Reviewerly/exHarmony huggingface dataset:
A New Perspective on Evaluation Methods for Explainable Artificial Intelligence (XAI)

One of the big challenges in the field of explainable artificial intelligence (XAI) is how to evaluate explainability approaches. Many evaluation methods (EMs) have been proposed, but a gold standard has yet to be established. Several authors classified EMs for explainability approaches into categories along aspects of the EMs themselves (e.g., heuristic-based, human-centered, application-grounded, functionally-grounded). In this vision paper, we propose that EMs can also be classified according to aspects of the XAI process they target. Building on models that spell out the main processes in XAI, we propose that there are explanatory information EMs, understanding EMs, and desiderata EMs. This novel perspective is intended to augment the perspective of other authors by focusing less on the EMs themselves but on what explainability approaches intend to achieve (i.e., provide good explanatory information, facilitate understanding, satisfy societal desiderata). We hope that the combination of the two perspectives will allow us to more comprehensively evaluate the advantages and disadvantages of explainability approaches, helping us to make a more informed decision about which approaches to use or how to improve them.
Enhancing Startup Success Rates: Towards a Pragmatic Framework for Product Managers (PFPM)

This study addresses the underrepresentation of the Product Manager (PM) role in determining the success of startups, particularly in the Requirements Engineering (RE) domain. Despite the high failure rate (63%) among software industry startups, the PM role is often completely unknown to founders, and academic research on this issue is lacking. Through a preliminary literature study, 662 unique tasks, condensed to 122 activities, were identified, associated with the PM role, with cash flow management was only mentioned once in the literature, despite being a critical reason (82%) for startup failures. Only a small percentage (11%) of the papers focused specifically on the startup context. To address these issues, our aim is to develop a novel, startup-specific framework that reduces the number of PM tasks to those that are valuable in its context and suggests improvements for each task accordingly. This framework could enhance early-stage product decision-making for founders, including cash flow management related tasks, and increase their probability of success. Not only contribution have practical implications for startups, but it could also stimulate further collaborative academic research in the field of RE.
Designing End-User Personas for Explainability Requirements Using Mixed Methods Research

With software systems becoming increasingly complex and opaque, explainability has become an emerging software quality aspect of our time. Raising explainability requirements for a large crowd of stakeholders can be a difficult task. At this point, it is unclear what factors have an impact on the subjective need for explanations. As researchers, we do not know whether end-users' explainability needs are specific for each system or if there are user types that have more generalized needs. Designing stakeholder personas has proven to be an effective method to elicit and communicate requirements. Indeed, personas have been established in both research and in applied scenarios such as software projects. Past work has created end-user personas for explainability requirements in the context of a specific system. The goal of this work is to design personas that do not only apply to explainability requirements of a certain software, but to explainability requirements in general. To this end, we conduct a mixed-methods user study consisting of an online survey and an interview study. Based on insights of 70 participants from the online survey, 10 of which were also interviewed, we design four end-user personas for general explainability requirements. We also validate the personas by doing use case walkthroughs with our participants. Our results indicate that it is possible to design end-user personas for general explainability requirements. These personas might be used in early software development stages to estimate end-users explainability needs before more specific requirements can be raised.
Dynamic behavior assessment protocol for secure Decentralized Federated Learning

Decentralized Federated Learning (DFL) is a prevalent approach to efficiently train deep learning models and preserve privacy by sharing model gradients instead of the local data. However, participants in the DFL may opt to adopt a dynamic behavior for personal gains. The existing DFL models cannot differentiate between the adaptive behavior of the participants in the massively distributed environments and assume that all the participants are honest. As a result, free riders or malicious participants remain undetected and not penalized. In this paper, we present a DFL architecture where decentralized participants assess the behavior of each other using the quality of gradients. A novel dynamic reputation assessment protocol is implemented to detect and eliminate participants with adaptive behavior. The proposed architecture is evaluated using behavior-based attacks in a decentralized environment by increasing the percentage of adaptive participants from 10% to 40%. The results show that our proposed protocol can effectively detect and eliminate participants with adaptive behaviour from the DFL in only two rounds whereas centralized federated learning fails to detect behavior-based attacks.
High-dimensional quantum key distribution using energy-time entanglement over 242 km partially deployed fiber

