Reviewerly/exHarmony huggingface dataset:
The Hidden Costs of Ignoring Cash Flow: A Call for Strategic Requirements Prioritization at Startups During an Era of Rising Interest Rates

Cash flow management, a key factor in 82% of startup failures, has surprisingly only been referenced once as a criterion for Requirements Prioritization (RP) in a 1983 study. Only 5 studies have been identified that consider one or more financial ratios during RP. Therefore it's fair to state that this type of RP criteria is underrepresented. The current financial climate is marked by a steady rise in interest rates the past two years, reaching a peak unseen in previous decade. Consequently, investors have become more discerning, no longer investing hastily into software startups at the first glimpse of traction or user growth. There is a renewed emphasis on financial health. Therefore adept cash flow management is top of mind again as a pivotal consideration by private equity investors. Should startups persist in their currently employed RP criteria, the 63% failure rate is set to rise due to funding difficulties tied to an overemphasis on growth and user value, rather than cash flow as RP criterion. To address this, future research should aim to evolve prioritization methods to enhance early-stage decision-making and increase startup success rates. This study not only has practical implications for startups but also paves the way for further collaborative academic research in Requirements Engineering (RE) and contributes to the creation of the first academically supported Pragmatic Framework for Product Managers (PFPM) at Software Startups.
Multi-Modal Emotion Recognition for Enhanced Requirements Engineering: A Novel Approach

Requirements engineering (RE) plays a crucial role in developing software systems by bridging the gap between stakeholders' needs and system specifications. However, effective communication and elicitation of stakeholder requirements can be challenging, as traditional RE methods often overlook emotional cues. This paper introduces a multi-modal emotion recognition platform (MEmoRE) to enhance the requirements engineering process by capturing and analyzing the emotional cues of stakeholders in real-time. MEmoRE leverages state-of-the-art emotion recognition techniques, integrating facial expression, vocal intonation, and textual sentiment analysis to comprehensively understand stakeholder emotions. This multimodal approach ensures the accurate and timely detection of emotional cues, enabling requirements engineers to tailor their elicitation strategies and improve overall communication with stakeholders. We further intend to employ our platform for later RE stages, such as requirements reviews and usability testing. By integrating multi-modal emotion recognition into requirements engineering, we aim to pave the way for more empathetic, effective, and successful software development processes. We performed a preliminary evaluation of our platform. This paper reports on the platform design, preliminary evaluation, and future development plan as an ongoing project.
AutoReco: A Tool for Recommending Requirements for their Non-Conformance with Requirement Templates (RTs)

RTs generally possess a fixed syntactic structure and comprise pre-defined slots, and requirements written in the format of RTs must conform with the template structure. If the requirements do not conform to the RT, manually rewriting them to adhere to the RTs structure is tedious. In this paper, we develop the AutoReco tool for the automated recommendation of functional requirements for non-conformance with requirement templates (RTs). Our preliminary results on nine case studies show an accuracy of 83.9% for providing recommendations to non-conformant requirements with RTs. Demonstration video: https://youtu.be/OSX71cP-oXw
SAFER: Safe Collision Avoidance Using Focused and Efficient Trajectory Search with Reinforcement Learning

Collision avoidance is key for mobile robots and agents to operate safely in the real world. In this work we present SAFER, an efficient and effective collision avoidance system that is able to improve safety by correcting the control commands sent by an operator. It combines real-world reinforcement learning (RL), search-based online trajectory planning, and automatic emergency intervention, e.g. automatic emergency braking (AEB). The goal of the RL is to learn an effective corrective control action that is used in a focused search for collision-free trajectories, and to reduce the frequency of triggering automatic emergency braking. This novel setup enables the RL policy to learn safely and directly on mobile robots in a real-world indoor environment, minimizing actual crashes even during training. Our real-world experiments show that, when compared with several baselines, our approach enjoys a higher average speed, lower crash rate, less emergency intervention, smaller computation overhead, and smoother overall control.
Connectivity-Aware Semi-Decentralized Federated Learning over Time-Varying D2D Networks

Semi-decentralized federated learning blends the conventional device-to-server (D2S) interaction structure of federated model training with localized device-to-device (D2D) communications. We study this architecture over practical edge networks with multiple D2D clusters modeled as time-varying and directed communication graphs. Our investigation results in an algorithm that controls the fundamental trade-off between (a) the rate of convergence of the model training process towards the global optimizer, and (b) the number of D2S transmissions required for global aggregation. Specifically, in our semi-decentralized methodology, D2D consensus updates are injected into the federated averaging framework based on column-stochastic weight matrices that encapsulate the connectivity within the clusters. To arrive at our algorithm, we show how the expected optimality gap in the current global model depends on the greatest two singular values of the weighted adjacency matrices (and hence on the densities) of the D2D clusters. We then derive tight bounds on these singular values in terms of the node degrees of the D2D clusters, and we use the resulting expressions to design a threshold on the number of clients required to participate in any given global aggregation round so as to ensure a desired convergence rate. Simulations performed on real-world datasets reveal that our connectivity-aware algorithm reduces the total communication cost required to reach a target accuracy significantly compared with baselines depending on the connectivity structure and the learning task.
Human-like scene interpretation by a guided counterstream processing

