Reviewerly/exHarmony huggingface dataset:
FinOps in Cloud-Native Near Real-Time Serverless Streaming Solutions

FinOps is a novel discipline in cloud computing and technology management that aims to optimize an organization’s cloud spending, enhance financial resource management, and promote collaboration between technology and finance teams for efficient cloud resource utilization and cost control. The adoption of cloud-native and serverless architectures has revolutionized the way organizations design and deploy their near-real-time streaming solutions. These solutions are vital for various applications, including data analytics, monitoring, and content delivery. However, understanding the cost implications of scaling these solutions to accommodate varying numbers of concurrent users remains a challenge. This paper presents a cost analysis for a cloud-native near real-time serverless streaming solution, considering both streaming and idle times of the system. The cost of cloud services is heavily influenced by efficient processing management and the selection of suitable services. This decision can result in substantial cost differences across various cloud providers, varying from expensive to cost-efficient options. This paper explores the critical factors impacting costs, highlighting differences in cost levels based on several factors.
Detection of Real Concept Drift Under Noisy Data Stream

Concept drift detection in noisy data streams is challenging yet essential. This paper introduces NPRDD, a new concept drift detection algorithm that is robust to noise and accurately identifies Real drifts. NPRDD operates on a moving window of recent data, utilizing predicted class probabilities and cross-entropy-based surprise measures to weigh real drift candidates. In line with the Bayesian definition of Real concept drift, NPRDD considers a sample as a drift candidate when the classifier makes an error but is highly confident in its judgment. We evaluate NPRDD on synthetic datasets by varying the noise levels and comparing its performance with other well-established methods. Our results show that NPRDD outperforms other methods regarding ROC-AUC and Accuracy metrics.
Real-Time Clustering of Text Data for News Aggregation

This paper explores real-time text data clustering in news aggregation using the Message Passing Interface (MPI) with parallelized K-Means algorithm variants. We evaluate batch-based, centroid-based, and fusion-based methods, measuring their training time in two experiments—one based on cluster complexity and the other on dataset size. Our study aims to identify the most effective method and analyze trade-offs between parallelization strategies. Results indicate that MPI-based solutions substantially accelerate training time compared to serial K-Means implementation in this context.
Expressive multilingual speech synthesizer

The research presented in the paper addresses the challenge of multilingual text-to-speech with the specific aim to be able to generate speech in combination of speaker and language which is absent from the training dataset. This scenario is commonly referred to as cross-lingual speech synthesis. The innovative models introduced in the paper accomplish cross-lingual speech synthesis by applying neural network embeddings, not only to speaker and speaking style identifiers but also to context-dependent phonemes and various prosodic events. This approach enables the model to effectively capture relationships between phonemes and prosodic events across different languages, thus enabling the synthesis of speech in the voice of an individual who has never spoken the target language. Subjective and objective evaluation of various aspects of synthesized speech have confirmed that the proposed models are able to synthesize high-quality speech and to maintain the properties of the voice of the original speaker in a cross-lingual scenario.
Extracting important layout features from the web page content

The visual layout has an enormous influence on human perception and is a subject of many studies, including research on web page similarity comparison. Structure-based approaches use the possibility of direct access to HTML content, whereas visual methods have widespread usage due to the ability to analyze image screenshots of entire web pages. A solution described within this paper will focus on extracting web page layout in forms needed by both above-mentioned approaches.
Virtual Mouse with Hand Gestures Using Machine Learning

Scientists and researchers are currently developing ways to make the electronic devices more user friendly and less reliant on physical interaction. In line with this trend, this research study has proposed an interactive computer system, which can function without the need for a traditional mouse. Instead, we plan to leverage the power of cameras and computer vision technologies to manage tasks that would typically require a mouse, such as clicking and scrolling. This research study demonstrates how this system can replicate all the functionality of existing mouse devices, without requiring the physical use of one. To achieve this, we will be utilizing programming languages such as Python and cutting-edge computer vision software like OpenCV. These tools will be instrumental to build a system that can interpret visual input from a camera, translate it into commands, and execute them in real-time. By doing so, we hope to create a more intuitive and user-friendly way of interacting with the devices used, without the limitations of traditional mouse devices.
Limitations of AWS and GCP Serverless Functions

