Reviewerly/exHarmony huggingface dataset:
An Experience Report on the use of Problem-based learning and Design Thinking in a Requirements Engineering Postgraduate Course

Context: Education in Requirements Engineering (RE) is fundamental to train professionals capable of dealing with the challenges of the software development. In this sense, methodologies such as Problem-Based Learning (PBL) and Design Thinking (DT) are valuable tools that can be used to prepare students to tackle challenges of the real world. Objective: This paper presents an experience report of a RE graduate course with 21 students that adopted innovative techniques like DT and PBL. Method: We used quantitative and qualitative analysis to determine the strengths and the points of improvement of the applied methodological approach, the benefits and challenges of using DT, the degree of easiness and usefulness of the used techniques from the point of view of the students and also evaluating both the students’ self-reporting and team performance. Results: The most difficult practices/techniques to implement were "partial deliveries of project activities," "crazy 8’s," and "textual specification of use cases." On the other hand, the easiest practices/techniques were "prototype" and "development of requirements document." Regarding usefulness, the analysis indicates that the most useful practices/techniques were "prototype," "use case diagram," "brainstorming," "project presentation," and "collaborative construction of the project." Conversely, the least useful practices/techniques were "validation of requirements using a checklist" and "personas." In general, the results indicate that the teams are constantly evolving, and periodic evaluation can be an important tool for identifying strengths and areas for improvement. Conclusions: The course offered a valuable opportunity for professional development, but there is room for improvement in certain aspects to ensure an even better learning experience for future students. We contribute to the literature by presenting a successful experience of using DT and PBL in a short RE course as well as we discuss some lessons learned and highlight the potential this approach has to improve RE education.
SENSE: An unsupervised semantic learning model for cross-platform vulnerability search

Binary Similarity Analysis (BSA) emerges as a vital approach for identifying homologous vulnerabilities. However, it is constrained by semantic incompleteness, structural differences, and false positives arising from variations in compilation environments. In this paper, we propose a novel Unsupervised Semantic Learning Model named SENSE for cross-platform vulnerability search. The model comprises two main components: semantic learner and graph learner. The semantic learner is pre-trained with a mask language task on a well-normalized binary corpus, enabling it to capture contextual semantic relations and generate block embedding that effectively encode the semantic features. In the graph learner, a gated graph neural network with a self-gating layer is adopted to eliminate redundant features and an adversarial loss is incorporated to enhance the robustness of function embedding across different compiler environments. Finally, SENSE is trained in an unsupervised manner using a batch-wise sampling strategy along with maximum mutual information loss. This encourages semantically similar functions to exhibit tighter embedding representations, thereby reducing false positives and improving search efficiency. Through extensive experiments, we have demonstrated that SENSE outperforms state-of-the-art methods in terms of binary search accuracy. Our results also reveal that SENSE is capable of generating robust function embedding that mitigate the differences arising from diverse architecture and optimization options.
Fast Gaussian Process Emulation of Mars Global Climate Model

Abstract The NASA Ames Mars Global Climate Model (MGCM) software has been in steady use at NASA for decades and was recently released to the public. This model simulates the complex interactions of various weather cycles that exist on Mars, namely the Dust Cycle, the CO 2 Cycle, and the Water cycle. Utilized by NASA, the MGCM is used to help understand their empirically observed data through the use of sensitivity studies. However, these sensitivity studies are computationally taxing, requiring weeks to run. To address this issue, we have trained a surrogate model using Gaussian processes (GP) that can emulate the output of this model with relatively small amounts of data in a reduced amount of time (on the order of minutes). We demonstrate the effectiveness of our emulator using backward error.
The control of particulate matter and hydrogeochemistry on uranium–thorium partition coefficients in two representative lakes, Qaidam Basin, China

