Reviewerly/exHarmony huggingface dataset:
An Application of the INFINITE framework in a Human-Agent Negotiation Competition

We discuss an application of the INFINITE negotiation architecture for developing agents that can negotiate with others while representing its user's preferences. We developed an agent, Draft Agent, that was entered into the 2019 Human-Agent league (HAL) of the Autonomous Negotiating Agent Competition (ANAC). We discuss Draft Agent's performance, highlighting where it worked well and aspects that can be further improved. A key feature of Draft Agent is the use of an alternate-issue-selection protocol to model the opponent's preference structure. The learnt preferences are then used to propose a fair, and where possible, win-win deal. Though this approach allows Draft Agent to obtain relatively high individual as well as joint utility, it might be considered somewhat rigid by human users and hence scores comparatively low on the likeability scale. We present a detailed analysis of the comparative performance of Draft Agent and the competing finalists of the HAL competition. We also suggest some options to further improve Draft Agent's performance and likeability.
Method for Identifying Abnormal Access to Sensitive Data Based on Network Flow

Abstract With the increasing scale and quantity of civil aviation information system, the data carried in the information system is becoming more and more important and sensitive. In the face of a large number of information systems, sorting out and monitoring whether passenger information in the information system has been accessed in violation of regulations is an urgent problem in the civil aviation industry. In this paper, the civil aviation business system is deeply studied, and a method of identifying abnormal access to sensitive data based on network traffic is proposed. Through the collection and analysis of network traffic, and then identify the sensitive information in the network traffic, and use k-means clustering method to classify the access behavior of sensitive information. Finally, combined with expert experience, the malicious access behavior for sensitive data is accurately identified.
Detecting Emergent Behavior in Scenario-Based Specifications using a Probabilistic Model

Verification of requirements documents is crucial for developing quality software products. Scenarios are powerful tools for requirements elicitation. Manual verification of the requirements documents is a tedious job. This paper presents an automated approach to detect emergent behaviour in scenario-based specifications using a probabilistic model. Emergent behaviours are the unexpected behaviours in software specifications that are not easily visible in the design documents but may appear during execution and cause risk hazards after the implementation. In this paper, we define the interactions between system components as a sequence of words in a sentence and predict the possible violation in the execution order using probabilities. We have demonstrated our approach with an illustrative example. Three different case studies are provided to manifest the efficiency of our approach.
Relevance of cryptomarkets, characteristics of purchasers and possibilities for survey research: experience from the European Web Survey on Drugs

Purpose The purpose of this paper is to assess the relevance of cryptomarkets, characteristics of purchasers and possibilities for survey research by approaching users directly on cryptomarkets. Design/methodology/approach Cross-country comparison of the results from the European Web Survey on Drugs (EWSD) and summarizing lessons learned during the data collection was carried out. Findings Purchasers of drugs on cryptomarkets are still a rather small segment of all drug purchasers, and most people who use cryptomarkets also use other sources of supply to buy drugs. The percentage of people using cryptomarkets is unevenly distributed across countries and substances. Purchasers on cryptomarkets in most countries are more likely to be men and more likely, on average, to use more substances. Other characteristics such as age or place of residence do not show a consistent pattern across countries. Recruitment of respondents on cryptomarkets calls for specific techniques and procedures. Specific attention should be paid to build trust and guarantee credibility and anonymity. Research limitations/implications Interpretation of the quantitative results is limited by nonprobabilistic sampling and different recruitment strategies in different countries. Practical implications Users of cryptomarkets show some specific characteristics, providing a challenge for research and prevention agencies to keep up with digital technology. Increasing knowledge about characteristics of users of cryptomarkets may help to create adequate responses for harm reduction measures in different supply settings. However, collecting self-reported data from users on cryptomarkets is limited owing to significant privacy concerns and requires specific skills and strategies. Originality/value The EWSD provides a rare opportunity for detailed analyses of consumption patterns and characteristics of active drug users across several European countries. Furthermore, experiences of a new recruitment strategy are discussed.
The Black-White Pixels Ratio in Medial Temporal Lobe Brain Structure in Transcranial B-Images as a Measurable Marker of Alzheimer’s Disease Probability: The Reproducibility Overview

