Reviewerly/exHarmony huggingface dataset:
Towards Well-Founded and Richer Context-Awareness Conceptual Models

We observe that context-aware systems currently developed in one domain or another are mostly technology-driven, and not so much user-centric. They are often not based on a thorough analysis of the effects they produce when interacting with their context, especially regarding the contribution of these effects to user needs. We argue that a conceptual framework is needed to support such analyses. In this paper we identify the concepts necessary to define important structural aspects of a context-aware system and its context, and to formulate generalizations about effects of the interaction of the context-aware system and its context related to user needs. Using this conceptual framework, we classify context-aware systems in terms of the kinds of context assumptions that we can make at design time, and we discuss several threats to validity of a context-aware system. We believe that the proposed conceptual framework can help to better assess the utility concerning a context-aware system design. We use various examples of context-aware applications to illustrate our ideas.
A divide and conquer approach for web services location allocation problem

The appropriate choice of locations for the deployment of web services is of significant importance. The placement of a web service closer to user centers minimizes the response time, however deployment cost may increase. The placement becomes more challenging when multiple web services are involved. In this paper, we address the problem of placing multiple web services with the aim of simultaneously minimizing conflicting objectives of total deployment cost and network latency. We solve the location allocation problem for each web service independently and combine the resulting solutions using a novel merge algorithm. We demonstrate through extensive experiments and simulations that the proposed approach is not only computationally efficient but also produces good quality solutions. Further, the proposed merge algorithm is generic and could be easily adapted to tackle any bi-objective optimization problem that can be decomposed into non-overlapping sub-problems.
Simulating a logistics enterprise using an asymmetrical wargame simulation with soar reinforcement learning and coevolutionary algorithms

We demonstrate an innovative framework (CoEvSoarRL) that leverages machine learning algorithms to optimize and simulate a resilient and agile logistics enterprise to improve the readiness and sustainment, as well as reduce the operational risk. The CoEvSoarRL is an asymmetrical wargame simulation that leverages reinforcement learning and coevolutionary algorithms to improve the functions of a total logistics enterprise value chain. We address two of the key challenges: (1) the need to apply holistic prediction, optimization, and wargame simulation to improve the total logistics enterprise readiness; (2) the uncertainty and lack of data which require large-scale systematic what-if scenarios and analysis of alternatives to simulate potential new and unknown situations. Our CoEvSoarRL learns a model of a logistic enterprise environment from historical data with Soar reinforcement learning. Then the Soar model is used to evaluate new decisions and operating conditions. We simulate the logistics enterprise vulnerability (risk) and evolve new and more difficult operating conditions (tests); meanwhile we also coevolve better logistics enterprise decision (solutions) to counter the tests. We present proof-of-concept results from a US Marine Corps maintenance and supply chain data set.
Human-Augmented Prescriptive Analytics With Interactive Multi-Objective Reinforcement Learning

The rise of Artificial Intelligence (AI) enables enterprises to manage large amounts of data in order to derive predictions about future performance and to gain meaningful insights. In this context, descriptive and predictive analytics has gained a significant research attention; however, prescriptive analytics has just started to emerge as the next step towards increasing data analytics maturity and leading to optimized decision making ahead of time. Although machine learning for decision making has been identified as one of the most important applications of AI, up to now, prescriptive analytics is mainly addressed with domain-specific optimization models. On the other hand, existing literature lacks generalized prescriptive analytics models capable of being dynamically adapted according to the human preferences. Reinforcement Learning, as the third machine learning paradigm alongside supervised learning and unsupervised learning, has the potential to deal with the dynamic, uncertain and time-variant environments, the huge states space of sequential decision making processes, as well as the incomplete knowledge. In this paper, we propose a human-augmented prescriptive analytics approach using Interactive Multi-Objective Reinforcement Learning (IMORL) in order to cope with the complexity of real-life environments and the need for optimized human-machine collaboration. The decision making process is modelled in a generalized way in order to assure scalability and applicability in a wide range of problems and applications. We deployed the proposed approach in a stock market case study in order to evaluate the proactive trading decisions that will lead to the maximum return and the minimum risk that the user's experience and the available data can yield in combination.
Games as Information Systems

In this work, we formally build the construct of games as information systems. Starting from models and definitions that describe them, we present models that show game elements: components, players, environment, and their relations; as information systems elements. This paper also presents the analysis of five games and their elements based on that premise. As a ratification of our rationale, we present tools and visions shared by both fields.
Solvable class of non-Markovian quantum multipartite dynamics

