Reviewerly/exHarmony huggingface dataset:
Joint Distribution Analysis of Multi-Dimensional Randomized Response

Data collection can provide services and convenience for people, but disclosure of personal privacy during data collection can be harmful. So, it is needed to protect personal privacy while collecting data. Randomized response is a reliable privacy-preserving technique. For multi-dimensional data, the most direct mechanism is RR-Independent, which applies randomized response on each attribute independently. After applying the RR-independent mechanism, the joint distribution of perturbed data needs to be modified in order to be close to that of input data better. Two estimation methods of joint distribution, Naive estimation and Castell estimation, are studied in this paper. Then, we propose a sufficient and necessary condition for the two estimations to be equal. In addition, a mathematical study shows that Castell estimation is closer to the original distribution of data than that of Naive estimation if the condition is not satisfied. Finally, numerical experiments are carried out to show that Castell estimation is better than Naive estimation and the results obtained by the two protocols in the paper are consistent.
Adoption of Big Data and AI in UAE SMEs in Unpredictable Environment

Digital technologies like “big data” and “artificial intelligence” have become firmly embedded in corporate operations over the past several years, and they are even starting to play a bigger role in specialized industries like retail, distribution and manufacturing. Despite government and relevant agency understanding, evaluation, support, and training regarding Industrial Revolution 4.0, the use of these technologies is only gradually being adopted in the UAE’s SME sector. This exploratory research paper identifies five major business challenges faced by these SMEs and suggests potential solutions by investigating an opportunity for cross-fertilization between Big Data, Artificial Intelligence and Advanced analytics. There have been very limited studies done in past to understand the adoption of these technologies with respect to UAE SMEs sector. An in-depth literature review has been conducted and the technology adoption models such as UTAUT and TOE framework is implemented plus extended, with the goal of conceptualizing a model that incorporates the discovered components. The study's findings may be used as a guide for identifying and forecasting the acceptance of Big Data as well as Artificial Intelligence amongst SMEs in U.A.E
Exploring Coexistence of Software Architecture Development and Agility through a Multivocal Literature Review

Agile development methods aim to balance up-front and just-in-time work, prioritize adaptive and exploratory approaches, and spend time and effort on essential activities in a project's lifespan. One of the key strategies in agile development is to defer making decisions as late as possible. Software architecture development (SAD) requires creating high-level software infrastructure that reflects a solution for quality requirements such as reliability and maintainability. SAD requires performing up-front work and having established software requirements. Given these characteristics, Agile and SAD may be perceived as two conflicting worlds. In this study, we present the results of a Multivocal Literature Review (MLR) aimed at understanding the challenges of developing software architecture in agile software development projects and the solutions proposed for these challenges. The MLR included 13 grey literature and 40 formally published research sources. The challenges and solutions are discussed across eight categories.
Large Language Models and Adversarial Reinforcement Learning to Automate PLCs Programming: A Preliminary Investigation

The manual programming of Programmable Logic Controllers (PLCs) is a time-consuming and error-prone task, particularly for complex systems. Researchers have proposed various techniques, such as the modular, decentralized, hierarchical, and distributed approaches, which all fall under the Supervisory Control Theory (SCT) whose main goal is to generate a supervisor that ensures that the controller's behavior satisfies the specifications. While these techniques have demonstrated efficiency in systems of low complexity, high-complexity systems and probabilistic ones remain a major challenge. In this paper, we propose a novel pipeline that relies solely on Artificial Intelligence (AI)-techniques, namely, Reinforcement Learning (RL) and Natural Language Processing (NLP) techniques, including Named Entity Recognition (NER) and Large Language Models (LLMs). As a preliminary result, we demonstrate the potential of the proposed pipeline using the recent ChatGPT model by making a sample dataset in this context and generating Structured Text code from specifications presented in a natural language format. Finally, we outline future directions toward a fully automated AI-based PLC programming tool.
A Graph Matching Approach by Aggregated Search

Over the last decade, graphs have gained in popularity and have been widely used to model complex real world structures and data in various application domains. This leads to a rapid proliferation of large graph databases with hundreds of millions of nodes and edges. Thus, graph querying becomes a major problem that emerges from these collections of graphs. However, the current approaches for graph matching fail if no single graph satisfies all requirements of a query. In this paper, we present a novel approach for graph matching, where a new graph is constructed from fragments of other graphs. As a result, several relevant graph fragments are assembled and presented as solution. The proposed approach returns the best aggregated graph that fulfils as much as possible the query requirements, instead of returning a ranked list of graphs. We analyse the execution time of the proposed solution and conduct experimental evaluations to measure its effectiveness compared to several graph matching algorithms. The results show the good performance of the proposed approach.
CloverNet: A Real-Time Network for Semantic Segmentation Onboard Edge Devices Towards Planetary Exploration