Cloud providers offer serverless functions such that users can focus on the business logic and worry only about the code and application, leaving scaling, security, software updates, and everything else to the cloud provider. However, the minimal user interactions with the configuration of the functions may result in errors executing the code or may not scale properly. In this research, we analyze different implementations of a complex cloud function that requires execution in less than 10 seconds and analyze the responses from AWS and GCP serverless platforms. We observed that AWS can activate a maximum of 1000 instances, although they claim a higher number, while GCP can reach a maximum of 3000 active cloud instances. However, GCP faces another problem of activating only a smaller number of cloud instances for executing small-sized functions. This limitation might surprise the user since it generates a lot of errors with a “request timeout” response. We elaborate on lessons learned by comparing AWS and GCP functions with details on the bad performance of GCP Cloud Functions in our experiments.
An Innovative Approach for Osteosarcoma Bone Cancer Detection based on Attention Embedded R-CNN Approach

The malignant bone tumor osteosarcoma. Any bone is at risk, but lengthy bones like the limbs are more vulnerable. Although the precise cause of this malignant growth is uncertain, experts concur that it is caused by changes to deoxyribonucleic acid (DNA) inside the bones. This can cause the breakdown of good tissue and the growth of aberrant, pathological bone. Osteosarcoma has a 76% cure rate if detected early and treated before it spreads to other parts of the body. An X-ray is the primary tool for detecting bone tumors. Bone X-rays and other imaging tests can help detect osteosarcoma. A biopsy should be performed for an accurate diagnosis. This is a time-consuming and tedious task that might be greatly reduced with the help of appropriate tools. Data preprocessing, segmentation, feature extraction, and model training are the four main pillars of the proposed approach. Unwanted noises can be filtered out with some preprocessing. Low-spatial-frequency and high-spatial-frequency components are separated using segmentation. The proposed approach employed Tumor Border Clarity, Joint Distance, Tumor Texture, and other features for feature extraction. Let's move on to A-Residual CNN model training. The success percentage of the proposed approach was 96.39 percent.
Knowledge Extraction About Beer Classification Using Evolving Fuzzy Neural Networks

Evolving Fuzzy Neural Networks (EFNNs) are well-regarded for their interpretability and proficiency in pattern classification tasks. However, their accuracy may need to be improved when confronted with limited samples for specific classes or the emergence of new classes in the data stream. To overcome this limitation, we applied the EFNN-Gen, a novel approach that integrates a priori knowledge through generalist rules to solve a beer classification problem. These rules are derived from assessing the specificity of Gaussian functions within the first layer neurons of the EFNN. They represent expert knowledge about the classification problem and are aimed at enhancing the network's performance. Experimental tests conducted on the Beer dataset, a real-world multiclass pattern classification dataset, demonstrate that integrating generalist rules leads to a significant accuracy improvement of 97.14%.
Image Forgery Detection Algorithm Using Particle Swarm Optimization

Copy-move forgery is one of the most used manipulations for tampering with digital images. The authenticity of the image becomes more crucial when the images are used in important processes. keypoints-based algorithms have been reported to be very effective in revealing copy-move evidence due to their robustness against various attacks. However, these approaches sometimes fail to make good prediction because of different factors such small number of keypoints detected, or wrongly detected keypoints. Matching the correct keypoints and filtering the wrong keypoints are other difficult tasks. One reason behind these issues is the parameters used to configure the key point detection algorithm. In this paper, another CMF (copy-move forgery) detection algorithm is proposed, by applying particle swarm optimization to find the best parameters for the algorithm for all different phases. Furthermore, filtering is achieved through two stages to remove most of the wrong keypoints detected. Additionally, triangulation is used as another technique applied to the algorithm in order to increase the detection area. Experimental results shows that the algorithm has good performance.
Quantum Representation Based Job Shop Scheduling

This paper proposes a quantum representation-based genetic algorithm for solving the job-shop scheduling problem, aiming to minimize the makespan. The job-shop scheduling is a typical scheduling problem that falls under the NP-hard combinatorial optimization problems and has undergone extensive investigation in the literature. Over time, various heuristic and intelligent methods have been developed to tackle this challenging problem. Inspired by the promise of quantum computing, this paper explores using quantum information representation and processing techniques to enhance the performance of conventional genetic algorithms on classical computers to solve the job-shop scheduling problem. The proposed quantum-inspired genetic algorithm employs a conversion mechanism of quantum representation to code the schedule; and utilizes a rotation angle table to update the population. The effectiveness of the quantum-inspired genetic algorithm is compared to that of a standard genetic algorithm, with experimental results confirming the potential of the proposed approach in tackling complex combinatorial optimization problems.
HSI-Drive v2.0: More Data for New Challenges in Scene Understanding for Autonomous Driving