Radionuclides (including those of uranium and thorium) are commonly used to quantify complex geochemical processes in aqueous environments. The estimation of mass transport fluxes for uranium (U) and thorium (Th) isotopes is plagued, however, by uncertainties predominantly driven by the inability to determine solute–particulate partition coefficients (Kd). Such uncertainties are especially pronounced for lakes in rarely studied, environmentally sensitive areas. This study examines the factors controlling the Kd values of U and Th isotopes in both freshwater (Keluke Lake) and saline water (Tuosu Lake) lakes within the Qaidam Basin, China, a basin sensitive to the impacts of climatic change. The analysis found a negative correlation of both Kd(Th) and Kd(U) with the concentration of suspended particulate matter (SPM), suggesting that particle concentrations affect Kd values in the sandy Keluke Lake. In contrast, there was no significant correlation between Kd values and SPM in the muddy (fine-grained) Tuosu Lake. Compositionally, three types of particles were found in both lakes and were derived from the same sediment sources. The Kd values were primarily controlled by lake evaporation and/or secondary water–rock interactions that were influenced by hydrogeochemical ecosystem differences between the lakes. In addition to differences in particle composition and concentrations, redox potential, ionic strength, organic matter concentration and pH of the lake water were likely to influence the Kd(Th) and Kd(U) values. These results are not only of significance to quantitative studies of U and Th isotopes in water (e.g., reducing transport flux uncertainties and improving the precision of U–Th isotope dating), but also provide an important reference for the application of radionuclides in other climate sensitive regions of the world.
Research on Chaotic Chimp Optimization Algorithm Based on Adaptive Tuning and Its Optimization for Engineering Application

The original Chimp Optimization Algorithm has disadvantages such as slow convergence, the tendency to fall into local optima, and low accuracy in finding the best. To alleviate the existing problems, a chaotic chimp optimization algorithm based on adaptive tuning is proposed. First, sine chaos mapping was used to initialize the chimpanzee population and enhance the quality and diversity of the initialized population. Then the global search capability and local exploitation capability of the optimization algorithm at iteration are enhanced by improving the convergence factor f and dynamically changing the number of chimpanzee precedence echelons. Finally, 10 benchmark functions are used to test the optimization-seeking performance of the Improved Chimp Optimization Algorithm, while an engineering design optimization problem is introduced to compare the experimental results with other swarm intelligence optimization algorithms. The Improved Chimp Optimization Algorithm is shown to have better convergence and solution accuracy, resulting in an improvement in the global optimization-seeking capability of the original Chimp Optimization Algorithm.
An Efficient Machine Learning-Based Cluster Analysis Mechanism for IoT Data

The prevailing developments in internet of things (IoT) and other sensor technologies such as cyber physical systems (CPS) and wireless sensor networks (WSNs), the huge amount of sensor data has been generating from various IoT devices and protocols. Making predictions and finding density patterns over such data is a challenging task. In order to find the density patterns and make analysis over real-time dynamic data, the machine learning (ML) based algorithms are widely used to deal with the IoT data. In this article, the authors proposed an efficient ML-based cluster analysis mechanism for finding density patterns in IoT dynamic data effectively. In this proposed mechanism, the k-means and GMM models are used for clustering data analysis. The proposed mechanism has been implemented on ThingSpeak Cloud platform for analysing the data efficiently on daily and weekly basis. Finally, the proposed mechanism acquired superior results than the existing benchmarked mechanisms over all the performance evaluation metrics used for analysis over IoT dynamic data.
On the Understanding of the Role of Continuous Experimentation in Technology-Based Startup

Technology startups are constantly emerging, trying to create innovative solutions in environments of uncertainty, and because they face numerous challenges, they have high failure rates. The scarcity of resources and the product’s lack of adherence to market needs are among the challenges. In an attempt to alleviate these challenges, initiatives such as Continuous Experimentation arise. This approach supports systematical tests of hypotheses, helping teams prioritize deliveries that increase perceived value by the users. Our interview-based study aimed to identify how Continuous Experimentation is being adopted and how it underlies software engineering activities throughout the product development cycle of technology-based startups. We found that data-driven decisions and reduced development effort are benefits of adopting such an approach while the low competence and education in experimentation is among the main challenges, suggesting that there is room for qualifying professionals in the matter.
Improving Multitask Retrieval by Promoting Task Specialization

Abstract In multitask retrieval, a single retriever is trained to retrieve relevant contexts for multiple tasks. Despite its practical appeal, naive multitask retrieval lags behind task-specific retrieval, in which a separate retriever is trained for each task. We show that it is possible to train a multitask retriever that outperforms task-specific retrievers by promoting task specialization. The main ingredients are: (1) a better choice of pretrained model—one that is explicitly optimized for multitasking—along with compatible prompting, and (2) a novel adaptive learning method that encourages each parameter to specialize in a particular task. The resulting multitask retriever is highly performant on the KILT benchmark. Upon analysis, we find that the model indeed learns parameters that are more task-specialized compared to naive multitasking without prompting or adaptive learning.1
Discrete Versus Continuous Algorithms in Dynamics of Affective Decision Making

