Reviewerly/exHarmony huggingface dataset:
Service Design Methods for the Design of Smart Surfaces

Smart environments, integrating different sensors and other technology solutions, are one of the key directions in which everyday spaces in buildings are expected to develop in the future. One aspect of smart environments includes large interactive surface areas, which are embedded to walls, tables, or other furniture. This chapter introduces how service design methods have been applied in our research on smart surfaces and discusses different design methods and their strengths in this context. Specifically, we present three service design cases, interactive wallpaper, a smart building infrastructure and a smart cafe table, and how the related use cases have been studied with service design methods. These three design cases are positioned at different maturity levels of the product development process, the interactive wallpaper, and smart building representing early design concepts, and the interactive table a functional prototype.
SFFSORT Multi-Object Tracking by Shallow Feature Fusion for Vehicle Counting

Standard Multi-Object Tracking (MOT) frameworks are currently categorised into three categories: tracking-by-detection, joint detection, and tracking and attention mechanisms. Infrequently, the latter two frameworks require substantial computing resources. The difficulty of implementing real-time tracking does not apply to vehicle detection at traffic crossings. Not only is it essential to meet real-time requirements for vehicle tracking and detection at traffic intersections, but it is also necessary to address common MOT issues such as target occlusion, repetition technology, error detection, etc. Detection-based tracking has a great deal of potential. This study proposed a shallow feature fusion algorithm based on SORT, called SFFSORT and developed an innovative architecture for vehicle monitoring and counting based on detection tracking. This tracking method is more efficient than both SORT and DeepSORT. It achieved 60.9% MOTA and 65.5% IDF1 in MOT16 while MOTA achieved 60.1% and 64.7% IDF1 in MOT17. Utilizing this tracking method as a foundation, we have developed a vehicle counting framework and successfully implemented it on road traffic videos sourced from the Malaysian transportation department. The tracking algorithm presented here effectively addresses the tracking challenges arising from both detection errors and inaccuracies, providing a robust solution. The experimental findings demonstrate that the deep learning framework is capable of achieving lane-level vehicle counting even in scenarios with limited labelled data.
Autoregressive Decoder With Extracted Gap Sessions for Sequential/Session-Based Recommendation

Learning the complex relationships between items in a sequential recommendation system (SRS) and session-based recommendation system (SBRS) is critical for obtaining higher prediction scores. In recent studies, to capture item-item information, items have been represented as the nodes of graph neural networks (GNNs) and the relevance of items with self-/soft attention layers has been calculated. GNNs have been used because standalone attention-based methods focus only on the relative significance of items within a single session, neglecting high-order item-item relationships that change through sessions. The relational summarization task is a natural language processing task that extracts the relationship between two tokens from a related corpus; however, its adaptation to SRS and SBRS is unknown. To fill this lacuna, in this study, the relationships between items from related sessions are extracted using the transformer-based abstractive summarization model PEGASUS. To improve session embedding, the proposed model, named “gap-session transformer" utilizes gap-session masking to learn the relationships between items within different sessions. In addition, a group of sessions are divided into multiple corpus sets based on the theme of each corpus, and the autoregressive beam-search decoder is connected to a transformer decoder for the generation of the next session while auxiliary tasks are performed to enhance the recommendation task. Extensive experiments conducted on the MovieLens1M dataset and Yoochoose dataset verify that our model significantly outperforms the state-of-the-art (SOTA) methods, and the results demonstrate the efficacy of the relational summarization task in recommendation systems.
Estimation of Occlusion Region Using Image Completion by Network Model Consisting of Transformer and U-Net

Object recognition from the images with occlusion was a difficult problem. In this paper, we attempted to solve this problem by image completion. In order to interpolate the hidden regions of objects from the surrounding image data, this paper proposed a new pix2pix type image generation network, in which the transformer was used instead of the convolution network in the generator. In this model, U-Net composed of the transformer blocks encodes the contextual relationships between the pixels, and the followed additional transformer block generate the interpolated image using them. Since the convolution was based on the global features, it could not interpolate the images if the missing regions were large. By replacing it with transformer, it was possible to analogize the missing regions from surrounding pixels. The effectiveness of the proposed method was confirmed by image interpolation experiments for several images with occlusions.
Smoothing piecewise linear activation functions based on mollified square root functions