Abstract Entanglement-based quantum key distribution (QKD) is an essential ingredient in quantum communication, owing to the property of source-independent security and the potential on constructing large-scale quantum communication networks. However, implementation of entanglement-based QKD over long-distance optical fiber links is still challenging, especially over deployed fibers. In this work, we report an experimental QKD using energy-time entangled photon pairs that transmit over optical fibers of 242 km (including a section of 19 km deployed fibers). The QKD is realized through the protocol of dispersive-optics QKD (DO-QKD) with high-dimensional encoding to utilize coincidence counts more efficiently. A reliable, high-accuracy time synchronization technology for long-distance entanglement-based QKD is developed based on the distribution of optical pulses in quantum channels. Our system operates continuously for more than 7 d without active polarization or phase calibration. We ultimately generate secure keys with secure key rates of 0.22 bps and 0.06 bps in the asymptotic and finite-size regimes, respectively. It shows that entanglement-based DO-QKD is reliable for long-distance realization in the field if its high requirement on time synchronization is satisfied.
Who invests in cryptocurrency? The role of overconfidence among American investors

Cryptocurrency has received increasing attention due to its uniqueness among financial assets, and the characteristics of crypto investors have recently gained much interest. The present study aims to examine the linkage between financial confidence and cryptocurrency ownership. Using the representative sample of American investors, we find that financially overconfident investors are nearly 8% more likely to be crypto owners and 10% more likely to be probable crypto owners as compared to underconfident investors. Our findings are robust to sample selection bias and help in understanding the characteristics of crypto owners in terms of financial confidence. Based on these findings, regulators and policymakers may raise awareness about the overconfidence bias among existing and potential crypto investors.
An Efficient Unified Polynomial Arithmetic Unit for CRYSTALS-Dilithium

The CRYSTALS-Dilithium protocol is considered as one of the most promising digital signature schemes in NIST’s post-quantum cryptography standardization process. While separating arithmetic computation units can be advantageous in some cases, it can lead to increased hardware resource consumption and performance degradation. To overcome this issue, this paper proposes a novel architecture called the Unified Polynomial Arithmetic Unit (UniPAU), specifically designed for the Dilithium signature scheme. The proposed UniPAU offers a unique hardware module that can execute all the polynomial operations required for the Dilithium signature scheme. To demonstrate the effectiveness of our design, we implemented it on the Xilinx Zynq UltraScale+ ZCU102 (xczu9eg-ffvb1156-2-e) FPGA platform and evaluated its hardware efficiency and performance. Our implementation results indicate that the proposed UniPAU can achieve comparable throughput while consuming fewer hardware resources compared to state-of-the-art studies. These findings suggest that our UniPAU can provide an optimized and efficient hardware solution for polynomial arithmetic operations in the Dilithium signature scheme.
Analyzing influence of COVID-19 on crypto &amp; financial markets and sentiment analysis using deep ensemble model

COVID-19 affected the world's economy severely and increased the inflation rate in both developed and developing countries. COVID-19 also affected the financial markets and crypto markets significantly, however, some crypto markets flourished and touched their peak during the pandemic era. This study performs an analysis of the impact of COVID-19 on public opinion and sentiments regarding the financial markets and crypto markets. It conducts sentiment analysis on tweets related to financial markets and crypto markets posted during COVID-19 peak days. Using sentiment analysis, it investigates the people's sentiments regarding investment in these markets during COVID-19. In addition, damage analysis in terms of market value is also carried out along with the worse time for financial and crypto markets. For analysis, the data is extracted from Twitter using the SNSscraper library. This study proposes a hybrid model called CNN-LSTM (convolutional neural network-long short-term memory model) for sentiment classification. CNN-LSTM outperforms with 0.89, and 0.92 F1 Scores for crypto and financial markets, respectively. Moreover, topic extraction from the tweets is also performed along with the sentiments related to each topic.
A Higher radix architecture for quantum carry-lookahead adder

Abstract In this paper, we propose an efficient quantum carry-lookahead adder based on the higher radix structure. For the addition of two n -bit numbers, our adder uses $$O(n)-O(\frac{n}{r})$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>n</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> <mml:mo>-</mml:mo> <mml:mi>O</mml:mi> <mml:mrow> <mml:mo>(</mml:mo> <mml:mfrac> <mml:mi>n</mml:mi> <mml:mi>r</mml:mi> </mml:mfrac> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> qubits and $$O(n)+O(\frac{n}{r})$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>n</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> <mml:mo>+</mml:mo> <mml:mi>O</mml:mi> <mml:mrow> <mml:mo>(</mml:mo> <mml:mfrac> <mml:mi>n</mml:mi> <mml:mi>r</mml:mi> </mml:mfrac> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> T gates to get the correct answer in T-depth $$O(r)+O(\log {\frac{n}{r}})$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>r</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> <mml:mo>+</mml:mo> <mml:mi>O</mml:mi> <mml:mrow> <mml:mo>(</mml:mo> <mml:mo>log</mml:mo> <mml:mfrac> <mml:mi>n</mml:mi> <mml:mi>r</mml:mi> </mml:mfrac> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> , where r is the radix. Quantum carry-lookahead adder has already attracted some attention because of its low T-depth. Our work further reduces the overall cost by introducing a higher radix layer. By analyzing the performance in T-depth, T-count, and qubit count, it is shown that the proposed adder is superior to existing quantum carry-lookahead adders. Even compared to the Draper out-of-place adder which is very compact and efficient, our adder is still better in terms of T-count.
Research on a Hotel Collaborative Filtering Recommendation Algorithm Based on the Probabilistic Language Term Set

