Reviewerly/exHarmony huggingface dataset:
Identifying Agile Practices to Reduce Defects in Medical Device Software Development

Medical Device Software (MDS) defects have caused death of patients and continue to be the major cause of recalls of medical devices in the US and Europe. Despite various approaches proposed to address defects, dealing with defects in MDS is an increasingly difficult task as MDS has become more complex to support a growing number of functions. To increase quality in any software development project, it is essential that defects are identified and addressed quickly in the early stages of the software development life cycle. Agile methods have been advocated to increase software quality by minimising defects through their agile practices. However, agile methods on their own are deficient in satisfying the regulatory requirements for the MDS domain. Instead, the common approach is to integrate agile practices into the plan driven methods. Consequently, frameworks have been developed to help developers in the MDS domain to accrue the benefits of agile development while fulfilling regulatory requirements. Despite the adoption of agile practices in MDS development, it is still unclear as to which agile practice(s) is effective and how it is applied to address MDS defects. The purpose of this research is to identify agile practices that can assist in addressing defects in MDS development. This will help MDS developers to select the appropriate agile practice(s) to address defects.
A Two-Stage Framework for Kidney Segmentation in Ultrasound Images

In this paper, we propose an algorithm framework for kidney segmentation in ultrasound images. Due to the characteristics of kidney ultrasound images, such as high noise, heterogeneous structure, low contrast, multiple artifacts, and relatively fixed shape, accurately segmenting clear and complete kidney structures from the images is still a challenging task. Our framework consists of two parts: shape aware dual-task multi-scale fusion network and self-correction. The first part uses a U-shape structure with multi-scale feature cross fusion skip connections to perform segmentation and can simultaneously predict the segmentation map and the shape-aware prediction level set function, we use a dual-task consistency module to constrain the shape of the segmentation map, enabling the network to learn the target area more accurately, through dual-task consistency supervised learning, we obtain a pre-trained model. The second part uses an iterative aggregation strategy for the pre-trained model to optimize it and reduce the noise and other issues in the prediction results. Experimental results show that our algorithm framework outperforms several state-of-the-art methods on kidney ultrasound datasets.
Requirements Engineering for Cyber-Physical Products

Today's cyber-physical products are software-intense. That means that the software process is decisive for the ability of these products to learn and adapt behavior, but in turn also to physically harm humans or the environment. Because such systems learn, change their behavior, unlearn, and adapt to their environment makes not only testing a challenge, but also requirements engineering. A new model for knowledge representation opens a possibility to make intelligent systems predictable at least for certain specific aspects. This in turn opens new challenges for identifying the areas where such predictability should precede over the adaptability that is protruding for intelligent systems. Adapting the software process leads to Continuous Requirements Engineering as an extension to Continuous Integration/Continuous Deployment & Delivery and Autonomous Real-time Testing.
The Recognition Accuracy in the SSD Model

The Single Shot Multibox Detector (SSD) technique is currently among the fastest and most accurate detection algorithms available. However, the majority of research on the accuracy of this approach has focused on noiseless objects. Thus, this study evaluates the algorithm's accuracy with both noisy and noiseless objects. To that goal, the algorithm is trained to recognize ten different flower species. Experiments are then carried out on photographs in four different scenarios: the item is totally lighted, 1/3 of the object is darkened, 1/2 of the object is darkened, and the object is fully darkened. The performance of the algorithm is then evaluated using SPSS 20.0 software and the analysis of variance (ANOVA) and least significant difference (LSD). The experimental results reveal that the algorithm accuracy is strongly dependent on the noise level. The detection accuracy is 100%, 81.3%, 44.7%, and 62%, respectively, when the item is fully lighted, 1/3, 1/2 size of the object is darkened, and the object is fully darkened.
Leverage OneAPI to identify fake reviews for e-marketplace using NLP techniques

Nowadays customers rely on a particular product based on the reviews available on the online e-commerce site. These reviews affect the customer&apos;s purchase decision as well as the manufacturer in both positive and negative ways. Positive reviews promote a manufacturer&apos;s product and negative reviews helps the manufacturer to improve their quality. However, certain companies use spammers to express negative reviews against their competitor&apos;s product. Spammer write fake reviews without using the items which affects the product reputation. Fake reviews are designed to deceive people by providing false information. These fake reviews by a competitor can destroy a products creditability. It affects the business profitability as well as the consumer&apos;s purchasing decision. This present paper attempts to develop an intelligent system that can detect fake reviews on e-marketplace using NLP techniques leveraging the OneAPI tools on Intel CPU to improve the efficiency of fake reviews detection on an e-commerce system. This paper aims in designing a unified framework to implement fake reviews identification using Machine Learning (ML) and Deep Learning (DL) accelerations on Intel Hardware.
Partially Defined Boolean Functions in TRIZ

