances even during collision-avoidance maneuvers.
Dependencies for Graphs

What are graph dependencies? What do we need them for? What new challenges do they introduce? This article tackles these questions. It aims to incite curiosity and interest in this emerging area of research.
UAVs Fleet Mission Planning Subject to Weather Fore-Cast and Energy Consumption Constraints

The problems of mission planning for UAVs fleets are subject of intensive research. Their roots go back to the well-known extensions of VRP addressing the routing and scheduling of UAVs to deliver goods from a depot to customer locations. Rising expectations following the new outdoor applications besides seamless flow routing constraints forces to consider other aspects such as the weather forecast and energy consumption. In that context, this research concerns a declarative framework enabling to state a model aimed at the analysis of the relationships between the structure of a given UAVs driven supply network and its behavior resulting in a sequence of submissions following a required delivery. Because of the Diophantine character of the considered model the main question concerns its solvability. The provided illustrative example shows an approach leading to sufficient conditions guaranteeing solutions existence and as a consequence providing requirements for a solvable class of UAV driven mission planning problems.
Workflow Scheduling in the Cloud With Weighted Upward-Rank Priority Scheme Using Random Walk and Uniform Spare Budget Splitting

We study a difficult problem of how to schedule complex workflows with precedence constraints under a limited budget in the cloud environment. We first formulate the scheduling problem as an integer programming problem, which can be optimized and used as the baseline of performance. We then consider the traditional approach of scheduling jobs in a prioritized order based on the upward-rank of each job. For those jobs with no precedence constraints among themselves, the plain upward-rank priority scheme assigns priorities in an arbitrary way. We propose a job prioritization scheme that uses the Markovian chain stationary probabilities as a measure of the importance of jobs. The scheme keeps the precedence order for the jobs that have precedence constraints between each other and assigns priorities according to the jobs' importance for the jobs without precedence constraints. We finally design a uniform spare budget-splitting strategy that splits the spare budget uniformly across all the jobs. We test our algorithms on a variety of workflows, including the Fast Fourier transform (FFT), the Gaussian elimination, typical scientific workflows, randomly generated workflows, and workflows from an in-production cluster of an online streaming service company. We compare our algorithms with state-of-the-art algorithms. The empirical results show that the uniform spare budget splitting scheme outperforms the splitting scheme in proportion to extra demand on average for most cases, and the Markovian-based prioritization further improves the workflow makespan.
A learning to rank approach for cross-language information retrieval exploiting multiple translation resources

Abstract Cross-language information retrieval (CLIR), finding information in one language in response to queries expressed in another language, has attracted much attention due to the explosive growth of multilingual information in the World Wide Web. One important issue in CLIR is how to apply monolingual information retrieval (IR) methods in cross-lingual environments. Recently, learning to rank (LTR) approach has been successfully employed in different IR tasks. In this paper, we use LTR for CLIR. In order to adapt monolingual LTR techniques in CLIR and pass the barrier of language difference, we map monolingual IR features to CLIR ones using translation information extracted from different translation resources. The performance of CLIR is highly dependent on the size and quality of available bilingual resources. Effective use of available resources is especially important in low-resource language pairs. In this paper, we further propose an LTR-based method for combining translation resources in CLIR. We have studied the effectiveness of the proposed approach using different translation resources. Our results also show that LTR can be used to successfully combine different translation resources to improve the CLIR performance. In the best scenario, the LTR-based combination method improves the performance of single-resource-based CLIR method by 6% in terms of Mean Average Precision.
A gamma-signal-regulated connected components labeling algorithm

This paper proposes a novel connected components labeling (CCL) approach that introduces a gamma signal to record certain mask pixels’ values to eliminate duplicated pixel checking and regulate the labeling process for higher efficiency. A new block-based two-scan CCL algorithm, Eight-Connected Gamma-Signal-regulated (ECGS) algorithm, is designed and developed by applying this approach to evaluate a block of 2 × 2 pixels (with just 6 mask pixels) in each iteration such that the total number of operations is considerably reduced and the labeling efficiency is significantly improved. The experiments conducted on a public benchmark, YACCLAB (Yet Another Connected Components Labeling Benchmark), have demonstrated that the proposed ECGS algorithm can outperform current state-of-the-art CCL algorithms for a number of digital images.
NAuth: Secure Face-to-Face Device Authentication via Nonlinearity

With the increasing prevalence of mobile devices, face-to-face device-to-device (D2D) communication has been applied to a variety of daily scenarios such as mobile payment and short distance file transfer. In D2D communications, a critical security problem is verifying the legitimacy of devices when they share no secrets in advance. Previous research addressed the problem with device authentication and pairing schemes based on user intervention or exploiting physical properties of the radio or acoustic channels. However, a remaining challenge is to secure face-to-face D2D communication even in the middle of a crowd, within which an attacker may hide. In this paper, we present Nhuth, a nonlinearity-enhanced, location-sensitive authentication mechanism for such communication. Especially, we target at the secure authentication within a limited range such as 20 cm, which is the common case for face-to-face scenarios. Nhuth contains averification scheme based on the nonlinear distortion of speaker-microphone systems and a location-based-validation model. The verification scheme guarantees device authentication consistency by extracting acoustic nonlinearity patterns (ANP) while the validation model ensures device legitimacy by measuring the time difference of arrival (TDOA) at two microphones. We analyze the security of Nhuth theoretically and evaluate its performance experimentally. Results show that Nhuth can verify the device legitimacy in the presence of nearby attackers.
Novel Belief Propagation Algorithm for Stereo Matching With a Robust Cost Computation