The dynamics of affective decision making is considered for an intelligent network composed of agents with different types of memory: long-term and short-term memory. The consideration is based on probabilistic affective decision theory, which takes into account the rational utility of alternatives as well as the emotional alternative attractiveness. The objective of this paper is the comparison of two multistep operational algorithms of the intelligent network: one based on discrete dynamics and the other on continuous dynamics. By means of numerical analysis, it is shown that, depending on the network parameters, the characteristic probabilities for continuous and discrete operations can exhibit either close or drastically different behavior. Thus, depending on which algorithm is employed, either discrete or continuous, theoretical predictions can be rather different, which does not allow for a uniquely defined description of practical problems. This finding is important for understanding which of the algorithms is more appropriate for the correct analysis of decision-making tasks. A discussion is given, revealing that the discrete operation seems to be more realistic for describing intelligent networks as well as affective artificial intelligence.
Chapter 10. Automatic speech translation in the classroom and lecture setting

With dramatic improvements in quality for the technologies underlying Speech Translation, e.g., Speech Recognition and Machine Translation, the potential viability of Speech Translation in certain scenarios may finally be within reach. This is no truer than in educational settings where, with ever-growing immigration and an increasingly global workforce, multilingual classrooms and educational settings have become the norm around the world rather than the exception. The problem is that many educational institutions are faced with the daunting challenge of meeting the needs of upwards of 30–100 language communities simultaneously. Scenarios include providing translated content or instruction to linguistically diverse student populations, often in the same classroom, and parent-educator interactions, sometimes individually but also often in group settings. In the former scenario, speech translation technology can be a bridge between the student’s home language and the dominant language used in the classroom and an aid for learning the dominant language. In the latter scenario, parents, unlike their children, may never achieve proficiency in the dominant language(s), yet still need to be involved in their children’s education. The technology can provide them access where otherwise there may be none, or where the options may be severely limited. The large-scale multilingual requirements of these immigrant and diverse communities when interacting with educators generally defies reliable human-centric solutions and begs for technological ones.
On the Support for Designing a Conversational Software API: An Action Research Study

Application Programming Interfaces (APIs) are everyday tools for every software professional. When creating an API, a designer typically abstracts the tasks the API intends to perform. The API user, on the other hand, will use the API to perform specific-propose tasks. During the development of an API, the designer needs to write dialogues with which the user will interact with the API, performing a conversation between them. In this way, an API capable of offering effective dialogues to its users is called a conversational API. In this paper, we report the action research that we conducted to identify effective strategies for designing conversational APIs. For six months, we followed the redesign of an existing API developed at an R&D laboratory of a large IT company. The action research required three complete cycles, exploring different techniques for supporting the API redesign. Based on this experience, we present in this paper a set of lessons learned and actionable solutions to support the design of conversational APIs.
Signal Recognition and Location of Distributed Optical Fiber Vibration Sensing System Using Reservoir Computing Based on Mutually Injected Semiconductor Lasers

A distributed optical fiber vibration sensing (DOFVS) system for signal recognition and location is proposed and implemented by using a reservoir computing (RC) system based on mutually injected semiconductor lasers (SLs). The sensitivity of the RC to the environment is used to detect an external vibration, that is, the mutually injected channel of the SLs is the sensing fiber of the DOFVS system. The phase changes induced by the vibration are converted into the intensity fluctuations through the mutually injected effects of the SLs. Both the signal recognition and location are realized by the training and testing process of the RC classifier model, especially building multiple linear classifiers of the RC and adopting a winner-takes-all method. It is verified numerically and experimentally that the system can detect well the external vibration and meets the basic properties as a reservoir in the injection locking (IL) state. The recognition and location accuracies of two types of vibration signals and 20 sensing positions can reach 99.99% and 100% in an experimental system with a 1.12-km-long sensing fiber. The experimental results show that the proposed system can simultaneously realize the signal identification and location with the simple method, fast processing speed, and high accuracy.
Shor's Algorithm Using Efficient Approximate Quantum Fourier Transform