Activation functions(AFs) are crucial components in neural networks for deep learning.Piecewise linear functions(PLFs) have been widely employed to act as AFs, thanks to their computational efficiencies and simplicities.However, PLFs are not completely differentiable, have potential problems of training.The analytical expressions of AFs based on pure PLFs can be smoothed via mollified square root function(MSRF) method, inspired by SquarePlus method of ReLU approximation.In this paper, we propose a proposition defining AFs as maximum or minimum of two PLFs, and transform the results into a smoothed function via MSRF method.Based on MSRF, we modify the well-known AFs composed of two PLFs, three or four PLFs to regularized ones systematically, including ReLU, LReLU, vReLU, Step, Biplolar, BReLU (Bounded ReLU ), Htanh(Hard Tanh), Pan(Frying pan function), STF (Soft Thresholding Formulas), HTF (Soft Thresholding Formulas), SReLU (S-shaped ReLU ), MReLU (Mexican hat type ReLU ), TSF (Trapezoidshaped function) functions.Additionally, according to the equivalences of SquarePlus and SoftPlus functions, some classic compound AFs, such as ELU, Swish, Mish, SoftSign Logish and DLU functions, can be expressed via MSRF method also.The derivatives of their mollified versions demonstrate their smoothness properties.The proposed method can be extended to AFs composed of multiple PLFs easily, which will be investigated deeply in the future.
A Quantum Policy and Ethics Roadmap

This essay describes the ethical and legal issues that will arise from emerging quantum technologies. Quantum technologies and applications are currently in different levels of maturity, and the full range of potential applications will remain beyond the grasp of our current imagination. Nevertheless, the government and private industry are investing heavily in the first generation of quantum applications, so a preliminary assessment of the potential societal and ethical considerations will be valuable to private industry, the government, and to the general public. This essay provides concrete examples of the first wave of quantum technology applications and describes the possible societal repercussions. In some cases, societal goals will be in tension with one another, requiring difficult trade-offs. The best policies and ethical norms will need to be sensitive to the deployment context and use cases. This Quantum Policy and Ethics Roadmap injects a dose of realism to the discourse on quantum ethics by identifying trade-offs and acknowledging that the process of balancing risks and benefits is likely to be complex and contested.
A Practical Recipe for Federated Learning Under Statistical Heterogeneity Experimental Design

Federated Learning (FL) has been an area of active research in recent years. There have been numerous studies in FL to make it more successful in the presence of data heterogeneity. However, despite the existence of many publications, the state of progress in the field is unknown. Many of the works use inconsistent experimental settings and there are no comprehensive studies on the effect of FL-specific experimental variables on the results and practical insights for a more comparable and consistent FL experimental setup. Furthermore, the existence of several benchmarks and confounding variables has further complicated the issue of inconsistency and ambiguity. In this work, we present the first comprehensive study on the effect of FL-specific experimental variables in relation to each other and performance results, bringing several insights and recommendations for designing a meaningful and well-incentivized FL experimental setup. We further aid the community by releasing FedZoo-Bench, an open-source library based on PyTorch with pre-implementation of 22 state-of-the-art methods1, and a broad set of standardized and customizable features available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/MMorafah/FedZoo-Bench</uri> . We also provide a comprehensive comparison of several state-of-the-art (SOTA) methods to better understand the current state of the field and existing limitations.
Banking on Cryptography

In the 21st Century, banks have, through injudicious actions, seriously damaged their image for Trustworthiness with their customers.This paper discusses how modern cryptography may be employed to help restore trust in banking, at the same time making banking more secure, private and reliable.Efficient tools and techniques already exist to make the transition to a most trustworthy banking system using cryptography, but it will require taking a big-picture perspective of the problem. At the moment, individual banks and regulators are dipping their toes into the complex word of cryptography but that is only resulting in a series of disjointed, piecemeal solutions that, in the end, will, at best, only solve part of the problem and, after much expenditure. will have eventually to be rebuilt to more reliable standards.Cryptography is not a ‘silver bullet’ to the problem of re-establishing trust across the financial system but will be a key tool in helping customers to regain trust in the banking system. The paper first discusses the issue of Trust, in particular the changes in technology and organizations that have eroded citizen’s trust in their banks. The ‘friendly’ bank, familiar to all in the 20th Century is disappearing fast.After providing a necessarily brief history of Cryptography, focusing on Modern Cryptography, the paper proposes a high-level architecture for embedding Cryptography into modern banking, always recognizing the difficulties that changing any part of the global banking system will entail.
Requirements Elicitation Based on Psycho-Pedagogical Theatre for Context-Sensitive Affective Educational Recommender Systems

