Reviewerly/exHarmony huggingface dataset:
Multi-domain and Heterogeneous Data Driven Innovative Problem Solving: Towards a Unified Representation Framework

The exponential increase in the amount of knowledge produced has sparked an interest in the TRIZ community in using artificial intelligence and natural language processing to systematize various steps of the process. However, exploiting large volume of multi-domain and heterogeneous data sources simultaneously is still a work in progress. We propose a general knowledge formalization framework to represent information relative to various data sources. By having formalized representations of various knowledge sources, we can establish stronger links between otherwise unrelated sources and thus pair innovation problems with multi-domain solutions and uncover potential hidden solutions that are hard to discover otherwise. A demonstrative case study related to efficient energy generation is provided to illustrate possible uses of the framework. We noticed that the formalization of some data sources (such as web content) can be challenging since their structure varies wildly. Also, contrary to established sources (patents, scientific articles), web sources can contain a lot of noise or false information.
Effective Data Protection and Direct Cooperation on Digital Evidence

This chapter for the Cambridge Handbook of Digital Evidence in Criminal Investigations (CUP, 2023) explores the relationship between EU data protection law and public-private ‘direct cooperation’ on digital evidence in criminal investigations. It asks whether a prima facie neat separation between the GDPR and the LED always matches the realities of private-to-public data transfers for criminal investigations, and questions whether that legal framework is sufficiently harmonious to warrant description as an EU data protection acquis. The chapter distinguishes scenarios of formal (and informal) direct cooperation, viewing those scenarios through the conceptual prism of data controllership. That frame is then applied to the European Commission’s 2018 ‘e-Evidence package’, along with the other co-legislators’ competing visions, before a first look at the final 2023 compromise text from a data protection perspective. The chapter analyses the extent to which CJEU case law illuminates theoretical blind spots, and critically discusses whether the ongoing strengthening of enforcement powers is likely to herald not only greater legal certainty on the supply of digital evidence but also meaningful, workable data subject rights. The chapter closes with a reflection on the future place of EU data protection standards within the Council of Europe’s own new direct cooperation mechanism – that of the Second Additional Protocol to the Budapest Convention.
Unknown Intention Detection Method Based on Dynamic Constraint Boundary

Open intent classification is a challenging task in dialogue systems. On the one hand, ensuring the classification quality of known intents is crucial. On the other hand, identifying open (unknown) intent during testing. Current models are limited in finding the appropriate decision boundary to balance the performances of both known and open intents. In this chapter, a post-processing method is proposed to learn the adaptive decision boundary (ADB) for open intent classification. The model is initially pre-trained using labeled known intent samples. Then, well-trained features to automatically learn the adaptive spherical decision boundaries for each known intent. Specifically, a new loss function is introduced to balance both the empirical risk and the open space risk. This method does not need open samples and is free from modifying the model architecture. This approach is surprisingly insensitive with less labeled data and fewer known intents. Extensive experiments conducted on three benchmark datasets demonstrate significant improvements compared to state-of-the-art methods.
Exploring the Potential of Immersive Virtual Environments for Learning American Sign Language

Sign languages enable effective communication between deaf and hearing people. Despite years of extensive pedagogical research, learning sign language online comes with a number of difficulties that might be frustrating for some students. Indeed, most of the existing approaches rely heavily on learning resources uploaded on websites, assuming that users will frequently consult them; however, this approach may feel tedious and uninspiring. To address this issue, several researchers have started looking into learning sign language in a game-based environment. However, the majority of the existing work still relies on website-based designs, with only a very few proposed systems providing an immersive virtual environment, and there are no user studies comparing website-based and immersive virtual environments. In this paper, we present an immersive environment for learning numbers 0–9 in American Sign Language (ASL). Our hypothesis is that an immersive virtual environment can provide users with a better learning experience and that users will show a higher level of engagement compared to website-based learning. We conducted a questionnaire-based user survey, and our initial findings suggest that users prefer to learn in an immersive virtual environment.
Agile Teamwork Quality – Reflect Your Team While Playing and Identify Actions for Empowerment

