Reviewerly/exHarmony huggingface dataset:
Arabic Non-Functional Requirements Extraction Using Machine Learning

Non-Functional Requirements (NFR) are a set of quality attributes that software must have, such as security, reliability, and performance. Extracting NFR from software requirement specifications can help developers deliver quality software that meets users' expectations. However, since functional and non-functional requirements are mixed in the same SRS, it requires a lot of human effort to distinguish them. While many studies have proposed English language requirements extracting techniques, there is a lack of research in Arabic requirements extracting, as well as a lack of publicly available Arabic datasets in this field. In this study, we propose an automatic NFR extraction method for quality software development by combining machine learning and feature extraction techniques. Also, we will collect an Arabic dataset for requirements. This study aims to help software engineers save time, reduce costs and effort in the manual extraction process, and make the requirements engineering phase more efficient. Additionally, it provides new research areas in this field.
A Survey of Internet of Things (IoT) Forensics Frameworks and Challenges

The Internet of Things has led to the deployment of billions of interconnected devices in critical infrastructures such as health, transportation, environmental control, and home automation. IoT offers convenience and dependability, but it also presents new risks for hackers and hurdles for digital forensics. Digital forensic investigations are made more difficult by the enormous range of IoT devices, non-standard file formats, and cloud infrastructure. End-to-end encryption puts forensic investigation success and privacy at risk. In particular, all legal, privacy, and cloud security issues will be addressed in this article along with other key issues that arise during the complex investigation process involving IoT. Also, this study offers a summary of the previous and present theoretical frameworks in the field of digital forensics
Human Uncertainty in Concept-Based AI Systems

Placing a human in the loop may help abate the risks of deploying AI systems in safety-critical settings (e.g., a clinician working with a medical AI system). However, mitigating risks arising from human error and uncertainty within such human-AI interactions is an important and understudied issue. In this work, we study human uncertainty in the context of concept-based models, a family of AI systems that enable human feedback via concept interventions where an expert intervenes on human-interpretable concepts relevant to the task. Prior work in this space often assumes that humans are oracles who are always certain and correct. Yet, real-world decision-making by humans is prone to occasional mistakes and uncertainty. We study how existing concept-based models deal with uncertain interventions from humans using two novel datasets: UMNIST, a visual dataset with controlled simulated uncertainty based on the MNIST dataset, and CUB-S, a relabeling of the popular CUB concept dataset with rich, densely-annotated soft labels from humans. We show that training with uncertain concept labels may help mitigate weaknesses of concept-based systems when handling uncertain interventions. These results allow us to identify several open challenges, which we argue can be tackled through future multidisciplinary research on building interactive uncertainty-aware systems. To facilitate further research, we release a new elicitation platform, UElic, to collect uncertain feedback from humans in collaborative prediction tasks.
Federated Transfer Learning for Energy Efficiency in Smart Buildings

Nowadays, the generation of energy consumption models in buildings needs to address several issues. Indeed, most existing buildings lack the appropriate equipment to obtain the data required to create such models. Furthermore, the nature of energy consumption data could be correlated with additional information about people in the building that could raise privacy concerns. Based on such aspects, we propose a Federated Transfer Learning (FTL) framework to handle these buildings' data without compromising any private information in which a set of buildings are clustered according to certain characteristics. On the one hand, our works leverage the properties of Federated Learning (FL) to train an energy forecasting model using a small portion of the available buildings respecting their privacy. On the other hand, we transfer the model to the rest of the buildings by using Transfer Learning (TL). We extensively evaluate our approach, and demonstrate it improves the results of alternative scenarios where FL and TL are used separately.
Hybrid Deep Learning Model for Time Series Anomaly Detection

Multivariate time series anomaly detection is a fundamental challenge in real-world applications such as industry and business1. To address this issue, numerous models with diverse structures have been proposed. Each model leverages its unique structural characteristics to extract crucial features for time series anomaly detection. Our objective is to assess whether the performance of multivariate time series anomaly detection can be improved by employing a combination of models with different structures. In this paper, we propose a hybrid model for multivariate time series anomaly detection. The proposed hybrid model comprises two sub-models, each with a unique structure, and a simple layer. Each sub-model is designed to extract significant features from input time series. The simple layer combines the extracted features from both sub-models to generate the final output. Experimental results demonstrate that the proposed hybrid model outperforms single models.
Integration of Digital Twin and Federated Learning for Securing Vehicular Internet of Things