We present the updated version of the HSI-Drive dataset aimed at developing automated driving systems (ADS) using hyperspectral imaging (HSI). The v2.0 version includes new annotated images from videos recorded during winter and fall in real driving scenarios. Added to the spring and summer images included in the previous v1.1 version, the new dataset contains 752 images covering the four seasons. In this paper, we show the improvements achieved over previously published results obtained on the v1.1 dataset, showcasing the enhanced performance of models trained on the new v2.0 dataset. We also show the progress made in comprehensive scene understanding by experimenting with more capable image segmentation models. These models include new segmentation categories aimed at the identification of essential road safety objects such as the presence of vehicles and road signs, as well as highly vulnerable groups like pedestrians and cyclists. In addition, we provide evidence of the performance and robustness of the models when applied to segmenting HSI video sequences captured in various environments and conditions. Finally, for a correct assessment of the results described in this work, the constraints imposed by the processing platforms that can sensibly be deployed in vehicles for ADS must be taken into account. Thus, and although implementation details are out of the scope of this paper, we focus our research on the development of computationally efficient, lightweight ML models that can eventually operate at high throughput rates. The dataset and some examples of segmented videos are available in https://ipaccess.ehu.eus/HSI-Drive/.
A Large-scale Fine-grained Empirical Study on Security Concerns in Open-source Software

We conducted a large-scale fine-grained empirical study in which we quantitatively analyzed the commit histories of 200 Open-Source (OS) Python software systems, whose software repositories were publicly available on GitHub, for a total of 164,980 commits analyzed. We focused on commits—this is why our study is considered fine-grained—to investigate the spread and evolution of security concerns. To detect security concerns at a commit level, we used SonarQube, a popular Static Application Security Testing (SAST) tool. We found, among other things, that: security concerns are spread in OS Python software systems (on average, about 11 security concerns per commit) and tend to survive more than a couple of weeks and a dozen commits; and critical security concerns, despite their high severity level, are the most spread and tend to survive the most. Also, we found that 47 different kinds of security concerns were introduced into the source code of the studied software systems, and the top eight (per number of introductions) are severe and account for 87% of all introduced security concerns. Python developers should pay more attention to security concerns, especially those critical, and use secure coding practices, automated tools, or even DevSecOps to avoid the introduction of security concerns into their source code or fix them as soon as possible.
An “80-20” Approach to the Study of Coupling

In this paper, we use both afferent (i.e., incoming) and efferent (i.e., outgoing) class coupling to analyse trends in seven open-source and four industrial systems. We determine whether an 80-20 rule (or Pareto’s Law) applies to either type of coupling and whether 20% of classes contain 80% of coupling (whether incoming and outgoing). We also explore the relationship between bugs and both types of coupling. Results showed that an 80-20 rule applied overwhelmingly in the industrial systems (but not in the open-source). For the two types of coupling, the open-source systems also had a stronger, positive relationship with bugs; opposite effects were found in the industrial systems. These results highlight the need for adoption of different and impactful approaches to understanding systems which reflect on developer practice.
Low Latency Fiber Communication System Equalizer Based on Photonic Reservoir Computing

We demonstrate a low latency fiber channel distortion equalizer that implements mainly of the equalization calculations in the optical domain, based on an integrated photonic reservoir computing chip. We discussed the equalization effect of the equalizer under different dispersion noise and optical signal-to-noise ratio (OSNR) conditions. The bit error rate (BER) of the C-band with 25Gb/s on-off key (OOK) distorted signal in 25km standard single-mode fiber (SSMF) has been reduced by 3 orders of magnitude. By optimizing weights, this equalizer can adapt to link impairments. It can effectively equalize the distorted signals at data rates of 10–40 Gb/s over 25km in SSMF transmission. Furthermore, it can efficiently equalize the impaired signals after 5-40km SSMF transmission at a signal rate of 25Gb/s.
The Yin and Yang of Software Quality: On the Relationship between Design Patterns and Code Smells

Software reuse is considered the silver bullet of software engineering. It has been largely demonstrated that the proper implementation of design and reuse principles can substantially reduce the effort, time, and costs required to develop software systems. Design patterns are one of the most affirmed techniques for source code reuse. While previous work pointed out their benefits in terms of maintainability and understandability, some seem to raise the opposite concern, suggesting that they can negatively impact code quality from the developers’ perspectives. We recognize such discrepancy in the literature, and we aim to fill this gap by investigating whether and how design patterns are related to the emergence of issues compromising code understandability, namely the Complex Class, God Class, and Spaghetti Code smells, which have been also shown to increase the change- and fault-proneness of code. We perform an empirical evaluation on 15 Java projects evolving over 542 releases, and we find that, although design patterns are supposed to improve code quality without prejudice, they can be related to dangerous issues, as we observe the emergence of code smells in the classes participating in their implementation. From our findings, we distill a number of implications for developers and project managers to support them in dealing with design patterns.
Thrifty Guardians: Overcoming the Challenges of Establishing Security Champions on a Limited Budget