Educational Recommender Systems (ERSs), intelligent tutoring systems that adapt their pedagogical recommendations to each student, are becoming increasingly common. Context-Sensitive Affective Educational Recommender Systems (CSAERSs) personalize the recommendations according to a learning context with multiple dimensions, including the affective dimension and the personality traits of the user. To date, in the field of educational technology, there is little or no research that focuses on offering context-sensitive, personalized, psycho-pedagogical affective support to distance-learning students in real time. Nor do there seem to be any proposals for approaches to the knowledge engineering (term which encompasses knowledge acquisition and knowledge representation) of these systems, in which the relation between the user and his or her context is crucial. There is little work on a systematic approach to the requirements-elicitation phase and to the use of ontologies in the development and validation of ERSs, in general, and CSAERSs, in particular. In this article, we report on a student-centred requirements-elicitation methodology that uses psycho-pedagogical theatre in combination with student surveys. We then illustrate its application in the design and validation of an ontology, together with a semantic-similarity function, that could serve as the nucleus of a CSAERS.
PPDF: A Privacy-Preserving Cloud-Based Data Distribution System With Filtering

Cloud computing has emerged as a popular choice for distributing data among both individuals and companies. Ciphertext-policy attribute-based encryption (CP-ABE) has been extensively used to provide data security and enable fine-grained access control. With this encryption technique, only users whose attributes satisfy the access policy can access the plaintext. In order to mitigate the computational overhead on users, particularly on lightweight devices, partial decryption has been introduced, where the cloud assists in performing the decryption computations without revealing sensitive information. However, in this process, the cloud obtains the user's attributes, thus infringing on the user's privacy. To address this issue, this paper proposes a privacy-preserving cloud-based data distribution system with filtering (PPDF) to enable partial decryption without revealing the user's attributes. The proposed system also employs an edge server to assist the user in filtering out invalid ciphertexts, i.e., ciphertexts where the user's attributes do not satisfy the access policy, and transmit only the valid partially decrypted ciphertexts to the data receiver. Consequently, the proposed PPDF scheme achieves constant decryption cost for the data receiver. We provide a security proof and a performance evaluation of the proposed scheme, which confirms its effectiveness and practicality in various real-world applications.
Exploring Image Unified Space for Improving Information Technology for Person Identification

This paper describes the study of conditions and parameter settings of the algorithm, that underlies in the basis of information technology of person recognition and identification, for creating a unified space for both low-quality and high-quality images with the purpose of developing the requirements to the input images, that will allow correctly identify images with different characteristics. For experimental research the algorithm was used that underlies in information technology of person identification in video stream. It includes the anisotropic diffusion as an image preprocessing method, Gabor wavelet transform as an image processing method, histogram of oriented gradients (HOG) and local binary patterns in 1-dimensional space (1DLBP) as the methods of feature vector extraction from the images. In this work anisotropic diffusion was applied with the parameter that preserves sharper boundaries than previous formulations and improves the automatic stopping of the diffusion. In the study three person images databases were used: The Database of Faces, Facial Recognition Technology (FERET) database and Surveillance Cameras Face Database (SCface). The algorithm performance provided various identification accuracy rate results with the difference, which amount to 20% in average. It arose the issue of input images space mismatching that significantly affected the algorithm performance. Therefore, it has been decided to perform the experimental research to search the possibility to create unified space of the requirements to the input images that will allow correctly identify images from different databases. So, there were performed the experiments with the image compression variety, difference of image resolutions and areas of face regions covering the images.
What Do User Experience Professionals Discuss Online? Topic Modeling of a User Experience Q&amp;A Community

Questioning and answering (Q&A) communities have been widely used by user experience (UX) professionals to exchange knowledge online. As online content has been increasingly generated by professional users in the community, it becomes infeasible for human experts to manually analyze those discussions. Thus, automatic and intelligent analysis of the online content generated by UX professionals is needed to understand how UX knowledge is created and maintained by professionals in online communities. This research offers a comprehensive understanding of user-generated content in a UX Q&A community through topic modeling, an intelligent text-mining approach for discovering hidden topics from textual documents. Specifically, this research identifies 40 important latent discussion topics from a dataset containing 31,314 questions and 80,579 answers posted by UX professionals. Those topics are classified into 8 major categories, followed by popularity and content cohesion analysis of those categories and their mapping to a well-established design thinking process model. Overall, this research contributes a systematic exploration of user-generated content by UX professionals in a Q&A community and highlights opportunities for UX researchers and practitioners.
Virtual Reality for Smart Government – Requirements, Opportunities, and Challenges

