Reviewerly/exHarmony huggingface dataset:
Addressing Vulnerability in Medical Deep Learning through Robust Training

Deep neural networks have been incorporated into healthcare for the purpose of diagnosing and detecting medical conditions. However, studies have shown that the vulnerability of neural networks to adversary and noise remains a pervasive problem that compromises trust of medical practitioners and accuracy in diagnosis, prognosis, and outcome prediction by such systems. In this study we show that robust training methods can help models perform more robustly against not only adversarial attacks, but also noises and calibration errors.
A Preliminary Critical Review of the Impact of Three Popular Development Practices on Source Code Maintainability

The maintainability of source code is a vital quality of resilient software systems and speaks to the ability of software engineering teams to modify source code quickly. For organizations seeking to build resilient software systems, it is crucial to consider source code maintainability because rapidly changing source code in response to new adversities is critical to the long-term resilience of software systems. In this study, we review relevant literature on three popular software development practices that aim to improve the maintainability of source code: test-driven development, refactoring, and pair programming. Our review provides insights into the strengths and weaknesses of these practices and highlights their potential role in enhancing the resilience of software systems.
Prediction of Bug Inducing Commits Using Metrics Trend Analysis

Continuous software engineering advocates a release-small, release-often process model, where new functionality is added to a system very frequently and in small increments. In such a process model, it is important to be able to identify as early as possible, and every time a change is introduced, whether the system has entered a state where faults are more likely to occur. In this paper, we present a method that is based on process, quality, and source code metrics to evaluate the likelihood that an imminent bug inducing commit is highly probable. More specifically, the method analyzes the correlations, and the rate of change of selected structural and quality metrics. The findings from the SonarQube Technical Debt open-source dataset indicate that before bug inducing commits, metrics which otherwise are not corelated, suddenly exhibit a high correlation or high rate of metric value change. This metric behavior can then be used as a predictor for a imminent bug inducing commit. The technique is programing language agnostic, as it is based on metrics which are extracted without the use of specialized parsers, and can be applied to forewarn developers that a file, or a collection of files, has entered a state where faults are highly probable.
DP2AS—Definitive Privacy-Preserving Analytical Scheme for Healthcare Data Processing

Smart healthcare systems require secure and robust data computations for providing uninterrupted monitoring, recommendation, and assistance. Wearable sensor (WS) data sources serve as the prime aggregator for data handling. Considering the security demands in sensitive healthcare data, this article introduces a Definitive Privacy-Preserving Analytical Scheme (DP2AS). The proposed scheme exploits the data classification feature based on false positives and replication. The suggested method detects redundant data in healthcare by comparing open and secure aggregation scenarios. Classifying data features as either continuous or replicating helps prevent fraudulent data insertion. By employing tree classifiers, the data attributes are accounted for in different WS aggregation intervals preventing replications. The computations are independent of false data and application-specific computations, retaining the WS privacy. In this analysis process, the error-free/ false positive fewer data chunks are concealed with user adaptable security mechanism for preventing data poisonings. The analytical model considers the previous data state with the current processing data for avoiding erroneous interruptions. The state classffier’s maximum replication mitigation provides application-specific data transfers with fast computation possibility. The proposed scheme’s performance is analyzed using the metrics false rate, data utilization, and analysis time.
Secure Feedback to Edge Servers in Distributed Machine Learning Using Rich Clients

The use of data collected by edge devices in machine learning, including personal information, has become an important trend in recent years. Most distributed machine learning methods such as Federated Learning aggregate and manage all data or training results on a high-performance edge servers. However, passing users’ personal information to an external server may involve some privacy concerns owing to the risk of information leakage. To address this problem, we consider a distributed machine learning model with excellent privacy protection in which the user can choose not to pass any personal data to the server. In the proposed model, the edge device takes over the training at the edge server and sends only the results for which the user has given permission to the edge server for integration. To validate the effectiveness of the proposed model, we performed experiments on facial image recognition using a Jetson Nano as an edge device. The experimental results confirm that edge devices were able to use personal information in a short period of time, while the edge server was able to obtain more accurate results by integrating several training results. Thus, the results show that the proposed model enables the safe and efficient utilization of data collected by edge devices.
Personalizing Student Graduation Paths Using Expressed Student Interests

This paper proposes an intelligent recommendation approach to facilitate personalized education and help students in planning their path to graduation. The goal is to identify a path that aligns with a student’s interests and career goals and approaches optimality with respect to one or more criteria, such as time-to-graduation or credit hours taken. The approach is illustrated and verified through application to undergraduate curricula at the Missouri University of Science and Technology.
Online Spatio-temporal Action Detection for Eldercare