Agile working is based on teams. To perform their tasks each team has to find ways to establish and improve the way they work. Agile Teamwork Quality is an approach to reflect and improve the teamwork capability systematically. To reflect the current team quality level a gamification approach is proposed. The game can be part of the cyclic retrospectives or other team events. The game presents typical scenarios respectively cases which can happen. The team has to argue why this also can happen or not in their team. These case discussion promote awareness about the team’ current situation. The awareness is used to initiate improvement actions in real world after the game. The presented case study of the Volkswagen Group IT shows the facilitation of the teamwork quality.
Function-Oriented Search, TRIZ and the Sustainable Use of Information Resources

Finding leading areas and industries is currently not supported within Function-Oriented search. The proposed workflow aims at a systematic approach to help finding possible leading areas together with the implementation of existing resources. This increases the sustainability because the amount of used human resources is decreased and the utilisation of present information resources is increased. Thus, the effort within development is improved which increases the efficiency. The application and value of Function-Oriented Search is simplified, and the level of subjectiveness is reduced. The proposed workflow was tested with an example. Therefore, it should be substantiated with additional example problems in different fields of industry. Furthermore, during the work a change of the resources were perceived. This could lead to further research if those changes could be predicted. Expert systems and AI could enhance the process by automated analysis of search results or suggestions where to perform additional searches.
Intelligent Character Recognition of Handwritten Forms with Deep Neural Networks

The automatic processing of handwritten forms remains a challenging task, wherein detection and subsequent classification of handwritten characters are essential steps. We describe a novel approach, in which both steps - detection and classification - are executed in one task through a deep neural network. Therefore, training data is not annotated by hand, but manufactured artificially from the underlying forms and yet existing datasets. It can be demonstrated that this single-task approach is superior in comparison to the state-of-the-art two task approach. The current study focuses on hand-written Latin letters and employs the EMNIST data set. However, limitations were identified with this data set, necessitating further customization. Finally, an overall recognition rate of 88.28% was attained on real data obtained from a written exam.
Concept and Implementation of BIM-to-World Co-registration for Mixed Reality Applications

Mixed reality tools have witnessed a surge in meaningful applications within the construction industry. A critical prerequisite for these tools is the proper alignment or co-registration of the real world with the virtual and digital content, a process referred to as co-registration at the beginning, and tracking during users’ movement. Notably, the majority of showcased use cases focus primarily on superimposing innovative virtual design content onto tablets (AR) and see-through devices (MR) that have already been accurately registered and aligned, without providing insights into the registration process. Although most of the available MR devices perform well in tracking the device’s position and orientation during user movement, the co-registration procedure is usually manual, tedious, and constitutes a significant shortcoming in the overall user experience and MR tool adoption. This paper presents a distributed client-server concept for a co-registration process for MR devices. The server process is bifurcated into a semi-automated rough registration part that uses minimal user input, and a fully automated fine registration part employing the Iterative Closest Point algorithm. The client part runs the projection, visualization, and tracking process utilizing the initial co-registration data. The presented concept is implemented and tested on the Microsoft HoloLens as the MR-client and a software server that runs a dedicated Python script. Experiments and results demonstrate that the presented concept and implementation effectively support the co-registration process with minimal user input, offering reasonable accuracy and latency, even when the corresponding part of the real world has not been captured previously.
A Quantum Computing Approach for the Unit Commitment Problem

Planning energy production is a challenging task due to its cost-sensitivity, fast-moving energy markets, uncertainties in demand, and technical constraints of power plants. Thus, more complex models of this so-called unit commitment problem (UCP) have to be solved more rapidly, a task that probably can be solved more efficiently via quantum computing. In this article, we model a UCP with minimum running and idle times as a quadratic unconstrained optimization problem to solve it on quantum computing hardware. First experiments confirm the advantages of our formulation in terms of qubit usage and connectivity and most importantly solution quality.
Augmented Intelligence in Tutoring Systems: A Case Study in Real-Time Pose Tracking to Enhance the Self-learning of Fitness Exercises

In technology enhanced learning is development of psycho-motor skills an area with a lot of potential, which is enabled by rapid improvements of sensors and wearable devices, combined with artificial intelligence. Here we focus on fitness exercises and present a novel approach based on computer vision techniques to track the practitioner’s pose and provide automatically real-time feedback for improvement, based on the input from an expert trainer. Taking into account the gathered data and ground-truth poses, the proposed pipeline can learn actively from a professional trainer demonstrating an exercise in front of a camera or passively from a recorded video. In our experiment, we used professional fitness exercise videos as the ground truth and measured the performance of five inexperienced participants. The results show positive responses from participants, indicating the feasibility of the proposed approach as well as suggestions for its further improvement.
Dialogue System