Shor's algorithm solves the integer factoring and discrete logarithm problems in polynomial time. Therefore, the evaluation of Shor's algorithm is essential for evaluating the security of currently used public-key cryptosystems because the integer factoring and discrete logarithm problems are crucial for the security of these cryptosystems. In this article, a new approximate quantum Fourier transform is proposed, and it is applied to Rines and Chuang's implementation. The proposed implementation requires one-third the number of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$T$</tex-math></inline-formula> gates of the original. Moreover, it requires one-fourth the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$T$</tex-math></inline-formula> -depth of the original. Finally, a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$T$</tex-math></inline-formula> -scheduling method for running the circuit with the smallest KQ is presented.
The Economic Development Model and Strategy Selection of the Internet of Things Based on Big Data

The internet of things (IoT) has become a key support object for Chinese strategic emerging industries. It is of great practical significance to promote the construction of the internet of things. Driven by national policies, the development of the internet of things in China has achieved certain results, but it also faces many problems. For example, there are few theoretical and empirical studies on the internet of things economy. In this context, from the perspective of big data, this paper studies the development model and influencing factors of the internet of things economy, and takes Jiangsu Province as an example to put forward development strategies. This article studies the three-stage development model of the IoT economic big data ecosystem: the primary stage, the growth and maturity stage, and the integration stage. On the basis of the research on the development model, the general evaluation method of the economic development model of the internet of things from the perspective of big data is studied, and the general model of strategy selection is established.
Human-Computer Interaction for Knowledge Discovery for Management

The rising volume of data in our networked world is a huge problem that necessitates practical and user-friendly solutions. Computational approaches may be useful, but we must recognize that problem-solving knowledge is stored in the human mind, not in robots. A strategic goal for finding answers to data-intensive problems might be to combine two domains that provide optimal preconditions: human-computer interaction (HCI) and knowledge discovery (KDD). HCI is concerned with human vision, cognition, intelligence, decision-making, and interactive visualization approaches; hence, it focuses mostly on supervised methods. KDD is primarily concerned with intelligent machines and data mining, namely the creation of scalable algorithms for discovering previously undiscovered associations in data, and hence focuses on automatic computational approaches. A proverb illustrates this perfectly: “Computers are incredibly fast, accurate, but stupid. Humans are incredibly slow, inaccurate, but brilliant.”
Overcoming MSME Challenges With Blockchain

Emerging technologies are distinct in that they stimulate the creation of automated and intelligent businesses at a quick pace. Business processes are being reimagined and reinvented by these companies. When blockchain was initially introduced to the world in 2008, it was touted as the next great digital revolution. This chapter seeks to offer an overview of blockchain technology by defining it and explaining how it works. However, because blockchain technology is still in its infancy, nothing is known about how this new technology may be used to practical applications. As a result, the chapter highlights its distinct characteristics that set it apart from other technologies. Despite government intervention and an environment that promotes entrepreneurship, MSMEs, which have long been the backbone of any economy, continue to face the same difficulties that small businesses face. This chapter demonstrates how blockchain may assist in resolving these challenges in the hopes of spurring greater research and development in the field of blockchain, particularly in the context of MSMEs.
Calibrating AI Models for Wireless Communications via Conformal Prediction

When used in complex engineered systems, such as communication networks, artificial intelligence (AI) models should be not only as accurate as possible, but also well calibrated. A well-calibrated AI model is one that can reliably quantify the uncertainty of its decisions, assigning high confidence levels to decisions that are likely to be correct, and low confidence levels to decisions that are likely to be erroneous. This paper investigates the application of conformal prediction as a general framework to obtain AI models that produce decisions with formal calibration guarantees. Conformal prediction transforms probabilistic predictors into set predictors that are guaranteed to contain the correct answer with a probability chosen by the designer. Such formal calibration guarantees hold irrespective of the true, unknown, distribution underlying the generation of the variables of interest, and can be defined in terms of ensemble or time-averaged probabilities. In this paper, conformal prediction is applied for the first time to the design of AI for communication systems in conjunction with both frequentist and Bayesian learning, focusing on the key tasks of demodulation, modulation classification, and channel prediction. For demodulation and modulation classification, we apply both validation-based and cross-validation-based conformal prediction; while we investigate the use of online conformal prediction for channel prediction. For each task, we evaluate the probability that the set predictor contains the true output, validating the theoretical coverage guarantees of conformal prediction, as well as the informativeness of the predictor via the average predicted set size.