This paper introduces CloverNet, a novel convolutional neural network (CNN) designed for real-time semantic segmentation capabilities onboard planetary exploration rovers. Teleoperating planetary rovers is a tedious task as it requires close to real-time monitoring and planning, while distance and communication delay further cripple the efficiency. Future up-scaling of off-world robotic systems deployment makes it increasingly difficult to monitor and command each agent in real-time. Thus, at least an autonomous maneuvering capability is essential to reduce the operational workload at the ground station. In order to achieve this objective, the rover is required to understand its operational environment. The rover then needs to process the information at real-time and efficiently respond in order to complete the allocated tasks or to recover from hazards. An innovative CNN for real-time semantic segmentation is proposed, which can be run on limited memory and graphics processing unit (GPU). The solution is benchmarked on an RGB-D visual sensor and on an NVIDIA Jetson AGX Xavier mounted on a skid-steering micro-rover platform. CloverNet consists of a light encoder-decoder architecture that is optimized and quantized using the NVIDIA TensorRT framework, enabling it to operate at high frame rate and low GPU latency. The network is trained using a new data set curated for developing semantic segmentation of rocks and rovers belonging to Lunar and Martian analogue environments. Experimental results showed it can reach high performance in terms of accuracy and intersection over the union, as well as achieving a maximum inference speed of ∼ 55 FPS on a GPU board at the edge. As such, the results prove CloverNet suitable for planetary missions.
Multistep Hyperparameter Tuning via Reinforcement Learning for Simulated Annealing

In this study, a hyperparameter (HP) tuning method for simulated annealing (SA) is proposed. In recent years, annealing machines, i.e., non-Neumann architecture computers inspired by the annealing process of spins in the Ising model, have been developed to solve various optimization problems quickly. The performance of annealing machines depends on various HPs, e.g., the temperature range and cooling rate, which are required to set the annealing conditions. Due to the commonality of optimization calculations based on the Ising model, HP tuning methods for SA can be applied to annealing machines. A representative HP tuning method is multistep tuning, where HPs are changed adaptively during the annealing steps. However, adaptive tuning tends to increase computation time costs; thus, the number of HP tuning cycles in multistep tuning methods should be minimized. Therefore, the proposed method implements reinforcement learning (RL) into multistep tuning to set HPs appropriately for various inputs while minimizing the number of tuning cycles. The proposed RL-based method was evaluated by introducing a benchmark environment, and the results demonstrate that the proposed method can obtain the optimal policy that provides appropriate HP values with fewer cycles compared to existing tuning methods.
The Impact of Grid Search on Bug Resolution Prediction for Open-Source Software

In software engineering, maintenance effort estimation is a challenging research topic. Several empirical studies have focused on Maintenance Effort Estimation for Open Source Software (O-MEE) using Machine Learning (ML) techniques. Although Tuning the Parameters (TP) of ML techniques has proved to improve their performance, it has not yet been investigated in the context of O-MEE for bug resolution prediction. Therefore, this empirical study investigates the impact of grid search TP method, on the performance of k-Nearest Neighbor, Support Vector Machine, and Random Tree using Eclipse Platform, Eclipse JDT, and Apache datasets. A set of 18 ML classifiers were built and the results show the positive impact of the TP on the ML techniques' performance compared to the default ones.
Semantic Approach for Auto-Driven Medical Database Construction

Ontologies have become a standard for knowledge representation, supporting the organization and acquisition of knowledge across different domains. However, there are similarities between ontologies and databases in terms of organizing and structuring information, managing relationships between data elements, and supporting querying capabilities. In this paper, our objective is to leverage the information stored in ontologies to automatically construct a database. We propose a novel approach that automates and optimizes the storage of data in a database based on the ontology's structure. We define a set of rules to analyze the ontology, identify concepts, attributes, and relationships, and generate the corresponding database schema. This automation streamlines the database creation process, ensures consistency between the ontology and the database, and enables efficient storage, retrieval, and management of data based on the semantic structure provided by the ontology. To evaluate our approach, we develop a framework that tests the rules using various medical ontologies and generates corresponding databases. We conduct a series of tests to analyze the resulting database and compare it with existing approaches in the literature. Our framework demonstrates effectiveness in terms of generating time and database size.
Community-Augmented Local-Link Intensity: A Score for Anomaly Detection in Graphs