We study a class of multipartite open quantum dynamics for systems of arbitrary number of qubits. The non-Markovian quantum master equation can involve arbitrary single or multipartite and time-dependent dissipative coupling mechanisms, expressed in terms of strings of Pauli operators. We formulate the general constraints that guarantee the complete positivity of this dynamics. We characterize in detail underlying mechanisms that lead to memory effects, together with properties of the dynamics encoded in the associated system rates. We specifically derive multipartite "eternal" non-Markovian master equations that we term hyperbolic and trigonometric due to the time dependence of their rates. For these models we identify a transition between positive and periodically divergent rates. We also study non-Markovian effects through an operational (measurement-based) memory witness approach.
The Organizational Cybersecurity Success Factors: An Exhaustive Literature Review

To identify gaps in cybersecurity research, the authors reviewed the secondary literatures of organizational cybersecurity posture, focusing on five factors: technology design, management procedures, organizational structure, legal environment, and human competencies. Their findings show security technology can be improved with advanced technology, usability, and design process. Organizations should employ a unified security platform for all security tools and consider insider threats in the design of cybersecurity-related organizational procedures. Legal literature highlights two major concerns for the future: small and midsized businesses will be major cyberattack victims, and human error is the largest factor in cyberattack success. Given the user as the weakest link of cybersecurity, human-centric technology designers should create human-centric designs in addition to training personnel against cyber incidents.
An Efficient Deep Convolutional Neural Network for Semantic Segmentation

Owing to emergence of Deep Learning (DL) techniques, the segmentation models considerably enhanced their accuracy. However, this improved performance of currently popular DL models for self-driving car applications come at the cost of time and computational efficiency. Moreover, networks with efficient model architecture experience lack of accuracy. Therefore, in this study, we propose an efficient deep convolutional neural network (DCNN) model architecture that combines carefully formulated encoding and decoding paths. Specifically, the contraction path uses mixture of dilated and asymmetric convolution layers with skip connections and bottleneck layers, while the decoding path benefits from nearest neighbor interpolation method that demands no trainable parameters to restore original image size. Owing to the prudently designed model architecture, it considerably reduces the number of trainable parameters, required memory space, training, and inference time. Also, it obtained competitive accuracy scores on several evaluation metrics.
Minimum Round Card-Based Cryptographic Protocols Using Private Operations

This paper shows new card-based cryptographic protocols with the minimum number of rounds, using private operations under the semi-honest model. Physical cards are used in card-based cryptographic protocols instead of computers to achieve secure multiparty computation. Operations that a player executes in a place where the other players cannot see are called private operations. Using three private operations—private random bisection cuts, private reverse cuts, and private reveals—the calculations of two variable Boolean functions and copy operations were realized with the minimum number of cards. Though the number of cards has been discussed, the efficiency of these protocols has not been discussed. This paper defines the number of rounds to evaluate the efficiency of the protocols, using private operations. Most of the meaningful calculations using private operations need at least two rounds. This paper presents a new two-round committed-input, committed-output logical XOR protocol, using four cards. Then, we show new two-round committed-input, committed-output logical AND and copy protocols, using six cards. Even if private reveal operations are not used, logical XOR, logical AND, and copy operations can be executed with the minimum number of rounds. Protocols for general n-variable Boolean functions and protocols that preserve an input are also shown. Lastly, protocols with asymmetric cards are shown.
Neural network-based multi-view enhanced multi-learner active learning: theory and experiments

As applications of neural networks increase in our daily lives, their practicality and accuracy become more of a challenge as they are applied to approximate more complicated functions typically composed of different dependent or independent views. While the complexity of the functions and the number of views to be approximated or simulated increases, the task becomes more complicated and more difficult in that it may eventually jeopardise the classifier’s accuracy and make the results unreliable. This paper surveys an improved active learning method called Enhanced Multi-Learner (EML) to facilitate the approximation or simulation of complex functions via neural networks by distributing the complexities of the task under simulation among an array of learners where each network is responsible for learning a specific view. We experimented with EML realisations through neural networks to solve complex problems where traditional methods did not provide adequate results. These experimental studies were conducted in three different domains and are summarised here. Legacy solutions were also provided in each experiment, and the results were compared. The experimental results indicate the superiority of EML base neural networks in dealing with sophisticated pattern recognition problems.
An empirical study of data constraint implementations in Java