In the present era of advanced technology, the Internet of Things (IoT) plays a crucial role in enabling smart connected environments. This includes various domains such as smart homes, smart healthcare, smart cities, smart vehicles, and many others. The IoT facilitates the integration and interconnection of devices, enabling them to communicate, share data, and work together to create intelligent and efficient systems. With ubiquitous smart connected devices and systems, a large amount of data associated with them is at a prime risk from malicious entities (e.g., users, devices, applications) in these systems. Innovative technologies, including cloud computing, Machine Learning (ML), and data analytics, support the development of anomaly detection models for the Vehicular Internet of Things (V-IoT), which encompasses collaborative automatic driving and enhanced transportation systems. However, traditional centralized anomaly detection models fail to provide better services for connected vehicles due to issues such as high latency, privacy leakage, performance overhead, and model drift.
CrossMoCo: Multi-Modal Momentum Contrastive Learning for Point Cloud

The point cloud is a 3D geometric data that lacks a specific structure and is permutation-invariant. The applications of point clouds have gained significant attention recently in the field of vision tasks. However, most existing works on point clouds utilize supervised learning on large labelled data, which are costly and laborious to collect. To this end, unsupervised learning, for example, self-supervised learning, has shown promising performance in various tasks of 2D computer vision and holds the potential in 3D computer vision applications. In this study, we introduce a novel selfsupervised method called CrossMoCo, which learns the representations of unlabelled point cloud data in a multi-modal setup that also utilizes the 2D rendered images of the point clouds. CrossMoCo outperforms existing methods on multimodal self-supervised learning on point cloud by introducing two new concepts: momentum contrastive learning with more negative samples and multiple-view intra-modal contrastive learning. The first component learns from an online encoder and a momentum encoder with a large number of negative samples, which provides consistent learning signals. The second component enforces consistency between different views of the samples of the same modality, thereby improving multimodal representation. We conduct extensive studies on two popular benchmark datasets (ModelNet40 and ScanObjectNN) for linear classification and few-shot learning tasks. Our results demonstrate that CrossMoCo achieves superior performance over existing methods for both tasks on both datasets, achieving up to 4.36% improvement on linear classification and up to 9.2% on few-shot tasks. Our code is available at https://github.com/snehaputul/CrossMoCo.
Contrastive Learning for Self-Supervised Pre-Training of Point Cloud Segmentation Networks With Image Data

Reducing the quantity of annotations required for supervised training is vital when labels are scarce and costly. This reduction is particularly important for semantic segmentation tasks involving 3D datasets, which are often significantly smaller and more challenging to annotate than their image-based counterparts. Self-supervised pre-training on unlabelled data is one way to reduce the amount of manual annotations needed. Previous work has focused on pre-training with point clouds exclusively. While useful, this approach often requires two or more registered views. In the present work, we combine image and point cloud modalities by first learning self-supervised image features and then using these features to train a 3D model. By incorporating image data, which is often included in many 3D datasets, our pre-training method only requires a single scan of a scene and can be applied to cases where localization information is unavailable. We demonstrate that our pre-training approach, despite using single scans, achieves comparable performance to other multi-scan, point cloud-only methods.
Truthful Incentive Mechanism for Federated Learning with Crowdsourced Data Labeling

Federated learning (FL) has recently emerged as a promising paradigm that trains machine learning (ML) models on clients' devices in a distributed manner without the need of transmitting clients' data to the FL server. In many applications of ML (e.g., image classification), the labels of training data need to be generated manually by human agents (e.g., recognizing and annotating objects in an image), which are usually costly and error-prone. In this paper, we study FL with crowdsourced data labeling where the local data of each participating client of FL are labeled manually by the client. We consider the strategic behavior of clients who may not make desired effort in their local data labeling and local model computation (quantified by the mini-batch size used in the stochastic gradient computation), and may misreport their local models to the FL server. We first characterize the performance bounds on the training loss as a function of clients' data labeling effort, local computation effort, and reported local models, which reveal the impacts of these factors on the training loss. With these insights, we devise Labeling and Computation Effort and local Model Elicitation (LCEME) mechanisms which incentivize strategic clients to make truthful efforts as desired by the server in local data labeling and local model computation, and also report true local models to the server. The truthful design of the LCEME mechanism exploits the non-trivial dependence of the training loss on clients' hidden efforts and private local models, and overcomes the intricate coupling in the joint elicitation of clients' efforts and local models. Under the LCEME mechanism, we characterize the server’s optimal local computation effort assignments and analyze their performance. We evaluate the proposed FL algorithms with crowdsourced data labeling and the LCEME mechanism for the MNIST-based hand-written digit classification. The results corroborate the improved learning accuracy and cost-effectiveness of the proposed approaches.
Real-Time Instance Segmentation with Polygons Using an Intersection-Over-Union Loss

