Reviewerly/exHarmony huggingface dataset:
An Approach for Formal Verification of Machine Learning based Complex Systems

Abstract A complex system is characterized by emergence of global properties which are very difficult, if not impossible, to anticipate just from complete knowledge of component behaviors. Emergence, hierarchical organization and numerosity are some of the characteristics of complex systems. With increasing system complexity, achieving confidence in systems becomes increasingly difficult. With the recent trend towards significant footprint of complex system's functionality being governed by machine learning based models and algorithms, there is a need to ensure that emergent behavior associated with such systems are well analyzed and understood. Traditional verification and validation approaches are often inadequate to bring in the nuances of potential emergent behavior, which may be positive or negative. This paper describes a novel approach towards application of formal methods for analyzing and evaluating emergent behavior of complex systems that are governed by machine learning models. The proposed approach involves developing a machine learning classifier model that learns on potential negative and positive emergent behaviors, and leveraging the classifier in a formal verification model checking environment to assert negative emergent behavior. The approach is illustrated through an example of a pitch control system of an aircraft. The effectiveness and performance of the approach are quantified.
Virtual Reality Serious Game Prototype for Presenting Military Units

In recent years, the popularity of serious games is increasing due to ability to learn by doing and practicing different skills by using game-based mechanisms. At the same time, virtual reality (VR) games are making a comeback. Military organizations use both serious games and VR to train users on how to fire a gun or to help soldiers with posttraumatic stress disorder treatment. The paper presents serious game enabling cadets to be better acquainted with specific military unit before deciding which one to enroll in during their military engineering study. The VR serious game prototype is developed in Unity game engine for Oculus Rift headset and Leap Motion sensor device. The main goal of the prototype is to analyze the potential of serious games application in the education of cadets of the military engineering study as well as to evaluate their satisfaction. Prototype scenes present chosen military units. Users can navigate through the scenes and interact with the virtual world using their hands. The prototype is developed in the cooperation of cadets and students of electrical engineering and computing. In addition, based on the user satisfaction questionnaires results, a conclusion has been drawn that virtual reality has great potential for military education.
Fusing Level and Ruleset Features for Multimodal Learning of Gameplay Outcomes

Which features of a game influence the dynamics of players interacting with it? Can a level's architecture change the balance between two competing players, or is it mainly determined by the character classes and roles that players choose before the game starts? This paper assesses how quantifiable gameplay outcomes such as score, duration and features of the heatmap can be predicted from different facets of the initial game state, specifically the architecture of the level and the character classes of the players. Experiments in this paper explore how different representations of a level and class parameters in a shooter game affect a deep learning model which attempts to predict gameplay outcomes in a large corpus of simulated matches. Findings in this paper indicate that a few features of the ruleset (i.e. character class parameters) are the main drivers for the model's accuracy in all tested gameplay outcomes, but the levels (especially when processed) can augment the model.
Feature Selection Algorithm Using Relative Odds for Data Mining Classification

A filter feature selection algorithm is developed and its performance tested. In the initial step, the algorithm dichotomizes the dataset then separately computes the association between each predictor and the class variable using relative odds (odds ratios). The value of the odds ratios becomes the importance ranking of the corresponding explanatory variable in determining the output. Logistic regression classification is deployed to test the performance of the new algorithm in comparison with three existing feature selection algorithms: the Fisher index, Pearson's correlation, and the varImp function. A number of experimental datasets are employed, and in most cases, the subsets selected by the new algorithm produced models with higher classification accuracy than the subsets suggested by the existing feature selection algorithms. Therefore, the proposed algorithm is a reliable alternative in filter feature selection for binary classification problems.
Community Detection with Side Information via Semidefinite Programming

Semidefinite programming is known to be both efficient and asymptotically optimal in solving community detection problems, but it has been studied in this context only when observations are purely graphical in nature. In this paper, we extend the use of semidefinite programming in community detection to observations that have both a graphical and a nongraphical component. We consider the binary censored block model with n nodes and study the effect of partially revealed labels on the performance of semidefinite programming. We address the question: do partially revealed labels help the semidefinite programming solution as much as they help the maximum likelihood solution? Our results are twofold. First, we show that partially revealed labels change the phase transition of exact recovery if and only if the information they provide grows no slower than Ω(log(n)). Second, we show that the semidefinite programming relaxation of maximum likelihood can achieve exact recovery down to the optimal threshold under partially revealed labels.
Thematic mapping of cloud computing based on a systematic review: a tertiary study