The most recognized area where Boolean functions are used in TRIZ is, presumably, the cause-effect analysis, with logical operators indicating how causes trigger the effects. An AND operator denotes logical conjunction, while an OR operator denotes logical disjunction, and both are fully defined functions, i.e., an explicit output value is specified for each combination of the arguments. This paper focuses on partially defined Boolean functions incurring uncertainty due to some output values indicated as unspecified or unknown. It discusses the opportunities and threats of using such functions to describe project requirements and relations between the causes in cause-effect models. The approaches to tackling the uncertainty resulting from such function definitions are also presented, which is deemed to be instrumental for TRIZ researchers and practitioners.
Shared Task 1 on NCAA 2023: Chinese Diabetes Question Classification

This shared task focuses on classifying Chinese diabetes questions. Medical question classification is one of important tasks in medical data processing. However, due to the unique characteristics of medical questions, such as the presence of many uncommon medical terms and ambiguous short texts, resulting in difficulty in obtaining the most ideal information for users. The goal of this shared task is to enhance the performance of classification results and promote the development of diabetes automatic question answering services. This paper presents the results of the Chinese Diabetes Question Classification Shared Task and summarizes the classification methods used by the winning teams.
Automatic Layer Freezing for Communication Efficiency in Cross-Device Federated Learning

Federated Learning (FL) is a collaborative machine learning paradigm where network-edge clients train a global model under the orchestration of a central server. Unlike traditional distributed learning, each participating client keeps its data locally, ensuring privacy protection by default. However, state-of-the-art FL implementations suffer from massive information exchange between clients and the server. This issue prevents the adoption in constrained environments, typical of the Internet-of-Things domain, where the communication bandwidth and the energy budget are severely limited. To achieve higher efficiency at scale, the future of FL calls for additional optimizations to reach high-quality learning capability with lower communication pressure. To address this challenge, we propose Automatic Layer Freezing (ALF), an embedded mechanism that gradually drops a growing portion of the model out of the training and synchronization phases of the learning loop, reducing the volume of exchanged data with the central server. ALF monitors the evolution of model updates and identifies layers that have reached a stable representation, where further weight updates would have minimal impact on accuracy. By freezing these layers, ALF achieves substantial savings in communication bandwidth and energy consumption. The proposed implementation of the ALF mechanism is compatible with any FL strategy, requiring minimal effort and without interfering with existing optimizations. The extensive experiments conducted using a representative set of FL strategies applied to two image classification tasks show that ALF improves the communication efficiency of the baseline FL implementations, ensuring up to 83.91% of data volume savings with no or marginal losses of accuracy.
Identifying Key Factors to Distinguish Artificial and Human Avatars in the Metaverse: Insights from Software Practitioners

The Metaverse comprises a network of interconnected 3D virtual worlds, poised to become the primary gateway for future online experiences. These experiences hinge upon the use of avatars, participants' virtual counterparts capable of exhibiting human-like non-verbal behaviors, such as gestures, walking, dancing, and social interaction. Discerning between human and artificial avatars becomes crucial as the concept gains prominence. Advances in artificial intelligence have facilitated the creation of virtual human-like entities, underscoring the importance of distinguishing between virtual agents and human characters. This paper investigates the factors differentiating human and virtual participants within the Metaverse environment. A semi-structured interview approach was employed, with data collected from software practitioners (N = 10). Our preliminary findings indicate that response speed, adaptability to unforeseen events, and recurring scenarios play significant roles in determining whether an entity in the virtual world is a human or an intelligent agent.
Towards Improving the Reliability and Transparency of ChatGPT for Educational Question Answering