In this case study conducted over a six-months period where a Security Champions team was established, and an additional month for the remaining data collection and analysis, we explore the challenges and strategies in forming and sustaining a Security Champion team in a mid-size software engineering organization, given a very limited budget. Our research questions focus on identifying the main challenges and key success strategies when establishing such a team, to enable security awareness, competence and focus, with cost-efficient approaches. The chosen research methodology consisted of an exploratory case study, involving 11 Security Champion team members and two key stakeholders. A mixed-method approach was used, collecting data via semi-structured interviews and an online self-assessment survey, further processed with structural coding to identify key success factors and challenges. Our study shows that it is possible to establish a Security Champion team on a strictly limited budget, given the following identified key success factors: the team leader possesses prior experience with the concept, the team have short but frequent meetings, there is buy-in from management, team members, and other security organizations, and there are enough situations where the Champions can apply their skills. However, challenges persist, including lack of clear goals, unclear prioritization, lack of internal support, lack of buy-in from other security organizations and efficient ways to utilize the Champions’ skills within their teams. This study contributes by identifying key success factors and challenges to mitigate, for organizations looking to establish a Security Champion team with limited resources.
Medical Dataset Preparation and Privacy Preservation for Improving the Healthcare Facilities Using Federated Learning Approach

As a result of recent advances in deep learning, several breakthrough stories in modern medical diagnostics with data-driven insights into improving healthcare facilities' quality of treatment have arisen. Deep learning methods that perform well are significantly data-driven. As more data is trained, the deep learning model's performance becomes much more robust and generalizable. On the other hand, collecting medical data in a central storage system to train effective deep learning models raises concerns about privacy, ownership, and regulatory compliance. Federated learning overcomes the previous difficulties by using a deep learning model which is shared and a centralized aggregating platform. On the other side, patient data resides with the local party, assuring data confidentiality and data security. First, we give a comprehensive, up-to-date survey of federated learning research in healthcare applications. Next, we propose a solution for preparation of the medical dataset for federated learning approach from publicly available medical repositories and then apply federated averaging(FedAvg) and FedProx algorithm for aggregating across clients without accessing local private data.
Enhancing Residential Security with AI-Powered Intrusion Detection Systems

Using Intrusion Detection Systems (IDS) powered by artificial intelligence is presented in the proposed work as a novel method for enhancing residential security. The overarching goal of the study is to design, develop, and evaluate a system that employs artificial intelligence techniques for real-time detection and prevention of unauthorized access in response to the rising demand for such measures. Using anomaly detection, neural networks, and decision trees, which are all examples of machine learning algorithms that benefit from the incorporation of data from multiple sensors, the proposed system guarantees the accurate identification of suspicious activities. Proposed work examines large datasets and compares them to conventional security measures to demonstrate the system's superior performance and prospective impact on reducing home intrusions. Proposed work contributes to the field of residential security by proposing a dependable, adaptable, and intelligent method for protecting homes against the ever-changing types of infiltration threats that exist today.
Quantum and Classical Computing using Machine Learning Techniques

The field of quantum computing is in the midst of a period of transformation as a result of the recent discovery of quantum entanglement. It is designed to perform computations that classical computers cannot handle since it is based on and operates in compliance with the rules of quantum mechanics. Because it operates on certain premises, it enables this to happen. Quantum bits, also known as qubits, have the unique ability to represent many states at once, unlike their conventional analogues. However, classical bits can only take on the values 0 or 1, and no other values in between. Quantum bits are capable of existing in a wide variety of states simultaneously. As a result of their unique ability to explore multiple solutions at once, quantum computers have the potential to dramatically boost the processing speed with which certain problems may be solved. Furthermore, qubits can show entanglement, in which the state of one qubit becomes intertwined with the state of another qubit, even if the qubits are separated by a great distance. Quantum entanglement describes this strange occurrence. This entanglement provides the way for the creation of new algorithms and enhanced computing power by simplifying the execution of complex interactions. Although difficulties like qubit stability and error correction must be overcome, quantum computing has the potential to revolutionize many different areas, including cryptography, optimization, and scientific discovery. Although significant strides have been made, the implementation of large-scale quantum computers remains in its infancy, necessitating ongoing research and development.