Purpose A large number of systematic reviews (SRs) studies have been performed in the cloud computing field, demonstrating miscellaneous outcomes and utilizing different approaches. Accordingly, a meta-review of cloud SRs is needed to appraise the results of such studies and create an integrated understanding. The paper aims to discuss these issues. Design/methodology/approach A tertiary study was conducted using a systematic method to analyze SRs including two stages: searching and screening the SRs and thematic synthesis of results. As a qualitative data management tool, Nvivo software was used to support the research process, for data coding and synthesis. Findings First, by searching electronic sources between the year of 2011–2016, out of a total of 142 identified articles, 94 articles were included according to pre-determined criteria, of which 76 articles were approved after qualitative evaluation. In the second stage, identifying the research themes, a map of the concepts and issues related to each theme was drawn up. The analysis shows that the quality of articles has improved but can be further enhanced using methodological guidelines as well as supporting tools. The research has focused more on the technical aspect, although there is an equal demand for synthesizing of cloud governance concepts. Originality/value This is the first tertiary study which presents the main research themes and concepts of cloud SRs in form of thematic maps by using the thematic synthesis and SR methods. This paper also provides some recommendations to improve reviews after evaluating the quality of papers. This study can support reviewers for future SRs in the field and also helps practitioners and managers to have a better understanding of different aspects of cloud computing.
The Swiss Postal Voting Process and Its System and Security Analysis

The Swiss postal voting system builds on trust in governmental authorities and external suppliers. The federal structure of Switzerland of cantons and municipalities leads to a distributed architecture. Detailed information on the current postal voting procedure are manifested as implicit knowledge within fragmented institutions and are not easily accessible. This work serves (i) as an overview of the Swiss remote postal voting system, (ii) a detailed insight into the process flow, and (iii) a respective risk assessment.
Securing keystroke dynamics from replay attacks

Keystroke dynamics is a viable behavioral biometric technique for identity verification based on users' keyboard interaction traits. Keystroke dynamics can help prevent credentials from being abused in case of theft or leakage. But what happens if the keystroke events are eavesdropped and being replayed? Attackers that intercept keystroke dynamics authentication sessions of benign users can easily replay them from other sources unchanged or with minor changes and gain illegitimate privileges. Hence, even with its major security advantages, keystroke dynamics can still expose authentication mechanisms to replay attacks. Although replay attack is one of the oldest techniques to manipulate authentication systems, keystroke dynamics does not help preventing it. We suggest a new protocol for dynamics exchange based on choosing a subset of real and fake information snippets shared between the client and service providers to lure potential attackers. We evaluated our method on four state-of-the-art keystroke dynamics algorithms and three publicly available datasets and showed that we can dramatically reduce the possibility of replay attacks while preserving highly accurate user verification.
Monster Carlo 2: Integrating Learning and Tree Search for Machine Playtesting

We describe a machine playtesting system that combines two paradigms of artificial intelligence - learning and tree search - and intends to place them in the hands of independent game developers. This integration approach has shown great success in Go-playing systems like AlphaGo and AlphaZero, but until now has not been available to those outside of artificial intelligence labs. Our system expands the Monster Carlo machine playtesting framework for Unity games by integrating its tree search capabilities with the behavior cloning features of Unity's Machine Learning Agents Toolkit. Because experience gained in one playthrough may now usefully transfer to other playthroughs via imitation learning, the new system overcomes a serious limitation of the older one with respect to stochastic games (when memorizing a single optimal solution is ineffective). Additionally, learning allows search-based automated play to be bootstrapped from examples of human play styles or even from the best of its own past experiences. In this paper we demonstrate that our framework discovers higher-scoring and more-representative play with minimal need for machine learning or search expertise.
Exploring Potentials and Challenges of Blockchain-based Public Key Infrastructures

Traditional public key infrastructures (PKIs), in particular, X.509 and PGP, is plagued by security and usability issues. As reoccurring incidents show, these are not only of theoretical nature but allow attackers to inflict severe damage. Emerging blockchain technology allows for advances in this area, facilitating a trustless immutable ledger with fast consensus. There have been numerous proposals for utilization of the blockchain in the area of PKI, either as extensions upon existing methods or independent solutions. In this paper, we first study traditional PKI, then proceed with novel approaches, showing how they can improve upon recent issues. We provide a comprehensive evaluation, finding that independent blockchain-based solutions are preferable in the future, mainly due to their stronger security. However, global adoption of these yet requires advances in blockchain development, e.g., concerning scalability.
Irregular Product Coded Computation for High-Dimensional Matrix Multiplication