Using AI technologies for assisted living of the elderly greatly facilitates care provision by caregivers and healthcare professionals. This paper proposes a lightweight model for real-time detection of human actions focusing on the elderly using a conventional RGB camera and an AI edge device. Our model analyzes and predicts the actions as the video frames arrive live from the camera and utilize spatio-temporal action detection to detect and locate the human actions with minimum latency. It can also apply to scenarios involving multiple people. We evaluate the proposed method using the popular public action detection dataset, AVA as well as an in-house self-collected dataset. The results show that our model can accurately detect the various actions in real-time at 15.2 fps using a resource-constrained edge device, offering significant potential for applications in various smart monitoring systems.
A Model of Computational Creativity based on Engram Cell Theory

Artificial Intelligence technology has made remarkable progress in machine learning, but it is still in its infancy in creative thinking or computational creativity. In 2018, Yang and Li proposed that the physiological basis for the formation of memories and concepts in the human brain is engram cells (interneuron), and creative thinking is the process of forming new engram cells to connect previously seemingly unrelated concepts. During this process, association and prediction play a key role. In this study, a computational model based on engram cell theory was coded in Python to mimic the process of creative thinking. The validity of the model was tested by simulating the phenomenon of language generation and summarizing the artificial food-set regularity in the plus maze. The results show that, given 29 initial words and certain grammatical rules, the language generation program generates 25,405 sentences after 130,000 calculations, and these generated sentences can be combined into various short paragraphs. After 50 times of training in the cross maze puzzle solving program, the model can master 100% of the rules of artificial food settings. In conclusion, a computational model of creative thinking based on engram cell theory can creatively and automatically generate sentences and paragraphs, and can learn and summarize laws to solve simple puzzles. We plan to further use this model to address complex real-world problems, such as the study of cancer therapeutic targets
Application of XAI-based framework for PV Energy Generation Forecasting

The structural changes in the energy sector caused by renewable sources and digitization have resulted in an increased use of Artificial Intelligence (AI), including Machine Learning (ML) models. However, these models’ black-box nature and complexity can create issues with transparency and trust, thereby hindering their interpretability. The use of Explainable AI (XAI) can offer a solution to these challenges. This paper explores the application of an XAI-based framework to analyze and evaluate a photovoltaic energy generation forecasting problem and contribute to the trustworthiness of ML solutions.
Curating History Datasets and Training Materials as OER: An Experience

Evidence shows that practice-based learning is beneficial to students’ understanding of threshold concepts in all disciplines. Teaching activities that draw on research data provide students with real-world examples of how they might apply this new knowledge, and this reinforces their understanding. While research data from projects in disciplines such as computer science or economics is widely published, it is only recently that humanities scholars, in particular historians, have started considering publishing their research data in digital format. Using a case study of a 5-year funded collaborative project between historians and computer scientists, this paper discusses how the research data was created and applied in a classroom context, providing students with a ’real-world’ experience of working as a historian. It shows how the project data developed from a basic transcription of historical records into a fully enriched open dataset that can be used by teachers from a range of disciplines including history, historical geography, demography, computer science and medicine/medical humanities. It concludes that lessons planned around such open research data comprise a valuable educational resource to teachers and students.
The Global Workspace Theory: A Step Towards Artificial General Intelligence

Global Workspace Theory (GWT) and Artificial General Intelligence (AGI) are two concepts in cognitive science and Artificial Intelligence, respectively. This paper discusses the possibility of achieving AGI using a deep learning implementation of GWT. The shared latent space for GWT is trained using the latent spaces of the connected deep learning modules. This implementation aims to enhance the performance of specialized models in their specified tasks and achieve more general functions from single-task/specialized modules. The paper also discusses the possible applications of this implementation in healthcare.
Toward a Method Engineering Framework for Project Management and Machine Learning

Adopting or tailoring a project management (PM) method for the domain-specific requirements of Machine Learning (ML) and Software Engineering (SE) has been a major challenge. However, the review of the literature cannot provide sufficient work, and thus engineering PM processes for ML remains neglected. Therefore, this paper presents the theoretical and methodological aspects of a study, which is also the third and last part of an integrated research project conducted for Baskent University Hospital Ankara (BUHA). The outputs of the study are twofold: (a) a method engineering (ME) framework for ML PM; and (b) a new hybrid PM for ML projects. The research approach combined the guidelines and principles of Design Science Research and Action Research methods. It is thought that this study may be regarded as promising, and an attempt to improve SE and PM processes of ML in the healthcare domain.
Motive Metrics: A Jira Plug-In for Personality, Motivation and Performance Tracking