With the rapid development of Internet technology and the increasing popularity of intelligent robots and intelligent hardware devices, traditional keyword-based search engine information retrieval is no longer sufficient. Dialogue systems that enable users to interact naturally with robots and devices through voice dialogue and other natural language methods have gained attention and have a profound impact on daily life. Dialogue systems can be non-task-oriented or task-oriented, and can save labor costs and improve work efficiency by replacing repetitive mental work. However, building a human-computer dialogue system is challenging, especially the natural language understanding module that converts user input into structured semantic representations. Discovering unknown user intents that have not appeared in the training set and have not been identified is crucial for improving service quality and providing personalized services. Existing research on new intent discovery is divided into three schools based on unknown intent detection, non-clustering, and semi-clustering, but accurately identifying and understanding new types of user intents is still challenging. Efficiently identifying intents is a key step in answering user questions, and continuous optimization of intent recognition and discovery algorithms is necessary. The discovery of new intents in dialogue is a promising research field with high commercial value.
Unknown Intent Detection Based on Large-Margin Cosine Loss

Identifying the unknown (novel) user intents that have never appeared in the training set is a challenging task in the dialogue system. This chapter presents a two-stage method for detecting unknown intents. The method use a bidirectional long short-term memory (BiLSTM) network with the margin loss as the feature extractor. With margin loss, the network is encouraged to maximize inter-class variance and minimize intra-class variance, thereby learning descriminative deep feature. Then, the feature vectors are fed into a density-based novelty detection algorithm, local outlier factor (LOF), to detect unknown intents. Experiments on two benchmark datasets show that the proposed method can yield consistent improvements compared with the baseline methods.
NFTs Enabling Ownership and Value in the Metaverse

Non-fungible tokens (NFTs) revolutionize digital property rights and the representation of value. NFTs provide various digital assets with transparency, immutability, and traceability by leveraging blockchain technology and smart contracts. NFTs represent tangible assets digitally, bridging the gap between the physical and digital worlds. Luxury brands such as BMW, Balenciaga, Dolce &amp; Gabbana, Louis Vuitton, and Gucci use NFTs in the metaverse to facilitate distinct brand experiences and expression. NFTs authenticate digital identities and property in virtual environments, thereby promoting self-expression and social interactions. They establish a new economic paradigm, facilitating the exchange of value across domains and the preservation of intellectual property. Scalability, sustainability, and legal issues present themselves, necessitating solutions such as layer-2 scaling and copyright protection. It is essential for platforms, creators, and regulators to collaborate in order to establish standards and frameworks.
Unknown Intent Detection Method Based on Model Post-Processing

With the maturity and popularity of dialogue systems, detecting user’s unknown intent in dialogue systems has become an important task. It is also one of the most challenging tasks, as it is difficult to obtain examples, prior knowledge or the exact numbers of unknown intents. In this chapter, we introduce SofterMax and deep novelty detection (SMDN), a simple yet effective post-processing method for detecting unknown intent in dialogue systems based on pre-trained deep neural network classifiers. Our method can be flexibly applied on top of any classifiers trained in deep neural networks without changing the model architecture. By calibrating the confidence of the softmax outputs to compute the calibrated confidence score (i.e., SofterMax) and use it to calculate the decision boundary for unknown intent detection. Furthermore, the feature representations learned by the deep neural networks are fed into a traditional novelty detection algorithm to detect unknown intents from different perspectives. Finally, the methods mentioned above are combined to facilitate joint prediction. The proposed method classifies examples that differ from known intents as unknown and does not require any examples or prior knowledge of it. Extensive experiments have been conducted on three benchmark dialogue datasets. The results show that our method can yield significant improvements compared with the state-of-the-art baselines.
Multi-size Scaled CAM for More Accurate Visual Interpretation of CNNs