In modeling vision, there has been a remarkable progress in recognizing a range of scene components, but the problem of analyzing full scenes, an ultimate goal of visual perception, is still largely open. To deal with complete scenes, recent work focused on the training of models for extracting the full graph-like structure of a scene. In contrast with scene graphs, humans' scene perception focuses on selected structures in the scene, starting with a limited interpretation and evolving sequentially in a goal-directed manner [G. L. Malcolm, I. I. A. Groen, C. I. Baker, Trends. Cogn. Sci.20, 843-856 (2016)]. Guidance is crucial throughout scene interpretation since the extraction of full scene representation is often infeasible. Here, we present a model that performs human-like guided scene interpretation, using an iterative bottom-up, top-down processing, in a "counterstream" structure motivated by cortical circuitry. The process proceeds by the sequential application of top-down instructions that guide the interpretation process. The results show how scene structures of interest to the viewer are extracted by an automatically selected sequence of top-down instructions. The model shows two further benefits. One is an inherent capability to deal well with the problem of combinatorial generalization-generalizing broadly to unseen scene configurations, which is limited in current network models [B. Lake, M. Baroni, 35th International Conference on Machine Learning, ICML 2018 (2018)]. The second is the ability to combine visual with nonvisual information at each cycle of the interpretation process, which is a key aspect for modeling human perception as well as advancing AI vision systems.
Event-triggered risk-sensitive smoothing for linear Gaussian systems

An event-triggered risk-sensitive smoothing problem for linear Gaussian systems is investigated in this paper. Due to the special cost criterion of the risk-sensitive estimation, the smoothed information state is first constructed under a newly defined reference measure. Its Gaussian density and recursive forms are derived by processing it into the combination of the forward and backward information states, both of which are proven to have Gaussian densities and evolve in linear recursions. A stochastic even-triggering condition is adopted to preserve the Gaussian property during the derivation. Then the proposed problem is reformulated equivalently under the reference measure, expressed by minimizing an integral involving the smoothed information state, and finally solved by utilizing the Gaussian densities of information states. The applicability and effectiveness of the results are illustrated through a numerical example by comparisons with a naive risk-sensitive smoother and the event-triggered MMSE smoother.
Impact of Institutional Performance and Integrity on Public Trust during COVID-19: A Retrospective Examination in the Arabic Context

AbstractPurpose This study examines trust in public sector institutions during the coronavirus (COVID-19) pandemic and the impact factors.Methodology A survey was administered to a convenience sample of 1,372 local and expatriate residents in the United Arab Emirates during 2022–2023. Bivariate and multiple regression analyses were utilized.Findings There was a high level of general public trust among respondents (mean = 4.11), with the highest trust in the police (mean = 4.21) followed by trust in the information disseminated by the government (mean = 4.06) and in the performance of public sector institutions (mean = 3.89). On the bivariate and multivariate levels, demographic variables had an insignificant impact on public trust. General trust was significantly correlated with institutional performance (β = 0.417), institutional integrity (β = 0.397), perceived gains (β = 0.378), and government-disseminated information (β = 0.374).Implications Public trust is a complex reality linked to numerous organizational and nonorganizational factors. Improving public trust requires enhancing ethical conduct—crucial for maintaining trust—as well as good governance practices and leadership behavior.Limitations The sample size and quantitative focus might have limited the generalizability of the findings. In the future, researchers should use in-depth interviews or mixed methodologies.Keywords: Trustpublic sector COVID-19UAEmultiple linear regression AcknowledgmentsThanks are extended to the editorial team and the reviewers.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the United Arab Emirates University under the UAEU Program for Advanced Research (UPAR-2280-G00004171) and under United Arab Emirates University Summer Undergraduate Research Experiences (SurePlus-2115-G00003924).
Better Together: Ensemble Learning for Earthquake Detection and Phase Picking