Predicting a binary mask for an object is more accurate but also more computationally expensive than a bounding box. Polygonal masks as developed in CenterPoly can be a good compromise. In this paper, we improve over CenterPoly by enhancing the classical regression L1 loss with a novel region-based loss and a novel order loss, as well as with a new training process for the vertices prediction head. Moreover, the previous methods that predict polygonal masks use different coordinate systems, but it is not clear if one is better than another, if we abstract the architecture requirement. We therefore investigate their impact on the prediction. We also use a new evaluation protocol with oracle predictions for the detection head, to further isolate the segmentation process and better compare the polygonal masks with binary masks. Our instance segmentation method is trained and tested with challenging datasets containing urban scenes, with a high density of road users. Experiments show, in particular, that using a combination of a regression loss and a region-based loss allows significant improvements on the Cityscapes and IDD test set compared to CenterPoly. Moreover the inference stage remains fast enough to reach real-time performance with an average of 0.045 s per frame for 2048x1024 images on a single RTX 2070 GPU. The code is available at: https://github.com/KatiaJDL/CenterPoly-v2.
On the Capacity Region of a Quantum Switch with Entanglement Purification

Quantum switches are envisioned to be an integral component of future entanglement distribution networks. They can provide high quality entanglement distribution service to end-users by performing quantum operations such as entanglement swapping and entanglement purification. In this work, we characterize the capacity region of such a quantum switch under noisy channel transmissions and imperfect quantum operations. We express the capacity region as a function of the channel and network parameters (link and entanglement swap success probability), entanglement purification yield and application level parameters (target fidelity threshold). In particular, we provide necessary conditions to verify if a set of request rates belong to the capacity region of the switch. We use these conditions to find the maximum achievable end-to-end user entanglement generation throughput by solving a set of linear optimization problems. We develop a max-weight scheduling policy and prove that the policy stabilizes the switch for all feasible request arrival rates. As we develop scheduling policies, we also generate new results for computing the conditional yield distribution of different classes of purification protocols. The conclusions obtained in this work can yield useful guidelines for subsequent quantum switch designs.
ChatGPT Impacts on Academia

This research endeavors to examine the impact of Chat Generative Pre-trained Transformer (ChatGPT) on the education system, specifically in the realm of academia and the challenges that it poses. The incorporation of ChatGPT into academic practices may necessitate a reevaluation of current assessment and evaluation systems. The integration of ChatGPT into the academic world raises important questions regarding the ethics of AI-generated authorship and the implications it has on the value of creative work. This new chatbot has the potential to revolutionize various fields, particularly education and creative works, including art, music, creative writing, and all areas of humanity subjects. The paper presents a qualitative analysis of the implications of ChatGPT on the education system and academic research domain. The future trajectory of this new technology is not unlike that of other previous technological innovations but AI-chatbot technology is expected to reshape the value of knowledge. This study aims to shed light on these pressing issues and present a possible compromise strategy for resolving them.
An Effective Med-VQA Method Using a Transformer with Weights Fusion of Multiple Fine-Tuned Models

Visual question answering (VQA) is a task that generates or predicts an answer to a question in human language about visual images. VQA is an active field combining two AI branches: NLP and computer vision. VQA in the medical field is still at an early stage, and it needs vast efforts and exploration to reach practical usage. This paper proposes two models that are utilized in the latest vision and NLP transformers that outperform the SOTA and have not yet been utilized in medical VQA. The ELECTRA-base transformer is used for textual feature extraction, whereas SWIN is used for visual feature extraction. In the SOTA medical VQA, selecting the model is based on the model that achieves the highest validation accuracy or the last model in training. The first proposed model, the best-value-based model, is selected based on the highest validation accuracy. The second model, the greedy-soup-based model, uses a greedy soup technique based on the fusion of multiple fine-tuned models to set the model parameters. The greedy soup selects the model parameters by fusing the model parameters that have significant performance on the validation accuracy in training. The greedy-soup-based model outperforms the best-value-based model, and both proposed models outperform the SOTA, which has an accuracy of 83.49%. The greedy-soup-based model is optimized with batch size and learning rate. During the optimization, seven extra models exceed the SOTA accuracy. The best model trained with a learning rate of 1.0×10−4 and batch size 16 achieves an accuracy of 87.41%.
Image Formation Algorithms for Low-Cost Freehand Ultrasound Scanner Based on Ego-Motion Estimation and Unsupervised Clustering