The search for decision bases for image classification neural networks is one of the popular research directions in deep neural network interpretability. These studies highlight regions of interest to image classification models by generating a saliency map that assign contribution values to each pixel in the input image. However, these current methods cannot accurately locate the key features of the target object and tend to include other irrelevant objects in the salient region, resulting in unreliable saliency maps. In addition, the problems of noise and low resolution have also plagued the researchers. To address the above issues, we propose a saliency map generation method based on multi-size scaling of the input image for the CNN-based model. The method scales the input images in multiple sizes and extracts different resolution feature maps and their corresponding gradients from specific convolutional layers, and fuses them as masks of the input images. Then the masked input images are input to the model separately to obtain the weights of each mask, and finally the masks are combined with linear weighting to obtain the saliency map. Experiments show that our method can produce more detailed saliency maps and accurately target regions of interest to the model. It has significant advantages and higher application value than the current CAM-based (class activation mapping) methods.
A Study of Chinese-English Translation Teaching Based on Data Mining

The paper presents specific applications of data mining methods in the analysis of the data obtained from the translation process. The authors take knowledge nodes involved in Textwells platform as the analysis object, importing the data into Gephi, one of the data mining tools, to generate and visualize the relevance graph of all related knowledge nodes and analyze the hidden characteristics. This effort concludes that there are top four communities in the knowledge-based network, namely, semantic matching, syntactic matching, word-class alteration, rank alteration. In these four communities, the co-occurrence of the knowledge nodes is more prominent, which definitely will help teachers with their translation teaching. In addition, the authors review the existing Chinese-English translation courses on the Chinese University MOOCs platform, study the teaching syllabuses carefully and suggest redesigning and modifying the current translation courses based on the findings above so as to improve the teaching effectiveness and efficiency.
Rethinking and unravelling the complexity of breast cancer: an integrative approach to detection and diagnosis

Breast cancer is one of the most common types of cancer and a large contributor to cancer-related deaths in women worldwide. Early diagnosis of breast cancer significantly improves survivor rates. In order to diagnose breast cancer, this research study set out to construct a neural network model for the early detection of breast cancer as either benign or malignant. A reliable dataset, the Wisconsin Breast Cancer Dataset, was used to develop the algorithm. As a result, a dataset with only 30 features was created after several redundant characteristics were dropped. Initially, the dataset had 33 features, but only 30 features and 569 pertinent data points were used to assess the algorithm&apos;s performance. The findings demonstrated that the proposed artificial neural network model successfully detected breast cancer using the provided dataset, with an overall accuracy of 98.24% and a loss of 6.38%. These results imply that this algorithm could be useful for helping radiologists reliably and quickly discover breast cancer, which could ultimately result in early identification and better patient outcomes.
On the Service Quality of Cooperative VR Applications in 5G Cellular Networks

With the improvement of virtual reality (VR) technology and the spread of VR equipment, attention to VR services has increased, and a variety of new VR services have appeared. Among them VR services with interaction require real time synchronization of the user’s position in the space, actions taken, and other information, so it is important to increase the speed of communication to maintain the quality of the user experience. However, when synchronizing information between users using a cellular network, it is difficult to speed up communication for all terminals because multiple terminals share the same network and the cellular resources allocated to each terminal are limited. Therefore, it is necessary to clarify the characteristics of applications that should be prioritized for communication among VR applications that involve interaction and the extent to which communication delay affects the quality of user experience in order to do appropriate cellular resources allocation that do not impair the quality of user experience. In this study, VR services that involve information processing of user interaction are implemented on actual devices, and the user experience quality is measured in an environment with multiple different communication delays. The results provide data on the characteristics of VR applications with interaction that should reduce the amount of delay, as well as data related to the quality of the experience when running in own 5G environment.
kProp: Multi-neuron Relaxation Method for Neural Network Robustness Verification

With the increasing application of neural networks in safety-critical domains, their robustness becomes a crucial concern. In this paper, we present a multi-neuron relaxation-based verification framework kProp for ReLU neural networks with adversarial distortions in general norms. In contrast with existing verification methods tackling general distortion norms, the proposed multi-neuron relaxation method is able to capture the relations among a group of neurons, thus providing tighter convex relaxations and improving verification precision. In addition, existing methods based on linear relaxation may include infeasible inputs to the neural network for robustness verification, which further leads to verification precision loss. To address this problem, we propose a region clipping method to exclude infeasible inputs to further improve the verification precision. We implement our verification framework and evaluate its performance on open-source benchmarks. The experiments show that kProp can produce precise verification results where existing verification methods fail to produce conclusive results, and can be applied to neural networks with more than 4k neurons in general distortion norms.