The detection and picking of seismic waves is the first step toward earthquake catalog building, earthquake monitoring, and seismic hazard management. Recent advances in deep learning have leveraged the amount of labeled seismic data to improve the capability of detecting and picking earthquake signals. While these deep learning methods have shown great promise, their success remains hindered by low generalizability and poor performance in low signal-to-noise ratios (SNRs) data. Here, we propose a new processing workflow that integrates pretrained deep learning models, multi-frequency band predictions, and ensemble estimations to enhance the generalization of these algorithms. We test the performance of the ensemble model using three benchmark datasets, one of which is within-domain and has been used for training the deep learning models, the other two being cross-domain test datasets. We explore the performance given data and model characteristics. We also compare an ensemble approach with a transfer-learning approach and discuss the benefits and drawbacks of these two approaches when deploying on continuous data. Our experiments demonstrate that ensemble learning can drastically improve generalization ability and hence alleviate the need for transfer learning in the case where no labeled datasets exist.
Compound-Gaussian Spatial-Temporal Correlated Complex Clutter Simulation Based on a Two-Step Data-Driven Method

In this work, aiming to simulate random complex clutter with the similar spatial-temporal correlation and amplitude distribution to the measured clutter, a two-step data-driven method as an alternative to statistical methods is proposed. First, a complex data matrix with the desired spatial-temporal correlation is generated via the sliding-window filtering (SWF) from the measured clutter. Based on the compound-Gaussian model, it is explained that the SWF guarantees that the complex data matrix can roughly reproduce the amplitude distribution of the measured clutter. Second, to further improve the amplitude distribution reproducing performance, a scheme combining the amplitude-phase separation technique and the correlation transfer method is introduced to process the complex data matrix generated by the SWF. The experimental results show that the clutter generated by the two-step method can reproduce the main statistical characteristics observed in the measured clutter.
Invertible Energy-Aware Images

Displaying video content requires significant amounts of energy. In face of the energy crisis and climate emergency, we propose a new approach to produce energy-aware images. The purpose of such images is to consume less energy when displayed onscreen while maximizing the quality of experience. A new invertible neural network called Invertible Energy-Aware Network (InvEAN) is proposed to produce invertible energy-aware images, allowing to reduce the energy consumption of display devices and offering the possibility to recover the original image if required. Experimental results show that the InvEAN network outperforms two existing methods over three datasets.
Item Difficulty Prediction Using Item Text Features: Comparison of Predictive Performance across Machine-Learning Algorithms

This work presents a comparative analysis of various machine learning (ML) methods for predicting item difficulty in English reading comprehension tests using text features extracted from item wordings. A wide range of ML algorithms are employed within both the supervised regression and the classification tasks, including regularization methods, support vector machines, trees, random forests, back-propagation neural networks, and Naïve Bayes; moreover, the ML algorithms are compared to the performance of domain experts. Using f-fold cross-validation and considering the root mean square error (RMSE) as the performance metric, elastic net outperformed other approaches in a continuous item difficulty prediction. Within classifiers, random forests returned the highest extended predictive accuracy. We demonstrate that the ML algorithms implementing item text features can compete with predictions made by domain experts, and we suggest that they should be used to inform and improve these predictions, especially when item pre-testing is limited or unavailable. Future research is needed to study the performance of the ML algorithms using item text features on different item types and respondent populations.
Securing Fog Computing Through Consortium Blockchain Integration

The authors have developed an innovative topology by amalgamating consortium blockchain, often referred to as supervisory blockchain, with fog computing. The proposed system is organized into three distinct layers: the application layer, the fog layer, and the blockchain security layer. To accommodate this model effectively, the authors introduce the novel proof of enhanced concept (PoEC) consensus mechanism. This approach employs homomorphic encryption to secure transactions, which are then outsourced to the fog layer or fog devices. This strategy mitigates various security threats, including collusion attacks, phishing attacks, and replay attacks, bolstering the resilience of each layer against such incursions. To bolster security measures further, our model adopts a hybrid-deep learning protocol for safeguarding electronic medical records against potential breaches while concurrently reducing latency through a decentralized fog computing system.
A Fog-Based Privacy-Preserving Federated Learning System for Smart Healthcare Applications