The objective of the presented study is to introduce analysis the black-white pixels ratio in medial temporal lobe (MTL) brain structure in digitized transcranial B-images using our developed software B-MODE Assist. This ratio could be a significant marker related to the echogenicity grade. In B-image, the MTL is stable anechogenic structure but increased echogenicity of surrounding cortex is detectable in the case of MTL atrophy; the atrophy predicts Alzheimer’s Disease. Within this study, a set of 97 B-images is used. There are two key goals of this pilot study; verify the reproducibility of the ratio between two observers and to show that the ratio is not much different in the case of original image in comparison with a magnified segment and using constant brightness increasing; the ratio should be independent on image resolution and brightness. To select medial temporal lobe structure, a 20mm square-shaped Region of Interest is used. The correlation coefficient r=0.69 was achieved between two independent, non-experienced observers. The error does not exceed 28 % between 2 non-experienced observers and does not exceed 12 % between the measurements from the same observer. Also, the variance and the range were computed. Achieved results have been judged by an experienced sonographer. In the future, the ratio could be an effective value to determine the MTL atrophy based on the ratio value and possibility to create a rule-based expert system which should help evaluate probability of Alzheimer’s Disease based on the cortex echogenicity grade. In general, this approach could be a reliable way to detect risk of Alzheimer’s Disease using neurosonological imaging.
Batch-wise Regularization of Deep Neural Networks for Interpretability

Fast progress in the field of Machine Learning and Deep Learning strongly influences the research in many application domains like autonomous driving or health care. In this paper, we propose a batch-wise regularization technique to enhance the interpretability for deep neural networks (NN) by means of a global surrogate rule list. For this purpose, we introduce a novel regularization approach that yields a differentiable penalty term. Compared to other regularization approaches, our approach avoids repeated creating of surrogate models during training of the NN. The experiments show that the proposed approach has a high fidelity to the main model and also results in interpretable and more accurate models compared to some of the baselines.
A decentralized approach for discovering runtime software architectural models of distributed software systems

Runtime software architectural models of self-adaptive systems are needed for making adaptation decisions in architecture-based self-adaptive systems. However, when these systems are distributed and highly dynamic, there is an added need to discover the system's software architecture model at runtime. Current methods of runtime architecture discovery use a centralized approach, in which the process is carried out from a single location. These methods are inadequate for large distributed software systems because they do not scale up well and have a single point of failure. This paper describes DeSARM (Decentralized Software Architecture discoveRy Mechanism), a completely decentralized and automated approach, based on gossiping and message tracing, for runtime discovery of software architecture models of distributed software systems. DeSARM is able to identify at runtime important architectural characteristics such as components and connectors, in addition to synchronous and asynchronous communication patterns. Furthermore, through its use of gossiping, DeSARM exhibits the properties of scalability, global consistency among participating nodes, and resiliency to failures. This paper demonstrates DeSARM's properties and answers key research questions through experimentation with software architectures of varying sizes and complexities executing within a computer cluster. DeSARM enables the decentralized discovery of runtime software architectural models of distributed software systems while exhibiting the properties of scalability, global consistency among participating nodes and resiliency to failures. Scalability is achieved through DeSARM's ability to successfully discover software architectures of increasing sizes and complexities executing across node counts of increasing sizes. Global consistency among participating nodes is achieved by each node within the system discovering the complete software architecture independently but in coordination with each other. Finally, resiliency to failures is achieved by DeSARM successfully discovering the software architecture of the system in the presence of component failures.
GAN-Based Planning Model in Deep Reinforcement Learning

Deep reinforcement learning methods have achieved unprecedented success in many high-dimensional and large-scale space sequential decision-making tasks. In these methods, model-based methods rely on planning as their primary component, while model-free methods primarily rely on learning. However, the accuracy of the environmental model has a significant impact on the learned policy. When the model is incorrect, the planning process is likely to compute a suboptimal policy. In order to get a more accurate environmental model, this paper introduces the GAN-based Planning Model (GBPM) exploiting the strong expressive ability of Generative Adversarial Net (GAN), which can learn to simulate the environment from experience and construct implicit planning. The GBPM can be trained using real transfer samples experienced by the agent. Then, the agent can utilize the GBPM to produce simulated experience or trajectories so as to improve the learned policy. The GBPM can act as a role for experience replay so that it can be applied to both model-based and model-free methods, such as Dyna, DQN, ACER, and so on. Experimental results indicate that the GBPM can improve the data efficiency and algorithm performance on Maze and Atari 2600 game domain.
Ensuring Resource Availability with MRU/FRU Caching: A Dew-Blockcloud Model