Large language models (LLMs), such as ChatGPT, have shown remarkable performance on various natural language processing (NLP) tasks, including educational question answering (EQA). However, LLMs generate text entirely based on knowledge obtained during pre-training, which means they struggle with recent information or domain-specific knowledge bases. Moreover, only providing answers to questions posed to LLMs without any grounding materials makes it difficult for students to judge their validity. We therefore propose a method for integrating information retrieval systems with LLMs when developing EQA systems, which in addition to improving EQA performance grounds the answers in the educational context. Our experiments show that the proposed system outperforms vanilla ChatGPT with a significant margin of 110.9%, 67.8%, 43.3%, and 9.2% on BLEU, ROUGE, METEOR and BERTScore. In addition, we argue that the use of the retrieved educational context enhances the transparency and reliability of the EQA process, making it easier to determine the correctness of the answers.
Implementing a Desktop VR Tool in a European University: Priorities and Challenges

Abstract Virtual reality technologies in educational settings have demonstrated their potential to improve understanding, engagement, motivation and learning outcomes. However, there are multiple technical, pedagogical, and institutional challenges on the way of technology adoption in the education sector. In this group-concept-mapping study within the CloudClass project we aim at identifying the requirements for implementing a desktop VR tool (CloudClass) for education in the university context. Teachers, multimedia experts and managers from a Spanish and a Dutch university (a face-to-face and a distance learning one) were asked to complete the focus prompt “ To use/implement CloudClass in education it is required/ needed that.… ”. The generated statements were classified thematically and rated for importance and feasibility. 95 unique statements were generated and sorted statistically into 5 clusters: Evaluation, Institutional Requirements, Maintenance and Training, Student Requirements, Affordances and infrastructure. A strong correlation was identified between the importance and feasibility of the identified clusters. To ensure a sustainable implementation of a desktop VR tool like CloudClass in a university setting a holistic approach considering all identified clusters is needed. Clusters Maintenance and Training and Institutional requirements are the low-hanging fruits to invest in, as both clusters scored highest on importance and feasibility.
Broader and Deeper: A Multi-Features with Latent Relations BERT Knowledge Tracing Model

Knowledge tracing aims to estimate students’ knowledge state or skill mastering level over time, which is evolving into an essential task in educational technology. Traditional knowledge tracing algorithms generally use one or a few features to predict students’ behaviour and do not consider the latent relations between these features, which could be limiting and disregarding important information in the features. In this paper, we propose MLFBK: A Multi-Features with Latent Relations BERT Knowledge Tracing model, which is a novel BERT based Knowledge Tracing approach that utilises multiple features and mines latent relations between features to improve the performance of the Knowledge Tracing model. Specifically, our algorithm leverages four data features (student_id, skill_id, item_id, and response_id, as well as three meaningful latent relations among features to improve the performance: individual skill mastery, ability profile of students (learning transfer across skills), and problem difficulty. By incorporating these explicit features, latent relations, and the strength of the BERT model, we achieve higher accuracy and efficiency in knowledge tracing tasks. We use t-SNE as a visualisation tool to analyse different embedding strategies. Moreover, we conduct ablation studies and activation function evaluation to evaluate our model. Experimental results demonstrate that our algorithm outperforms baseline methods and demonstrates good interpretability.
What Makes Problem-Solving Practice Effective? Comparing Paper and AI Tutoring

Abstract In numerous studies, intelligent tutoring systems (ITSs) have proven effective in helping students learn mathematics. Prior work posits that their effectiveness derives from efficiently providing eventually-correct practice opportunities. Yet, there is little empirical evidence on how learning processes with ITSs compare to other forms of instruction. The current study compares problem-solving with an ITS versus solving the same problems on paper. We analyze the learning process and pre-post gain data from N = 97 middle school students practicing linear graphs in three curricular units. We find that (i) working with the ITS, students had more than twice the number of eventually-correct practice opportunities than when working on paper and (ii) omission errors on paper were associated with lower learning gains. Yet, contrary to our hypothesis, tutor practice did not yield greater learning gains, with tutor and paper comparing differently across curricular units. These findings align with tutoring allowing students to grapple with challenging steps through tutor assistance but not with eventually-correct opportunities driving learning gains. Gaming-the-system, lack of transfer to an unfamiliar test format, potentially ineffective tutor design, and learning affordances of paper can help explain this gap. This study provides first-of-its-kind quantitative evidence that ITSs yield more learning opportunities than equivalent paper-and-pencil practice and reveals that the relation between opportunities and learning gains emerges only when the instruction is effective.
A Transformer-Based Approach for the Automatic Generation of Concept-Wise Exercises to Provide Personalized Learning Support to Students