In the face of problems such as information overload and the information cocoon resulting from big data, it is a key point of current research to solve the problem of semantic fuzziness of online reviews and improve the accuracy of personalized recommendation algorithms by using online reviews. Based on the advantage of the probabilistic language term set to deal with fuzzy information and the historical data of online hotel reviews, this paper proposes a collaborative filtering recommendation algorithm for hotels. Firstly, the text data of hotel online reviews are crawled by a crawler and processed by jieba and TF-IDF tools. Secondly, the hotel evaluation attribute set is constructed, and the sentiment analysis of the review statements is carried out with the help of the HowNet sentiment dictionary and manual annotation method. The probabilistic language term set is used to classify the data and derive statistics, and the maximum deviation method is used to determine the weight of each attribute. Then, the cosine similarity formula is fused with the modified cosine similarity formula to calculate the similarity and construct the decision matrix. Finally, combined with the historical data of the user’s hotel selection, the hotel recommendation results are generated. This paper collected review data from 10 hotels in Macau from the official “Ctrip” website. The proposed recommendation algorithm model was then applied to process and analyze the data, resulting in the generation of a ranked list of hotel recommendations. To validate the accuracy and effectiveness of this research, the recommendation results were compared with those produced by other algorithms.
PVNet: A Used Vehicle Pedestrian Detection Tracking and Counting Method

Advances in technology have made people’s lives more prosperous. However, the increase in the number of cars and the emergence of autonomous driving technology have led to frequent road accidents. Manual observation of traffic conditions requires high labor intensity, low work efficiency, and poses safety risks. The paper proposes a deep learning-based pedestrian-vehicle detection model to replace manual observation, overcoming human resource limitations and safety concerns. The model optimizes the darknet53 backbone feature extraction network, reducing parameters and improving feature extraction capabilities, making it more suitable for pedestrian-vehicle scenarios. In addition, the PVFPN multi-scale feature fusion method is used to facilitate information exchange between different feature layers. Finally, the Bytetrack method is used for target counting and tracking. The paper model shows excellent performance in pedestrian-vehicle detection and tracking in traffic scenarios. The experimental results show that the improved model achieves a mAP@.5 of 0.952 with only 32% of the parameters compared to YOLOv8s. Furthermore, the proposed PVNet model, combined with the Bytetrack method, maintains high detection accuracy and is applicable to pedestrian-vehicle detection and tracking in traffic scenarios. In summary, this section discusses the traffic issues arising from technological development and presents the optimization and performance of the deep learning-based pedestrian-vehicle detection model, along with its potential applications in traffic scenarios.
Real-Time Sensor-Embedded Neural Network for Human Activity Recognition

This article introduces a novel approach to human activity recognition (HAR) by presenting a sensor that utilizes a real-time embedded neural network. The sensor incorporates a low-cost microcontroller and an inertial measurement unit (IMU), which is affixed to the subject's chest to capture their movements. Through the implementation of a convolutional neural network (CNN) on the microcontroller, the sensor is capable of detecting and predicting the wearer's activities in real-time, eliminating the need for external processing devices. The article provides a comprehensive description of the sensor and the methodology employed to achieve real-time prediction of subject behaviors. Experimental results demonstrate the accuracy and high inference performance of the proposed solution for real-time embedded activity recognition.
A New Semantic Segmentation Framework Based on UNet

This paper discusses a semantic segmentation framework and shows its application in agricultural intelligence, such as providing environmental awareness for agricultural robots to work autonomously and efficiently. We propose an ensemble framework based on the bagging strategy and the UNet network, using RGB and HSV color spaces. We evaluated the framework on our self-built dataset (Maize) and a public dataset (Sugar Beets). Then, we compared it with UNet-based methods (single RGB and single HSV), DeepLab V3+, and SegNet. Experimental results show that our ensemble framework can synthesize the advantages of each color space and obtain the best IoUs (0.8276 and 0.6972) on the datasets (Maize and Sugar Beets), respectively. In addition, including our framework, the UNet-based methods have faster speed and a smaller parameter space than DeepLab V3+ and SegNet, which are more suitable for deployment in resource-constrained environments such as mobile robots.
Phenomenological theory of variational quantum ground-state preparation