During the COVID-19 pandemic, the urgency of effective testing strategies had never been more apparent. The fusion of Artificial Intelligence (AI) and Machine Learning (ML) models, particularly within medical imaging (e.g., chest X-rays), holds promise in smart healthcare systems. Deep Learning (DL), a subset of AI, has exhibited prowess in enhancing classification accuracy, a crucial aspect in expediting COVID-19 diagnosis. However, the journey to harness DL’s potential is rife with challenges: notably, the intricate landscape of medical data privacy. Striking a balance between utilizing patient data for insights while upholding privacy is formidable. Federated Learning (FL) emerges as a solution by enabling collaborative model training across decentralized data sources, thus bypassing data centralization and preserving data privacy. This study presents a tailored, collaborative FL architecture for COVID-19 screening via chest X-ray images. Designed to facilitate cooperation among medical institutions, the framework ensures patient data remain localized, eliminating the need for direct data sharing. Addressing imbalanced and non-identically distributed data, the architecture is a robust solution. Implementation entails localized and fog-computing-based FL models. Localized models utilize Convolutional Neural Networks (CNNs) on institution-specific datasets, while the FL model, refined iteratively, takes precedence in the final classification. Intriguingly, the global FL model, fortified by fog computing, emerges as the frontrunner in classification after weight refinement, surpassing local models. Validation within the COLAB platform gauges the model’s performance through metrics such as accuracy, precision, recall, and F1-score. Remarkably, the proposed model excels across these metrics, solidifying its efficacy. This research navigates the confluence of AI, FL, and medical imaging, unveiling insights that could reshape healthcare delivery. The study enriches scientific discourse by addressing data privacy in collaborative learning and carries potential implications for enhanced patient care.
Scaling-up medical vision-and-language representation learning with federated learning

Medical Vision-and-Language Pre-training (MedVLP), which learns generic vision-language representations from medical images and texts to benefit various downstream medical tasks, has drawn remarkable attention in both artificial intelligence and clinical medicine. However, existing works ignore the privacy issues and the heavy computation burden in MedVLP. In this study, we propose a FedMedVLP model, which adopts federated learning to unify the datasets from different clients, e.g., centers and hospitals, to form a large-scale pre-training dataset. As a result, the unified large-scale pre-training dataset can be used to pre-train the MedVLP to achieve strong performance. Overall, our FedMedVLP can improve the performance of MedVLP while preventing data leakage. Extensive experiments prove that the proposed model sets new state-of-the-art results on five benchmark datasets across three medical mainstream tasks, i.e., medical image–text retrieval, medical text-image retrieval, and medical visual question answering tasks. Besides, we further evaluate our method on our curated well-balanced medical dataset COVID-Fed.
Application of an Effective Hierarchical Deep-Learning-Based Object Detection Model Integrated with Image-Processing Techniques for Detecting Speed Limit Signs, Rockfalls, Potholes, and Car Crashes

Against the backdrop of rising road traffic accident rates, measures to prevent road traffic accidents have always been a pressing issue in Taiwan. Road traffic accidents are mostly caused by speeding and roadway obstacles, especially in the form of rockfalls, potholes, and car crashes (involving damaged cars and overturned cars). To address this, it was necessary to design a real-time detection system that could detect speed limit signs, rockfalls, potholes, and car crashes, which would alert drivers to make timely decisions in the event of an emergency, thereby preventing secondary car crashes. This system would also be useful for alerting the relevant authorities, enabling a rapid response to the situation. In this study, a hierarchical deep-learning-based object detection model is proposed based on You Only Look Once v7 (YOLOv7) and mask region-based convolutional neural network (Mask R-CNN) algorithms. In the first level, YOLOv7 identifies speed limit signs and rockfalls, potholes, and car crashes. In the second level, Mask R-CNN subdivides the speed limit signs into nine categories (30, 40, 50, 60, 70, 80, 90, 100, and 110 km/h). The images used in this study consisted of screen captures of dashcam footage as well as images obtained from the Tsinghua-Tencent 100K dataset, Google Street View, and Google Images searches. During model training, we employed Gaussian noise and image rotation to simulate poor weather conditions as well as obscured, slanted, or twisted objects. Canny edge detection was used to enhance the contours of the detected objects and accentuate their features. The combined use of these image-processing techniques effectively increased the quantity and variety of images in the training set. During model testing, we evaluated the model’s performance based on its mean average precision (mAP). The experimental results showed that the mAP of our proposed model was 8.6 percentage points higher than that of the YOLOv7 model—a significant improvement in the overall accuracy of the model. In addition, we tested the model using videos showing different scenarios that had not been used in the training process, finding the model to have a rapid response time and a lower overall mean error rate. To summarize, the proposed model is a good candidate for road safety detection.
Postdigital Ethics

Ethics is not a discrete set of rules or God-given imperatives concerning how humans ought to behave. Ethics resembles a holistic process of normative inquiry, implicating concepts such as duty, utility, virtue, and rationality. Postdigitalism concerns the ways in which human knowledge and experience adapt to the rapid growth and dissemination of digital technologies. Since technology and morality are in constant flux, postdigital ethics must remain flexible and accommodating to the tools and mores that define a rapidly changing civilization. In this vein, the outcomes of postdigital ethical inquiry are typically treated as fallible, unsettled, and subject to revision in light of shifting societal norms and dynamic moral beliefs. The proper subject-matter of postdigital ethics includes theory, applied ethics, posthumanism, and theology.