Providing personalized support to students during courses is essential to facilitate them in their desired learning goals and reduce the dropout rate. Although teachers can play an effective role in providing personalized support, achieving individual-level assistance for massive courses becomes challenging. To overcome this challenge, this paper proposes a transformer-based approach that first models students’ knowledge of various course concepts based on their performance in various assessed tasks. Afterwards, the students’ concept-wise knowledge level derived from the models is combined with the available course material, leading to the generation of personalized concept-wise exercises by employing fine-tuned Text-to-Text Transfer Transformer (T5) architecture. These generated exercises help students to improve their knowledge about different course concepts. The proposed approach has been evaluated with various university courses to determine its quality, utility and effects on students’ academic performance. The evaluation results revealed that teachers and students were satisfied with the quality of the generated exercises, and these were found to be helpful for students to improve their concept-wise understanding. Furthermore, the generated exercises positively impacted students’ academic performance.
Study on Blockchain-Integrated IoT

The continuous development of interconnected devices is reconstructing livelihood and with the emergence of advanced technology yields growth in the usage of IoT devices. Though it is revolutionary and brings convenience, it faces different security challenges. The transition from traditional to smart in varieties of sectors makes the use of IoT technologies to become an integral part of lives which obviously requires a protection shield that can be offered by Blockchain. Blockchain's protected decentralization overcomes different hazards the IoT structure is facing. In this chapter, the authors have represented a detailed study on IoT, its layer and applications, a comparative analysis of different security threats, how an association with blockchain would mitigate different shortcomings of present IoT architecture, and the issues that exist in Blockchain-associated IoT.
Intent Recognition

In practical scenarios of the human-machine dialogue system, intent detection is an important and challenging problem. The human-machine dialogue system firstly converts the user’s query into a structured semantic representation that the machine can understand and maps it to the system’s pre-defined user intents so that the system can make a corresponding reply. The process of mapping user semantics to pre-defined user intents is called “known intent classification.” Accurate and efficient intent classification can guarantee that the dialogue system provides accurate services to users. With the vigorous development of human-machine dialogue systems, various new needs of users are also constantly increasing. How to discover new types of user intentions that have not been previously observed in the training set and have not yet been satisfied has become a vital question. The above process of detecting new user intents that do not appear in the pre-defined intent set is called “unknown intent detection.” After successfully separating unknown intents from known intents, more attention is given to what new intents have been discovered. The process of classifying unknown intents into new user intents is called “new intent discovery.” By discovering new intents of users, it can not only identify potential business opportunities but also provide guidance for the future research and development direction of the system and better understand potential user needs. To realize an effective dialogue intent recognition process, obtaining a good intent feature representation is firstly necessary. The quality of the intent representation plays a crucial role in the performance of subsequent intent classification. Therefore, Section 2.1 briefly reviews the research on intent representation. Sections 2.2 and 2.3 introduces the literature related to known and unknown intent detection. Section 2.4 reviews the literature related to new intent discovery. Section 2.5 concludes the chapter and leads the research works in this book through the literature review.
Discovering New Intents with Deep Aligned Clustering

Discovering new intents is a crucial task in dialogue systems. Most existing methods are limited in transferring the prior knowledge from known intents to new intents. These methods also have difficulties in providing high-quality supervised signals to learn clustering-friendly features for grouping unlabeled intents. In this work, we introduce an effective method (Deep Aligned Clustering) to discover new intents with the aid of limited known intent data. Firstly, by leveragin a few labeled known intent samples as prior knowledge to pre-train the model. Then, k-means is performed to produce cluster assignments as pseudo-labels. Moreover, an alignment strategy is proposed to tackle the label inconsistency problem during clustering assignments. Finally, the intent representations are learned under the supervision of the aligned pseudo-labels. With an unknown number of new intents, the number of intent categories is predicted by eliminating low-confidence intent-wise clusters. Extensive experiments on two benchmark datasets show that the method presented is more robust and achieves substantial improvements over the state-of-the-art methods.
Navigating the Knowledge Network: How Inter-Domain Information Pairing and Generative AI Can Enable Rapid Problem-Solving

This study introduces a novel methodological framework that leverages generative AI to retrieve scientific articles pertinent to engineering problems, framed within the context of TRIZ-based contradictions. The process entails searching scientific literature databases by keywords and subsequently prioritizing the resulting articles based on their pertinence to the research subject. Large Language Models are then employed to analyze a refined selection of articles, extracting features and amalgamating individual findings into a summary. Furthermore, we present a strategy towards inter-domain information search. The presented strategy has the potential to be generalized and applied to various domains, facilitating knowledge transfer and problem-solving across different fields.