Stereo matching is a challenging problem in the field of computer vision and has recently received extensive attention.However, the traditional methods are labor intensive and premised on specific conditions.In this paper, we propose a robust stereo matching cost algorithm that relies on refined features extracted by a stack auto-encoder.These features are robust for different types of image pairs, which significantly improve the generality of the proposed matching algorithm.In addition, we smoothed the belief volume with a guided filter to improve the performance of the belief propagation algorithm on edge regions.To deal with the time consumption issue of the standard belief propagation algorithm, we also proposed a constraint method to speed it up.This constraint does not degrade the matching results and can easily be generalized to other message passing algorithms.The experiments conducted on a Middlebury benchmark dataset demonstrate the effectiveness of the proposed algorithms.
Engaging Users with Educational Games

Phishing continues to be a difficult problem for individuals and organisations. Educational games and simulations have been increasingly acknowledged as versatile and powerful teaching tools, yet little work has examined how to engage users with these games. We explore this problem by conducting workshops with 9 younger adults and reporting on their expectations for cybersecurity educational games. We find a disconnect between casual and serious gamers, where casual gamers prefer simple games incorporating humour while serious gamers demand a congruent narrative or storyline. Importantly, both demographics agree that educational games should prioritise gameplay over information provision -- i.e. the game should be a game with educational content. We discuss the implications for educational games developers.
myDIG: Personalized Illicit Domain-Specific Knowledge Discovery with No Programming

With advances in machine learning, knowledge discovery systems have become very complicated to set up, requiring extensive tuning and programming effort. Democratizing such technology so that non-technical domain experts can avail themselves of these advances in an interactive and personalized way is an important problem. We describe myDIG, a highly modular, open source pipeline-construction system that is specifically geared towards investigative users (e.g., law enforcement) with no programming abilities. The myDIG system allows users both to build a knowledge graph of entities, relationships, and attributes for illicit domains from a raw HTML corpus and also to set up a personalized search interface for analyzing the structured knowledge. We use qualitative and quantitative data from five case studies involving investigative experts from illicit domains such as securities fraud and illegal firearms sales to illustrate the potential of myDIG.
State and runtime-aware scheduling in elastic stream computing systems

State and runtime-aware scheduling is one of the problems that is hard to resolve in elastic big data stream computing systems, as the state of each vertex is different, and the arrival rate of data streams fluctuates over time. A state and runtime-aware scheduling framework should be able to dynamically adapt to the fluctuation of the arrival rate of data streams and be aware of vertex states and resource availability. Currently, there is an increasing number of research work focusing on application scheduling in stream computing systems, however, this problem is still far from being completely solved. In this paper, we focus on the state of vertex in applications and the runtime feature of resources in a data center, and propose a state and runtime-aware scheduling framework (Sra-Stream) for elastic streaming computing systems, which incorporates the following features: (1) Profiling mathematical relationships between the system response time and the arrival rate of data streams, and identifying relevant resource constraints to meet the low response time and high throughput objectives. (2) Classifying vertex into stateless vertex or stateful vertex from a quantitative perspective, and achieving vertex parallelization by considering the state of the vertex. (3) Demonstrating a proposed stream application scheduling scheme consisting of a modified first-fit based runtime-aware data tuple scheduling strategy at the initial stage, and a maximum latency-sensitive based runtime-aware data stream scheduling strategy at the online stage, by considering the current scheduling status of the application. (4) Evaluating the achievement levels of low response time and high throughput objectives in a real-world elastic stream computing system. Experimental results conclusively demonstrate that the proposed Sra-Stream provides significant performance improvements on achieving the low system response time and high system throughput.
A quest for web search optimisation: an evidence-based approach to trainee translators’ behaviour

Many translation textbooks consist of chapters dedicated to web resources and web search in general. However, surprisingly little is known about how (trainee) translators actually conduct web search. The present study, therefore, adopts an evidence-based approach to empirically examine how the web is used by trainee translators, particularly for terminology purposes. Unlike previous studies that focus on translators’ idiosyncrasies and styles, this study taps into the idea of web search optimisation. In other words, it aims to depict what characterises a more optimal web search for translation and asks about what (strategic) web search process trainee translators could engage in order to optimise their search results. Eighteen postgraduate translation students were asked to translate three texts on three separate occasions. Their translation and web search processes were screen-recorded and partially verbalised. Data suggest that more optimal web search is characterised by an ‘explorer's approach’ and more profound engagement with ‘secondary actions’ rather than ‘primary actions’.