Combining the blockchain and cloud computing systems in different ways in order to address computing issues such as decentralization and security has slowly become a norm in recent times. These Block-Cloud model(s), which are becoming prominent in the literature seem to ensure integrity in the Cloud but lacks in exhibiting availability of resources, which is essential for security. In this study, a dew-blockcloud model is designed to address this problem. We propose a hybrid MRU/FRU caching algorithm for the dew server that reduces the number of repeated cloud accesses while maintaining relevant updated data. Using existing literature and postulation we show that dew computing might be the best availability system for the blockcloud model.
Exceptional in so Many Ways—Discovering Descriptors That Display Exceptional Behavior on Contrasting Scenarios

The current state of the art in supervised descriptive pattern mining is very good in automatically finding subsets of the dataset at hand that are exceptional in some sense. The most common form, subgroup discovery, generally finds subgroups where a single target variable has an unusual distribution. Exceptional model mining (EMM) typically finds subgroups where a pair of target variables display an unusual interaction. What these methods have in common is that one specific exceptionality is enough to flag up a subgroup as exceptional. This, however, naturally leads to the question: can we also find multiple instances of exceptional behaviour simultaneously in the same subgroup? This paper provides a first, affirmative answer to that question in the form of the SPEC (Subsets of Pairwise Exceptional Correlations) model class for EMM. Given a set of predefined numeric target variables, SPEC will flag up subgroups as interesting if multiple target pairs display an unusual rank correlation. This is a fundamental extension of the EMM toolbox, which comes with additional algorithmic challenges. To address these challenges, we provide a series of algorithmic solutions whose strengths/flaws are empirically analysed.
Interpolation based Anime Face Style Transfer

This paper presents a new post-processing method for transferring human face to anime style with geometry factors. This is a branch of image-to-image problem, which is attracting the attention of researchers recently. Existing methods just deal with either texture transfer or geometry transfer. They still do not keep the signatures of human face. Moreover, previous methods do not allow users to control output image with some specific factors. This thing makes these approaches not user-friendly. To address these issues, we propose a system which leverages the advantages of Generative Adversarial Networks model and Thin Plate Spline algorithm for face morphing. The generative model is trained on selfie2anime dataset including anime and human face images. Another advantage of this approach is that it does not require paired images. We evaluate our system on 3400 images resized to 256×256 for each domain. Experimental results illustrate that our method is able to generate anime faces, which have geometry factor associating with human faces.
Mathematical Modeling and Optimal Inference of Guided Markov-Like Trajectory

A trajectory of a destination-directed moving object (e.g. an aircraft from an origin airport to a destination airport) has three main components: an origin, a destination, and motion in between. We call such a trajectory that end up at the destination destination-directed trajectory (DDT). A class of conditionally Markov (CM) sequences (called CM <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</inf> ) has the following main components: a joint density of two endpoints and a Markov-like evolution law. A CM <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</inf> dynamic model can describe the evolution of a DDT but not of a guided object chasing a moving guide. The trajectory of a guided object is called a guided trajectory (GT). Inspired by a CM <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</inf> model, this paper proposes a model for a GT with a moving guide. The proposed model reduces to a CM <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</inf> model if the guide is not moving. We also study filtering and trajectory prediction based on the proposed model. Simulation results are presented.
An Evolutionary Approach to Automatic Keyword Selection for Twitter Data Analysis

In this paper, we propose an approach to intelligent and automatic keyword selection for the purpose of Twitter data collection and analysis. The proposed approach makes use of a combination of deep learning and evolutionary computing. As some context for application, we present the proposed algorithm using the case study of public health surveillance over Twitter, which is a field with a lot of interest. We also describe an optimization objective function particular to the keyword selection problem, as well as metrics for evaluating Twitter keywords, namely: reach and tweet retreival power, on top of traditional metrics such as precision. In our experiments, our evolutionary computing approach achieved a tweet retreival power of 0.55, compared to 0.35 achieved by the baseline human approach.
Affordances and Pedagogical Implications of Augmented Reality (AR)-Integrated Language Learning

Technologies have permeated the field of language education in recent decades. Language education has been informed by the technology-enhanced practices. Researchers have exhausted a variety of technologies and technological tools in the field of language education. Augmented reality (AR) is one of the emerging technologies that has been exploited in both education and language education. However, it is reported that educators' lack of knowledge and confidence in employing emerging technologies such as AR are limiting the use of these technologies in language education. To dive into the employed AR-related practices in language education, this chapter will (1) introduce the definition of AR, (2) discuss the underlying theories undergirding AR-integrated language learning, (3) present both the affordances and thorny issues accompanied with AR, and lastly, (4) share pedagogical implications of AR-integrated language learning to inform and usher the practices of language educators.
An intelligent recommendation algorithm for red team strategy in edge computing powered massive Cyber Defense Exercise