Software systems are designed according to guidelines and constraints defined by business rules. Some of these constraints define the allowable or required values for data handled by the systems. These data constraints usually originate from the problem domain (e.g., regulations), and developers must write code that enforces them. Understanding how data constraints are implemented is essential for testing, debugging, and software change. Unfortunately, there are no widely-accepted guidelines or best practices on how to implement data constraints. This paper presents an empirical study that investigates how data constraints are implemented in Java. We study the implementation of 187 data constraints extracted from the documentation of eight real-world Java software systems. First, we perform a qualitative analysis of the textual description of data constraints and identify four data constraint types. Second, we manually identify the implementations of these data constraints and reveal that they can be grouped into 31 implementation patterns. The analysis of these implementation patterns indicates that developers prefer a handful of patterns when implementing data constraints. We also found evidence suggesting that deviations from these patterns are associated with unusual implementation decisions or code smells. Third, we develop a tool-assisted protocol that allows us to identify 256 additional trace links for the data constraints implemented using the 13 most common patterns. We find that almost half of these data constraints have multiple enforcing statements, which are code clones of different types. Finally, a study with 16 professional developers indicates that the patterns we describe can be easily and accurately recognized in Java code.
Optimization of a refinery scheduling process with column generation and a quantum annealer

Abstract This study focuses on the optimization of a refinery scheduling process with the help of an adiabatic quantum computer, and more concretely one of the quantum annealers developed by D-Wave Systems. We present an algorithm for finding a global optimal solution of a MILP that leans on a solver for QUBO problems, and apply it to various possible cases of refinery scheduling optimization. We analyze the inconveniences found during the whole process, whether due to the heuristic nature of D-Wave or the implications of reducing a MILP to QUBO, and present some experimental results.
Abstraction for non-ground answer set programs

Abstraction is an important technique utilized by humans in model building and problem solving, in order to figure out key elements and relevant details of a world of interest. This naturally has led to investigations of using abstraction in AI and Computer Science to simplify problems, especially in the design of intelligent agents and automated problem solving. By omitting details, scenarios are reduced to ones that are easier to deal with and to understand, where further details are added back only when they matter. Despite the fact that abstraction is a powerful technique, it has not been considered much in the context of nonmonotonic knowledge representation and reasoning, and specifically not in Answer Set Programming (ASP), apart from some related simplification methods. In this work, we introduce a notion for abstracting from the domain of an ASP program such that the domain size shrinks while the set of answer sets (i.e., models) of the program is over-approximated. To achieve the latter, the program is transformed into an abstract program over the abstract domain while preserving the structure of the rules. We show in elaboration how this can be also achieved for single or multiple sub-domains (sorts) of a domain, and in case of structured domains like grid environments in which structure should be preserved. Furthermore, we introduce an abstraction-&-refinement methodology that makes it possible to start with an initial abstraction and to achieve automatically an abstraction with an associated abstract answer set that matches an answer set of the original program, provided that the program is satisfiable. Experiments based on prototypical implementations reveal the potential of the approach for problem analysis, by its ability to focus on the parts of the program that cause unsatisfiability and by achieving concrete abstract answer sets that merely reflect relevant details. This makes domain abstraction an interesting topic of research whose further use in important areas like Explainable AI remains to be explored.
Testing and selecting lightweight pseudo-random number generators for IoT devices

The Internet of things (IoT) is the network of physical objects embedded with software, sensors, and other technologies that connect and exchange data with other devices and systems over the Internet. The estimation is that they will be about 75% of the 42 Billion devices connected to the internet by 2025, and this growth brings a huge amount of sensitive network traffic. The design and implementation of secure IoT devices is an error-prone task, and statistical flaws in their random number generator can lead to catastrophic security vulnerabilities easily exploited by a malicious agent. This paper presents our effort to raise awareness on identifying and evaluating cryptographically secure lightweight PRNGs. We selected some random number generators and presented a simple description of how to run them against well-known statistical test suites TestU01 and PractRand. The results obtained indicate a set of generators with the potential to be used for cryptographic purposes.
Virtual Reality Sickness Evaluation in Exergames for Older Hypertensive Patients: A Comparative Study of Training Methods in a Virtual Environment