In this paper, we consider the straggler problem of the high-dimensional matrix multiplication over distributed workers. To tackle this problem, we propose an irregular-product-coded computation, which is a generalized scheme of the standard-product-coded computation proposed in [1]. Introducing the irregularity to the product-coded matrix multiplication, one can further speed up the matrix multiplication, enjoying the low decoding complexity of the product code. The idea behind the irregular product code introduced in [2] is allowing different code rates for the row and column constituent codes of the product code. We provide a latency analysis of the proposed irregular-product-coded computation. In terms of the total execution time, which is defined by a function of the computation time and decoding time, it is shown that the irregular-product-coded scheme outperforms other competing schemes including the replication, MDS-coded and standard-product-coded schemes in a specific regime.
A Hybrid Approach to Service Recommendation Based on Network Representation Learning

Network representation learning has attracted much attention as a new learning paradigm to embed network vertices into a low-dimensional vector space, by preserving network information.In this paper, in the light of user co-tag network and social network, we introduced network representation learning techniques into the learning of user preference, to encode user social relations into a continuous vector space.First, we proposed a hybrid network representation learning approach to effectively utilize users' tagging and social relationships, and then we took it for service recommendation.The experimental results show that, compared with four baselines on two public data sets, the improvement ratio over the baselines is up to 50% in terms of Recall@10 and Precision@10 and the improvement is even more than 90% in terms of NDGG@10 and MRR@10.
Utilizing Argument Mining Techniques for Argumentative Dialogue Systems

This work presents a pilot study for the application of argument mining techniques in the context of argumentative Dialogue Systems. Rach, Niklas We extract the argument structure of an online debate and show how it can be utilized to generate artificial persuasive dialogues in an agent-agent scenario. Langhammer, Saskia The interaction between the agents is formalized as argument game and the resulting artificial dialogues are evaluated in a user study by comparing them to human generated ones. Minker, Wolfgang The outcomes indicate that the artificial dialogues are logically consistent and thus show that the use of the employed argument annotation scheme was successful. Ultes, Stefan
Enhanced Privacy Preservation Using Anonymization in IOT-Enabled Smart Homes

Nowadays, Internet of Things (IOT) attracted researchers due to its smart features. Idea of IOT is to serve best to system users. Data get stored on things, locally on devices and on cloud. Security is the major issue here due to the nature of cloud computing, which opens challenges to researchers. Privacy preserving is a major issue in such things due to cloud services. In this paper, we articulate how Internet of Things works with cloud and discusses challenges of privacy issues in IOT. It also throws light on how IOT is best suited for home automation and work on its main pitfall that is security for smart things. Out paper mainly focuses on privacy preservation challenges and ways to overcome it. However, IOT eliminates person to machine interaction and makes system smart with device-to-device interaction, privacy of this interaction is still not completely guaranteed. Based on this concept, we have designed a software framework, which works in three ways as: privacy preservation, automation, and then fault reporting. Privacy would be provided using the concept of tokenization for anonymity because currently anonymity of users is not preserved by communication technologies. This software framework is evaluated by some case studies and with reference to previous researches. Results of this work surely intercalate new concept in an area of cloud security and Internet of Things, which would provide future research directions.
Video Game Description Language Environment for Unity Machine Learning Agents

This paper introduces UnityVGDL, a port of the Video Game Description Language (VGDL) to the widely used Unity game engine. Our framework is based on the General Video Game AI (GVGAI) competition framework and implements its core ontology, including a forward model. It integrates the Unity Machine Learning Agents (ML-Agents) toolkit with VGDL to train and run agents in VGDL-described games. We compare baseline learning results between GVGAI and UnityVGDL across four different games and conclude that the Unity port is comparable to the GVGAI framework. UnityVGDL is available at: https://github.com/pyjamads/UnityVGDL
A comprehensive review from hyperlink to intelligent technologies based personalized search systems