Requirements Engineering (RE) is an essential part of Software Engineering (SE). As RE activities rely heavily on people, the success of RE is influenced by the human aspects of the team involved. In this research, we develop a prototype application, called Motive Metrics, to improve RE activities by allowing managers to track developers’ personality and motivation and monitor their impact on developer performance and satisfaction. The tool takes the form of an extension to Jira and was developed through rapid prototyping. The effectiveness and usability of the tool were evaluated by student teams split into managers and team members. When evaluating Motive Metrics, 45.5% of participants rated 4 out of 5 for the application’s effectiveness in capturing personalities, but only 9.1% of participants rated 4 out of 5 for capturing motivations. Motive Metrics is likely ineffective in monitoring satisfaction and performance, as 45% of participants rated 1 out of 5 in comfort in sharing their responses. Our evaluation results also show that Motive Metrics might not be beneficial in tracking the influence of motivation on the outcome but slightly more beneficial in tracking the influence of personality on RE task performance.
Reinforcement Learning Approaches for Racing and Object Avoidance on AWS DeepRacer

Developing autonomous driving models through reinforcement learning is gaining widespread prominence. However, a pervasive problem is developing obstacle avoidance systems. Specifically, optimizing path completion times while avoiding objects is an underdeveloped area of research. AWS DeepRacer’s platform provides a powerful architecture for engineering and analyzing autonomous models. Using AWS DeepRacer, we integrate two pathfinding algorithms, A* and Line-of-Sight (LoS), into this paradigm of autonomous driving. LoS is a novel algorithm that incrementally updates the model’s heading angles to amply reach its destination. We trained three types of models: Centerline, A*, and LoS. The Centerline model utilizes logic from AWS and is practically the only model used by the AWS DeepRacer community that avoids objects. We developed models from A* and LoS that outperformed the default models in time per lap while maintaining commensurate stability.
Let’s Block Encrypted Malicious Sites

Always-On SSL (AOSSL) is now becoming common even in malicious sites for phishing or malware. Most of these AOSSLed malicious sites employs Domain-Validated (DV) certificate. Many of these DV certificates are issued by Let’s encrypt, which is a major CA that is dedicated for DV certificate.This paper proposes to regard AOSSLed sites using Let’s Encrypt with additional factors as malicious, and block them on a firewall in the wild. For judging maliciousness, use of Transport Layer Security (TLS) 1.2 or less, malicious Top-Level Domain (TLD) found in a heuristic way and dynamic DNS (DDNS) are considered as criteria. We have then implemented an on-the-fly access prevention to malicious sites on a next-generation firewall running in the actual environment of authors’ university. We have implemented these by configuration changes only on the firewall developed by Palo Alto Networks, Inc., and keep operability and an access prevention. In the authors’ university, the proposal has appeared that TLD based detection could detect malicious sites by 38.3% accuracy while no malicious sites was detected by DDNS.
Secure Deduplication with Dynamic Key Management in Fog Enabled Internet of Things

Fog computing is an extension of cloud computing and presents additional devices called fog devices near IoT devices providing services on behalf of cloud servers. Although fog computing brings several advantages, rapid growth in the data generated by IoT devices increases communication and computational costs. As data sensed by IoT devices may correlate, there is a high possibility of duplicate copies in the sensed data. Data deduplication is a compression technique that reduces communication and storage overhead by skipping the upload and storage of duplicate copies of data. However, deduplication for a fog-enabled IoT system introduces new security issues. In this paper, we propose a novel secure deduplication approach with dynamic key management in a fog-enabled IoT system. We introduce a multilayer encryption scheme with dynamic key management to prevent the access of revoked users to the data. We implement the proposed scheme in a realistic scenario using Raspberry Pi and Firebase cloud services. The performance analysis shows that our approach achieves confidentiality and forward secrecy along with lower storage, computational, and communication costs.
Fishing Trawler Event Detection: An Important Step Towards Digitization of Sustainable Fishing

Detection of anomalies within data streams is an important task that is useful for different important societal challenges such as in traffic control and fraud detection. To be able to perform anomaly detection, unsupervised analysis of data is an important key factor, especially in domains where obtaining labelled data is difficult or where the anomalies that should be detected are often changing or are not clearly definable at all. In this article, we present a complete machine learning based pipeline for real-time unsupervised anomaly detection that can handle different input data streams simultaneously. We evaluate the usefulness of the proposed method using three well- known datasets (fall detection, crime detection, and sport event detection) and a completely new and unlabelled dataset within the domain of commercial fishing. For all datasets, our method outperforms the baselines significantly and is able to detect relevant anomalies while simultaneously having low numbers of false positives. In addition to the good detection performance, the presented system can operate in real-time and is also very flexible and easy to expand.
Decentralized Reinforced Anonymous FLchain: a Secure Federated Learning Architecture for the Medical Industry