Virtual Reality (VR) might enable promising novel applications to involve citizens and businesses in governmental processes. As the body of literature in the field of VR in digital government is limited, we conducted an exploratory interview study with experts from the fields of e-government (both academia and practice) and VR technology to gain more knowledge about the potential utilization of the technology for smart government. The semi-structured interviews were encoded around thematic topics that emerged from the data. The findings cover important requirements, chances and opportunities, risks and challenges as well es potential application scenarios. Among other factors, authentication has been identified both a requirement and a core challenge to apply VR in governmental services. Despite recent decreases in prices of VR devices, our experts considered current costs still high for general interest. Nevertheless, promising potential applications such as community gatherings or the immersive presentation of urban planning projects have been identified.
BERT-Aided Sequence Detection With Natural Redundancy

In this letter, we consider exploiting the natural redundancy (NR) that widely exists in the transmitted sources to improve the performance of communication signal detection. To exploit the abundant NR in the sources, we introduce the pre-trained models (PTMs) with a powerful capability to capture knowledge from massive labeled and unlabeled data into sequence detection. Especially for uncompressed Chinese text sources, we propose an effective sequence detection scheme aided by the widely used Bidirectional Encoder Representation from Transformers (BERT) deep learning model and combined with the beam search (BS) algorithm, which can achieve approximate sequence MAP estimation with an acceptable complexity. Simulation results demonstrate that the bit error rate (BER) performance of our proposed BERT-aided BS scheme for sequence detection significantly outperforms the classical detector.
Differentially Private Social Graph Publishing With Nearest Neighbor Structure Preservation

The goal of privacy-preserving social graph publishing is to protect individual privacy while preserving graph utility. Node nearest neighbor structure is a crucial social graph utility as it is the basis for many graph analysis tasks. Most existing methods with differential privacy focus on preserving degree distribution yet neglect the maintenance of connections between nodes’ neighbors. Moreover, they require massive noise added to mask the change of a single edge, thereby rendering poor utility on the neighbor structure. As a result, it is tough to preserve high utility on the neighbor structure under differential privacy. To tackle these problems, we propose Priv-NNS, a private graph publishing algorithm to preserve the neighbor structure while guaranteeing individual privacy. This algorithm first decomposes a graph into subgraphs via extracting a nearest neighbor structure around each node. Then to yield node vectors satisfying differential privacy while preserving the neighbor structure, we design a private graph encoding approach with structure-awareness, which learns topological features of the subgraph by maximizing the co-occurrence probability among nodes. During this process, a novel objective perturbation approach with a random term, only requiring a scalar noise rather than a vector noise, is devised to balance the neighbor structure retained against the noise injected. Through formal privacy analysis, we prove that the yielded synthetic graph obeys ε-edge differential privacy. Experimental results demonstrate that Priv-NNS preserves high utility on the neighbor structure.
MiCro: Modeling Cross-Image Semantic Relationship Dependencies for Class-Incremental Semantic Segmentation in Remote Sensing Images

Continual learning is an effective way to overcome catastrophic forgetting (CF) in incremental learning for semantic segmentation. The existing continual semantic segmentation (CSS) methods of remote sensing (RS) ignore the semantic relationships among pixels across different images, which will lead to disappointing segmentation results, such as edge pixel misclassification and small object omission. In this paper, we propose a framework for modeling cross-image semantic relationship dependencies (MiCro), which aims to learn an inter-class separable and intra-class cohesive feature space from the pixel relationships across various images to ensure that learned categories can prevent CF in the incremental process. Specifically, we exploit the relationships among pixels of images in mini-batch to construct three losses: (a) Cross-image feature relationship distillation (CFRD) loss, which builds a well-structured feature space; (b) Cross-image intra-class feature cohesion (CIFC) loss, which is devised to make intra-class features more cohesive; and (c) Cross-image class-area weighted cross-entropy (CCWCE) loss, which is mainly employed to inversely weight the proportion of category area in mini-batch. The effectiveness of the proposed approach is demonstrated by extensive experiments on three RS semantic segmentation datasets from ISPRS Vaihingen, ISPRS Potsdam, and iSAID. MiCro is superior to the current most advanced methods in most incremental settings, especially improving mIoU by 11.59% on ISPRS Vaihingen, 13.17% on ISPRS Potsdam, and 15.01% on iSAID in the most difficult incremental settings, which promotes the CSS to a state-of-the-art (SOTA) level. The code will be available at https://github.com/RongXueE/MiCro.