In the present era of big data, web page searching and ranking in an efficient manner on the World Wide Web to satisfy the specific search needs of the modern user is undoubtedly a major challenge for search engines. Even though a large number of web search techniques have been developed, some problems still exist while searching with generic search engines as none of the search engines can index the entire web. The issue is not just the volume but also the relevance concerning the user’s requirements. Moreover, if the search query is partially incomplete or is ambiguous, then most of the modern search engines tend to return the result by interpreting all possible meanings of the query. Concerning search quality, more than half of the retrieved web pages have been reported to be irrelevant. Hence web search personalization is required to retrieve search results while incorporating the user’s interests. In the proposed research work we have highlighted the strengths and weakness of various studies as proposed in the literature for web search personalization by carrying out a detailed comparison among them. The in-depth comparative study with baselines leads to the recommendation of Intelligent Meta Search System (IMSS) and Advanced Cluster Vector Page Ranking (ACVPR) algorithm as one of the best approaches as proposed in the literature for web search personalization. Furthermore, the detailed discussion about the comparative analysis of all categories gives new opportunities to think in different research areas.
A lightweight and efficient digital image encryption using hybrid chaotic systems for wireless network applications

Due to limited processing capabilities and other constraints of most wireless networks, many existing security algorithms do not consider the network efficiency. This is because most of these security solutions exhibit intolerable overhead and consider only securing scalar data, which are not suitable for other data types such as digital images, hence affecting the provided security level and network performance. Thus, in this paper, we propose a lightweight and efficient security scheme based on chaotic algorithms to efficiently encrypt digital images. Our proposed algorithm handles digital images in two phases: Firstly, digital images are split into blocks and compressed by processing them in frequency domain instead of Red-Green-Blue (RGB) domain. The ultimate goal is to reduce their sizes to speed up the encryption process and to break the correlation among image pixel values. Secondly, 2D Logistic chaotic map is deployed in key generation, permutation, and substitution stages for image pixel shuffling and transposition. In addition, 2D Henon chaotic map is deployed to change the pixel values in the diffusion stage in order to enhance the required level of security and resist various security attacks. Security performance analysis based on standard test images shows that our proposed scheme overcomes the performance of other existing techniques.
HBF-DH: An Enhanced Payload Hybrid Data Hiding Method Based on a Hybrid Strategy and Block Features

This paper proposes an innovative and novel steganography method for an AMBTC compressed image by combining a turtle-shell reference matrix and (7, 4) Hamming code, called hybrid strategy and block feature-based data hiding method (HBF-DH). Firstly, the proposed method compresses an original image by an AMBTC compression technique, and then applies a user predefined threshold pair to classify all compressed blocks into three categories: a smooth block, less complex block, and high complex block. Next, the smooth and less complex blocks are smoothed out with the aim of vacating more space for data hiding. The embedding process is divided into two phases. In the first phase, each quantization level pair of the AMBTC compression codes is used to embed an 8-ary secret digit by using a restrictive turtle-shell reference matrix. The second phase embeds binary secret data into a bitmap according to block characteristics. For smooth blocks, the bitmap is directly replaced as binary secret data by using bit replacement. For a less complex block, the binary secret data is concealed into a bitmap by using our proposed (7, 4) Hamming code based data hiding strategy, which increases the data hiding capacity without significant impact on image quality. The experimental results and analyses prove that the proposed HBF-DH method is superior to other existing AMBTC based data hiding methods in terms of data hiding ability with satisfactory image visual quality.
A Many-objective Feature Selection Algorithm for Multi-label Classification Based on Computational Complexity of Features

Multi-label classification constructs a model on instances which are associated to a set of labels. Similar to traditional single-label classification, redundant and irrelevant features degrade the performance of classification in terms of multiple criteria. So, feature selection task can be modeled as optimizing several conflicting objectives in a large search space simultaneously. Minimizing the number of features and the error of classification are two well-known objectives which are considered in several multi-objective feature selection methods. In addition of these objectives, the computational complexity of features is one of the most important properties of selected features which should be minimized as a crucial objective. As a result, it is desired to select less complex features while offer a higher classification accuracy. The key contribution of this paper is proposing a many-objective optimization method, for first time, to select best subset of features for multi-label data based on four objectives including number of features, two classification error measures (i.e., Hamming loss and Ranking loss), and the complexity of selected features. The defined many-objective optimization problem is solved using a proposed binary version of NSGA-III algorithm. In order to evaluate the proposed algorithm (i.e., binary NSGA-III), a benchmarking is conducted on eight multi-label datasets in terms of several multi-objective assessment. Experimental results show significant improvements for proposed method in comparison with NSGA-II approach.