The variational approach is a cornerstone of computational physics, considering both conventional and quantum computing computational platforms. The variational quantum eigensolver algorithm aims to prepare the ground state of a Hamiltonian exploiting parametrized quantum circuits that may offer an advantage compared to classical trial states used, for instance, in quantum Monte Carlo or tensor network calculations. While, traditionally, the main focus has been on developing better trial circuits, we show that the algorithm's success, if optimized within stochastic gradient descent (SGD) or quantum natural gradient descent (QNGD), crucially depends on other parameters such as the learning rate, the number ${N}_{s}$ of measurements to estimate the gradient components, and the Hamiltonian gap $\mathrm{\ensuremath{\Delta}}$. Within the standard SGD or QNGD, we first observe the existence of a finite ${N}_{s}$ value below which the optimization is impossible, and the energy variance resembles the behavior of the specific heat in second-order phase transitions. Second, when ${N}_{s}$ is above such threshold level, and learning is possible, we develop a phenomenological model that relates the fidelity of the state preparation with the optimization hyperparameters and $\mathrm{\ensuremath{\Delta}}$. More specifically, we observe that the computational resources scale as $1/{\mathrm{\ensuremath{\Delta}}}^{2}$, and we propose a symmetry enhancement of the variational ansatz as a way to increase the closing gap. We test our understanding on several instances of two-dimensional frustrated quantum magnets, which are believed to be the most promising candidates for near-term quantum advantage through variational quantum simulations.
A Data-Driven Approach for Finding Requirements Relevant Feedback from TikTok and YouTube

The increasing importance of videos as a medium for engagement, communication, and content creation makes them critical for organizations to consider for user feedback. However, sifting through vast amounts of video content on social media platforms to extract requirements-relevant feedback is challenging. This study delves into the use of TikTok and YouTube, two widely used social media platforms that focus on video content, in identifying relevant user feedback that may be further refined into requirements using subsequent requirement generation steps. We demonstrate an approach of using videos as a source of user feedback by analyzing audio and visual text, and metadata (i.e., description/title) from 6276 videos of 20 popular products across various industries. We employed state-of-the-art deep learning transformer-based models, and classified 3097 videos consisting of requirements relevant information. We then clustered relevant videos and found multiple requirements relevant feedback themes for each of the 20 products. This feedback can later be refined into requirements artifacts. We found that product ratings (feature, design, performance), bug reports, and usage tutorial are persistent themes from the videos. Video-based social media such as TikTok and YouTube can provide valuable user insights, making them a powerful and novel resource for companies to improve customer-centric development.
Revisiting the Performance-Explainability Trade-Off in Explainable Artificial Intelligence (XAI)

Within the field of Requirements Engineering (RE), the increasing significance of Explainable Artificial Intelligence (XAI) in aligning AI-supported systems with user needs, societal expectations, and regulatory standards has garnered recognition. In general, explainability has emerged as an important non-functional requirement that impacts system quality. However, the supposed trade-off between explainability and performance challenges the presumed positive influence of explainability. If meeting the requirement of explainability entails a reduction in system performance, then careful consideration must be given to which of these quality aspects takes precedence and how to compromise between them. In this paper, we critically examine the alleged trade-off. We argue that it is best approached in a nuanced way that incorporates resource availability, domain characteristics, and considerations of risk. By providing a foundation for future research and best practices, this work aims to advance the field of RE for AI.
Forecasting Univariate Solar Irradiance using Machine learning models: A case study of two Andean Cities

The integration of solar energy into power systems is essential for the future sustainability of power systems, particularly for isolated systems, such as microgrids, where establishing a primary transmission network is difficult. Therefore, the development of prediction methods becomes crucial to enable accurate forecasting of solar energy generation, facilitating efficient planning and operation of these systems and ensuring their long-term viability. This study proposes distinct forecasting models for solar irradiance forecasting: an autoregressive (AR) model, a Random Forest model, and a Long Short-Term Memory (LSTM) neural network. The methodology involves preprocessing the historical solar irradiance data and performing feature engineering to extract relevant input features. The architectural design, hyperparameter tuning, and training procedures of each model are discussed in detail. The findings indicate that the LSTM model exhibits enhanced performance compared to the AR model, while maintaining similar predictive accuracy to the Random Forest model in forecasting global solar irradiance. Both models yield a mean absolute percentage error of roughly 25%, with the LSTM exhibiting the lower error rate. Moreover, the LSTM model showcases an advancement over the AR model, resulting in a reduction of approximately 10 W/m2 for both root mean square error and mean absolute error. This finding highlights the effectiveness of LSTM networks in capturing long-term dependencies for accurate solar irradiance forecasting. Furthermore, an analysis of the models' interpretability is conducted, offering valuable insights into the key factors that contribute to the shaping of solar irradiance patterns. These insights hold practical significance for the optimization of renewable energy systems.