The recent surge in the frequency and seriousness of cyber attacks is alarming and poses a critical threat against the stability of our society. Previously, most effort to mitigate cyber attacks has focused on the technical countermeasures. However, a number of recent cyber attacks showed the necessity of constantly offering proper massive Cyber Defense eXercise (CDX) to the workforce in a timely manner. In order to meet the ever growing demand, the most recent massive CDX platform utilizes various edge computing concepts to locally manage the overhead related to the trainees (blue team members) in real time unlike the traditional centralized CDX platform. So far, such massive CDX platform cannot be fully operational without sufficient number of qualified trainers (red team members) who have strong expertise in cyber offense and are willing to participate the CDX. Unfortunately, securing enough number of such red team members is greatly challenging in practice. To address this issue, this paper introduces an intelligent recommendation algorithm for the red team in a massive CDX so that such massive CDX can be organized without enough number of red team members with a strong expertise in cyber offense. Given a known attack graph for each cyber defense training module, we formally define the problem of identifying a subgraph including a victorious strategy for the red team as the victory subgraph computation problem. Then, we introduce a new algorithm to solve this problem as well as a new strategy to obtain a winning strategy for the offense team to assist such red team members. Besides, we also discuss about various approach to utilize our result to organize massive CDXs in an efficient manner.
Framework for Task Allocation in Global Software Development

The Global Software Development (GSD) promises high-quality software at low cost. It enables round-the-clock development to achieve maximum production in a short period by utilizing expertise around the globe. GSD is only possible if tasks are effectively distributed among sites to ensure smooth development. Therefore, one of the key challenges of GSD is designing a task allocation (TA) strategy. The main objective of the present research is to develop a framework that takes into account important factors, while allocating tasks to distributed sites involved in GSD. The current allocation in plan-based software development is done on ad-hoc basis and does not follow any systematic approach or framework. The framework facilitates decision-makers in allocation of tasks in a manner that controls delay and re-allocation. The study uses a mixed method approach, where the data used to create the framework is acquired via an industrial survey (58 participants) and interviews (10 participants) with GSD practitioners. The developed task allocation framework is validated with the help of an online focus group with participants (7 participants) from around the globe. The ability of the framework to be applicable in real-world scenarios is assessed from the feedback of industry practitioners. They have highlighted the usefulness of the framework to both, practitioners involved in task allocation decision as well as researchers working in the area. The automation and validation of the framework in real-world GSD scenarios is part of future work of this research.
Implicit requirements for ontological multi-level types in the UNICLASS classification

In the multi-level type modeling community, claims that most enterprise application systems use ontologically multi-level types are ubiquitous. To be able to empirically verify this claim one needs to be able to expose the (often underlying) ontological structure and show that it does, indeed, make a commitment to multi-level types. We have not been able to find any published data showing this being done. From a top-level ontology requirements perspective, checking this multi-level type claim is worthwhile. If the datasets for which the top-level ontology is required are ontologically committed to multi-level types, then this is a requirement for the top-level ontology. In this paper, we both present some empirical evidence that this ubiquitous claim is correct as well as describing the process we used to expose the underlying ontological commitments and examine them. We describe how we use the bCLEARer process to analyse the UNICLASS classifications making their implicit ontological commitments explicit. We show how this reveals the requirements for two general ontological commitments; higher-order types and first-class relations. This establishes a requirement for a top-level ontology that includes the UNICLASS classification to be able to accommodate these requirements. From a multi-level type perspective, we have established that the bCLEARer entification process can identify underlying ontological commitments to multi-level type that do not exist in the surface linguistic structure. So, we have a process that we can reuse on other datasets and application systems to help empirically verify the claim that ontological multi-level types are ubiquitous.
Domain Specific Factoid Question Answering by Regular Expression Generation

Answering questions through question answering (QA) system in natural language is highly acknowledged. Since 1960s, Artificial Intelligence (AI) supporting system is widely combined with QA. Different research work is spreading out for developing more efficient, time and space moderated way. QA can be done in three different ways: with the help of database, pattern matching and regular expression (RE). Database QA system depends on the availability of information in the database. Pattern matching needs mounding of patterns. RE can perform the task with the help of some RE patterns only. Our concern is doing QA on a specific domain Bangladesh Railway, using RE generation. Pattern matching with RE makes it easier to find a few needed strings from a big sentence or a set of strings. Answering in natural language after knowing its question pattern and mapping with accurate answer with the help of RE make our system efficient one.