This paper describes the application of unsupervised learning techniques to improve ego-motion estimation for a low-cost freehand ultrasound probe. Echo decorrelation measurements, which are used to estimate the lateral velocity of a scanning probe as it is passed over the skin, are found to be sensitive to varying tissue types and echogenicity in the imaged scene, and this can impact the geometric accuracy of the generated images. Here, we investigate algorithms to cluster the collated 1D echo data into regions of different echogenicity by applying a Gaussian mixture model (GMM), spatial fuzzy c-means (SFCM) or k-means clustering techniques, after which the decorrelation measurements can focus on the regions that yield the most accurate velocity estimates. A specially designed mechanical rig is used to provide the ground truth for the quantitative analysis of probe position estimation on phantom and in vivo data using different clustering techniques. It is concluded that the GMM is the most effective in classifying regions of echo data, leading to the reconstruction of the most geometrically correct 2D B-mode ultrasound image.
Deep Learning in the Recognition of Activities of Daily Living Using Smartwatch Data

The recognition of human activities (HAR) using wearable device data, such as smartwatches, has gained significant attention in the field of computer science due to its potential to provide insights into individuals' daily activities. This article aims to conduct a comparative study of deep learning techniques for recognizing activities of daily living (ADL). A mapping of HAR techniques was performed, and three techniques were selected for evaluation, along with a dataset. Experiments were conducted using the selected techniques to assess their performance in ADL recognition, employing standardized evaluation metrics, such as accuracy, precision, recall, and F1-score. Among the evaluated techniques, the DeepConvLSTM architecture, consisting of recurrent convolutional layers and a single LSTM layer, achieved the most promising results. These findings suggest that software applications utilizing this architecture can assist smartwatch users in understanding their movement routines more quickly and accurately.
Proposal for a Lorenz qubit

Nonlinear qubit master equations have recently been shown to exhibit rich dynamical phenomena such as period doubling, Hopf bifurcation, and strange attractors usually associated with classical nonlinear systems. Here we investigate nonlinear qubit models that support tunable Lorenz attractors. A Lorenz qubit could be realized experimentally by combining qubit torsion, generated by real or simulated mean field dynamics, with linear amplification and dissipation. This would extend engineered Lorenz systems to the quantum regime, allowing for their direct experimental study and possible application to quantum information processing.
How might blockchain technology be used in the food supply chain? A systematic literature review

How might blockchain technology be used in the food supply chain? A systematic literature reviewAll authorsRetno Astuti & Luki Hidayatihttps://doi.org/10.1080/23311975.2023.2246739Published online:29 August 2023Display full sizeThe complexity of the food supply chain necessitates the implementation of traceability systems. The traceability system optimized the food supply chain management. Blockchain technology promises a new distributed ledger, which provides various uses by solving several traceability issues. Unfortunately, the present study does not completely address the use, opportunities, and challenges of blockchain technology in the food supply chain. Therefore, this study investigates the general knowledge regarding blockchain technology (the concept, features, and type of blockchain) and its use in the food supply chain. The findings of this study assist in gaining a better understanding and information regarding how to strengthen the traceability system in the food supply chain by optimizing the use of blockchain technology. The findings confirmed that the use of blockchain technology in the food supply chain was primarily for traceability, with public/private blockchain assisting as a popular platform. The transaction data is about the product origin information, transaction information, and product label information. The use of blockchain in the food supply chain has the benefit of enhancing food supply chain traceability and transparency. The challenges are the need for significant financial, human resources, and infrastructure investment, limited technical knowledge of stakeholders, significant product changes, the rapid development of the global supply chain, the potential for data manipulation, and policy change.
An activity recognition system at home based on illuminance sensors

Abstract A social issue that leads to the safety and security of people is the network that monitors the safety of people living alone in remote areas. Various research and developments of indoor human activity recognition using IoT sensor nodes have been conducted in recent years. In this research, we focus on turning on and off indoor lighting and propose an activity recognition system at home using an illuminance sensor. An IoT sensor node with a built‐in illuminance sensor is attached to the wall near the indoor lighting equipment. The living activities of people in the room are estimated from changes in the illuminance. We assume a significant change in the illuminance of the indoor lighting is observed at least once a day at the sampling interval of the IoT sensor node. In that case, it is possible to estimate the operation of the indoor lighting due to daily activities and to confirm the safety of people living alone in remote areas. The activity recognition system was evaluated for a total of 380 days, excluding the missing period. The estimation of indoor lighting manipulation by daily activities was accurate for 376 days. Precision, Recall, and F‐measure score values, which are evaluation indices for activity estimation, were 94.9%, 98.2%, and 96.5%, respectively.