Virtual Reality (VR) sickness is an exclusive problem in immersive VR. However, the target group of hypertensive patients is a particularly vulnerable group that could be more sensitive to such a phenomenon. Therefore, the aim of this study was to determine whether there are differences in the symptoms of the VR sickness between a strength endurance-based and an endurance-based VR exergame. Participants over 65 years old with diagnosed essential hypertension were included. An assessment of cognitive status and an assessment of risk of falling were performed as inclusion criteria. All participants tested two VR exergames (strength endurance and endurance training) on two visits. The Simulator Sickness Questionnaire (SSQ) was used after the task-based application. The endurance exergame tended to have higher scores in the scales than the strength endurance exergame. The increased movement in space could be a cause for this. Significant differences were shown only for the scale “oculomotor”. Descriptively, more symptoms were found in women and participants without VR experience and participants over 76 years of age. There were no relevant self-reported cases of VR sickness in the sample. For the target group of older hypertensive patients an exergame, which is arranged as a frontal personal training and requires less movement through the room, could cause less VR sickness and be more appealing.
Security Performance of Public Key Distribution in Coherent Optical Communications Links

Theoretical uncertainties associated with the measurement of quantum bits have made Quantum Key Distribution (QKD) an attractive research topic for secure communication systems. Recent research in this area has focused on using weak coherent laser pulses as the carrier instead of the traditional approach of using single photon carriers. This opens the possibility to implement QKD infrastructure that is compatible with the existing optical fiber communication network. In this work, we report a theoretical study of a new approach that utilizes the Quantum Public Key Envelope (QPKE) based on the randomized Glauber states in a classical coherent optical communication system to obtain a secure communication. We verify the efficacy of the system using numerical simulation. We also study the possible approaches for an eavesdropper to test the degree of security enhancement of the proposed system at bitrate of 56 Gb/s. Two eavesdropping techniques are proposed for security analysis. A set of security parameters are used to simulate the variety of eavesdropping schemes.
Multiple instance classification: Bag noise filtering for negative instance noise cleaning

Data in the real world is far from being perfect. The appearance of noise is a common issue that arises from the limitations of data acquisition mechanisms and human knowledge. In classification, label noise will hinder the performance of almost all classifiers, inducing a bias in the built model. While label noise has recently attracted researchers' attention in standard classification, it has only recently begun to be studied in multiple instance classification. In this work, we propose the usage of filtering algorithms for multiple instance classification that are able to reduce the impact of negative instances within the bags. In order to do so, we decompose the bags to form a standard classification problem that can be efficiently treated by a specialized noise filter. Such a decomposition is tackled in different ways, with the aim of exploiting the knowledge offered by the examples from opposite bags. The bags are then rebuilt, without the identified noise instances. In our experiments, we show that by applying our approach we can diminish the impact of noise and even obtain better results at 0% noise level for several classifiers. Our approach sets out a promising approach to dealing with noise in the bags of multiple instance datasets and further improve the classification rate of the built models.
Designing for Self-awareness: Evidence-Based Explorations of Multimodal Stress-Tracking Wearables

Early wearable devices using multimodal data to promote stress-awareness are emerging on the consumer market. They prove to be effective tools to support users in tracking their daily activities, yet their potential still needs to be further explored. From a user experience design perspective, such wearable devices could help users understand how they feel stress and ultimately shed light on its psychophysiological bases. Based on this rationale, this paper reports the results of evidence-based explorations aimed at formalizing knowledge regarding the use of multimodal stress-tracking wearables. Following a human-centered design process, we design an interactive prototype that tracks two stress-related parameters, namely physiological and perceived stress. We employ a smartwatch to track blood volume pulse and heart rate variability to assess physiological stress, whereas we rely on self-reports gathered through a smartphone to assess perceived stress. We then test the prototype in a controlled setting with 16 end-users. Tests combine qualitative and quantitative research methods, including in-depth interviews, eye-tracking, and surveys encompassing a Kano model-based and AttrakDiff questionnaires. In-depth interviews reveal insights about the type and quantity of information users expect. Ocular scanpaths provide directions to leverage the cognitive effort required by users when interacting with multiple devices. Lastly, evidence from surveys highlights the features and functions that multimodal stress-tracking apps should include. Based on our findings, we create a set of considerations on personal informatics promoting stress awareness from a user experience design perspective. Lastly, we outline future directions for the research design of wearable solutions to promote self-awareness.