Graphs can model various systems, activities, or interactions. As a consequence, the ability to detect anomalies in graph-structured data is needed in many scenarios. This paper introduces Community-Augmented Local-Link Intensity (CALLI), an algorithm for quantifying the abnormality extent of nodes in a weighted digraph. Experiments are performed on both synthetically generated and real graphs modeling financial transactions. Throughout the tests, CALLI is directly used as the sole decision-making anomaly score, but also as a feature in a conventional outlier detection approach.
Sentiment Analysis Using Smoothed Probabilistic-Based Models

In this paper, we propose an unsupervised learning algorithm for Natural Language Processing (NLP). In particular, we present a sentiment analysis solution using probabilistic models. We propose two new smoothed probabilistic-based models that incorporate the benefits of smoothing techniques and scaling word vectors to address sparseness and high-dimensionality challenges. We introduce the smoothed scaled Dirichlet and the smoothed shifted scaled Dirichlet mixture models, the learning approach for the mixture parameters, the clustering algorithms, and the sentiment analysis framework. We consider in our experiments different benchmarks of sentiment analysis, namely, Stanford Twitter sentiment (STD), Stanford sentiment gold standard (STS-Gold), SemEval2014 Task9, and Sentiment Strength Twitter (SentiStrength). The results are compared with the baselines and state-of-the-art (SOTA) models in the literature where our proposed approaches outperform all the other models.
Autonomous PSO-DVSF<sup>2</sup> in the Control of Real Mobile Robots in Unknown Environments

Autonomous <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{PSO-DVSF}^{\text{2}}$</tex> is a PSO optimized <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{F}^{2}$</tex> based mobile robot motion planning approach that we have previously proposed to guide a robot in unknown environments. We proved the efficiency of this approach by means of simulation tests [15]. Hence in this paper, we are dealing with the experimental setup of our proposed simulations. As a first step, the approach is tested in the MobileSim virtual environment then in a second step the tests are done in experimental environments using the differential Pioneer P3-DX wheeled robot. The results of these tests proved the real efficiency of autonomous PSO- <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{DVSF}^{\text{2}}$</tex> algorithm to reach its destination selecting the shortest and the securest trajectory whatever the environment is static or dynamic and whatever its complexity.
Augmented Flight Management System for Future Single Pilot Operations

The next leap in civil aviation features the advent of Single Pilot Operations (SPO) which is expected to form the basis of the tomorrow air transport operations. Transition to SPO raises however many issues around safety risks, public acceptance, regulatory changes and certification, as human redundancy from the flight deck will be removed. It provides also numerous opportunities for the development of single-pilot-centered technologies. This paper promotes a human-centered pathway to support more autonomy by combining learning-enabled and data-driven components with model-based robust control theories, with limited impact on today airworthiness certification processes. This provides a realistic near-future perspective before, yet to come, breakthroughs around interpretable and trustworthy Artificial Intelligence (AI) in the cockpit. The paper briefly sketches some aspects related to risk-mitigation issues in SPO.
Study of Numerical Methods for Solving Optimal Control Problem for a Group of Robots

The problem of numerical solution of the optimal control problem for a group of robots is considered. The main complexity of the problem is the presence of dynamic phase constraints that describe the condition to prevent collisions between robots. Since there are several possibilities to avoid collision, and each of these possibilities corresponds to a local minimum, therefore the objective function has many local minima, thus the problem belongs to the class of global optimization. Precise methods for solving global optimization problems in this case cannot be applied due to the large dimension of the search space. It is proposed to use evolutionary algorithms to solve the optimal control problem of a group of robots. In computational experiment a complex optimal control problem for four quadcopters moving in space with four obstacles was solved by genetic and hybrid algorithms.
Research on Multi-Agent State Estimation Algorithm under Intermittent Observation

In this paper, state estimation based on multi-agent theory under intermittent observation is studied. In practice, multi-agent systems often suffer from loss of measurement data. For the case of episodic data loss, the statistical properties of the data before and after the loss are used to detect the loss and reconstruct the measurement function to complete the estimation under intermittent observations; for the case of long-term data loss, the time-varying weighting method is used to reduce the importance of the problem nodes and minimize the global estimation error. The effectiveness of the algorithm is demonstrated by experiments on the dynamic estimation of the target object.
Unsplittable Multi-Commodity Flow Problem via Quantum Computing