In the age of big data, data has already become a "high-value commodity" with clear price. Privacy leaks can lead to personal information security violations during training in federated learning, especially in the medical industry. The data of the medical industry is characterized by large amount of data and high demand for privacy. For this reason, we designed a Decentralized Reinforced Anonymous Federated Learning Based on Blockchain (DRA-FLchain) with high privacy protection and strong anonymity. In view of the large amount of data in the medical industry, DRA-FLchain uses blockchain technology to enable a large number of clients to participate in model training, and also uses cut through technology to reduce the cost of storage space. DRA-FLchain uses the blockchain and ring signature to ensure the anonymity of the client’s identity, and also uses homomorphic encryption and mask to protect the security of the model. For anonymous Federated Learning (FL), we set up a novel reward mechanism based on game theory Reward mechanism based on ring signature (RMBRS), which can distribute rewards fairly in the anonymous FL architecture. We compared the accuracy and operation efficiency of FL, Federated Learning Based on Blockchain (FLchain) and DRA-FLchain through experiments. The experimental results show that DRA-FLchain is an effective anonymous and secure architecture. Finally, we proved that DRA-FLchain can still protect the privacy of clients well in extreme cases through case study.
Multi-Agent Reinforcement Learning in Dynamic Industrial Context

Deep reinforcement learning has advanced signifi-cantly in recent years, and it is now used in embedded systems in addition to simulators and games. Reinforcement Learning (RL) algorithms are currently being used to enhance device operation so that they can learn on their own and offer clients better services. It has recently been studied in a variety of industrial applications. However, reinforcement learning, especially when controlling a large number of agents in an industrial environment, has been demonstrated to be unstable and unable to adapt to realistic situations when used in a real-world setting. To address this problem, the goal of this study is to enable multiple reinforcement learning agents to independently learn control policies on their own in dynamic industrial contexts. In order to solve the problem, we propose a dynamic multi-agent reinforcement learning (dynamic multi-RL) method along with adaptive exploration (AE) and vector-based action selection (VAS) techniques for accelerating model convergence and adapting to a complex industrial environment. The proposed algorithm is tested for validation in emergency situations within the telecommunications industry. In such circumstances, three unmanned aerial vehicles (UAV-BSs) are used to provide temporary coverage to mission-critical (MC) customers in disaster zones when the original serving base station (BS) is destroyed by natural disasters. The algorithm directs the participating agents automatically to enhance service quality. Our findings demonstrate that the proposed dynamic multi-RL algorithm can proficiently manage the learning of multiple agents and adjust to dynamic industrial environments. Additionally, it enhances learning speed and improves the quality of service.
Knowledge Graph Generation for Unstructured Data Using Data Processing Pipeline

The proliferation of technologies and unstructured data on the internet poses a persistent challenge in extracting valuable information from diverse formats. To address this, research leverages Machine Learning (ML) and Natural Language Processing (NLP) techniques. This study contributes to information extraction from unstructured text using a state-of-the-art pipeline, incorporating modules for coreference resolution (Neuralcoref), named entity linking (Wikifier API), and Relationship Extraction (RE) (OpenNRE and REBEL models). The resulting Knowledge Graph (KG) in Neo4j captures entity relationships. Experiments on a BBC news dataset analyzed the pipeline’s performance, focusing on RE. Accuracies of 61.4% (OpenNRE) and 87% (REBEL) were achieved. The research demonstrates the efficacy of the proposed pipeline in extracting structured knowledge from unstructured data, facilitating the preservation and utilization of valuable information.
The Graph-Massivizer Approach Toward a European Sustainable Data Center Digital Twin

Modeling and understanding an expensive next-generation data center operating at a sustainable exascale performance remains a challenge yet to solve. The paper presents the approach taken by the Graph-Massivizer project, funded by the European Union, towards a sustainable data center, targeting a massive graph representation and analysis of its digital twin. We introduce five interoperable open-source tools that support this undertaking, creating an automated, sustainable loop of graph creation, analytics, optimization, sustainable resource management, and operation, emphasizing state-of-the-art progress. We plan to employ the tools for designing a massive data center graph, representing a digital twin describing spatial, semantic, and temporal relationships between the monitoring metrics, hardware nodes, cooling equipment, and jobs. The project aims to strengthen Bologna Technopole as a leading European supercomputing and big data hub offering sustainable green computing for improved societally relevant science throughput.