Quantum computing is a promising area to tackle optimization problems. Several algorithms are derived from this paradigm. One interesting method is called Quantum Approximate Optimization Algorithm (QAOA) where the goal is to solve a Quadratic Unconstrained Binary Optimization. This method is heuristic in practice but can converge on an optimal solution with enough time and a computer with enough resources. In this paper, we focus on the unsplittable multi-commodity flow problem and we propose a dedicated algorithm using QAOA as a sub-routine. Thanks to well-known methods, cutting plane, column generation, and Lagrangian relaxation, we propose a framework to solve this problem based on QAOA.
Mode Recognition in Attack Graphs Based on Conditional State Probability

Attack Graphs are modeling tools to visualize the behaviour of an attacker (intruder) throughout the network. By using attack graphs, the cyber-security agent can evaluate the security of the network as well as know the potential actions of the attacker or even know at what level the system can be compromised. Attack graphs are usually used offline to measure the resilience of a network against cyber-attacks. This paper shows that such models can also be used online to recognize the current actions of an attacker throughout the network, while it is under attack using a conditional state probability. Our setting is that during the attack some events are observed with their time stamps. This information is used to refine the estimation of each mode i.e., attack action, in the graph over time.
Area Filtering Methods (AFM) are Used to Control and Improve Contour Detection of Shapes in Images

Edge detection in images is the widely used technique in the field of computer vision. There are many different ways but the problem is always that the contour algorithms are very precise so they are inefficient for some applications because they discover all the features of the shapes in the image. The problem that sometimes exists is the noise in the image, and if we process this noisy image, then the algorithm needs more time to finish the image processing. This delay can affect the operation of the system. Additionally in some applications, we need a specific size of the shape of the image, and therefore it is not necessary to discover all the sizes of this image. Therefore, the complementary algorithm of the contour algorithm is necessary to reduce the losses. In this research, we suggest a complementary algorithm that plays the role of contour filtration in the image. This filter is called “Area Filtering Method (AFM)” and depends on Python programming. The idea is based on the measurement of the shape area in pixels. This technique counts the number of pixels in the internal zone of each shape. This number obtained represents the area of the shape. Then, we compare this value with the minimum area value and the maximum area value. The algorithm determines these values because the size of the shapes in the image will vary from one image to another. The objective is to have the minimum possible errors when detecting shapes by contour. AFM was tried with photos taken by the smart camera and we got very good results.
Artificial Bee Colony Algorithm for Lean Software Reuse

Reuse in software development is a practice that improves the software development process. Reusing existing software artifacts requires an efficient retrieval mechanism. Leaning out software repository for effective and efficient retrieval and reuse of relevant artifacts requires identifying and eliminating the wasteful artifacts it may involve. Model matching is a preliminary step to identify what is common and what is variant among the software artifacts. However, the time for matching two models to find the optimal correspondence between their elements is exponential. Artificial Bee Colony algorithm is a heuristic that is getting popularity as reasonable solution for problems under different optimization scenarios. This paper presents a solution algorithm based on Artificial Bee Colony for matching UML class diagrams. On a dataset of ten pairs of class diagrams, the performance of the suggested approach is empirically evaluated and compared with Ant Colony approach. The performance of the two algorithms are reported in terms of accuracy of matching and execution time. The results indicate the superiority of the Artificial Bee Colony algorithm in terms of accuracy rate and execution time.
Machine Learning Control Synthesis by Symbolic Regression for Avoidance of Arbitrary Positioned Obstacles

The problem of control synthesis for a mobile robot with phase constraints in the form of an arbitrarily located obstacle is formulated. To solve the problem, a numerical method of machine learning based on symbolic regression is used. According to the approach, in addition to its state, the object receives information about the distance to the obstacle and the direction of its location. As a result of solving the synthesis problem, we obtain a nonlinear feedback control function that provides a better estimate of the obstacle bypass according to a given criterion. An example for a mobile robot with a differential drive is considered.
Multi-Agent Reinforcement Learning with Epistemic Priors

It is important for autonomous multi-agent teams to coordinate actions so that collaborative goals can be achieved efficiently without conflicts. Practical issues in multi-agent, real-time systems are limited sensing and communication capabilities. A significant number of multi-agent algorithms rely on accurate state information for all agents in order to effectively coordinate. In this paper, we propose an approach called Reinforcement Learning with Epistemic Priors (MARL-EP). MARL-EP incorporates a shared mental model between agents by leveraging epistemic estimation to infer portions of the obser-vation space which are unobservable. We show that MARL-EP allows a very high level of coordination to be achieved with severely impaired sensing and zero communication between agents.