Reviewerly/exHarmony huggingface dataset:
Gestural interaction in the kitchen: Insights into designing an interactive display controlled by hand specific on-skin gestures

The kitchen is one of the busiest and messiest hubs of a home, where the hands are usually spoiled with food. In this setting, gestural interaction can offer several advantages: efficient, intuitive, and touch-free orchestration of interactive devices. Yet, research scarcely investigates the user's perspective on gesture-based systems in the kitchen and lacks designs developed through a user-centred process. With the contribution of 234 participants, we conducted formative studies that investigated users' expectations, perceived benefits, and concerns of gesture-based control in the kitchen. These studies guided our conceptual design ‘2HandTouch’, an information display controlled by Hand Specific On-Skin Gestures (HSoS). Then, we evaluated the user experience of this design through a summative user study. We introduced four perceived benefits of HSoS use in the kitchen that has not been visible in such detail. These are i) hygiene, (ii) time management, (iii) imminent control, and (iv) an uninterrupted cooking practice. We also provided broader implications of our work for designing gesture-controlled devices in the kitchen context.
A Logic for Paraconsistent Transition Systems

Modelling complex information systems often entails the need for dealing with scenarios of inconsistency in which several requirements either reinforce or contradict each other. In this kind of scenarios, arising e.g. in knowledge representation, simulation of biological systems, or quantum computation, inconsistency has to be addressed in a precise and controlled way. This paper generalises Belnap-Dunn four-valued logic, introducing paraconsistent transition systems (PTS), endowed with positive and negative accessibility relations, and a metric space over the lattice of truth values, and their modal logic.
Virtual Mobility Adoption: a Study of Factors Affecting Students’ Satisfaction toward an Online English Program at a Young Vietnamese Higher Education Institution

In this paper, the success of delivering an online student mobility program at X University, one of Vietnam's top private educational institutions, is evaluated. A case study research approach was used to examine the concept, delivery method, and post-program evaluation of an online English program for Japanese students provided by X University's globalization department. The student satisfaction survey was analyzed based on the critical success factors of online learning in order to identify the program's strengths and weaknesses. From there, future opportunities to improve such programs are determined. Although this study is limited from the perspective of X University and online language training in student mobility, it provides a good example of the application and implementation of online components in a student mobility program. The study's results contribute to the quality of future virtual mobility programs in the studied institution and provide an initial benchmark for those of similar sizes and conditions to promote their own online student mobility adoptions.
Reducing Redundant Test Executions in Software Product Line Testing—A Case Study

In the context of software product line (SPL) engineering, test cases can be reused for testing a family of products that share common parts of source code. An approach to test the products of a product family is to exhaustively execute each test case on all the products. However, such an approach would be very inefficient because the common parts of source code will be tested multiple times unnecessarily. To reduce unnecessary repetition of testing, we previously proposed a method to avoid equivalent test executions of a product line in the context of regression testing. However, it turns out that the same approach can be used in a broader context than just regression testing of product families. In this paper, we argue the generality of the method in the sense that it can be used for testing of the first version of a product family as well as regression testing of its subsequent versions. In addition, in this paper, in order to make the method practically usable for users, we propose a process for applying it to SPL testing. We demonstrate the generality of our method and the practical applicability of the proposed process for the method by conducting a case study.
Security enhanced optical image cryptosystem based on phase encoding by generating a sparse volumetric ciphertext

Previously proposed optical image cryptosystems based on phase encoding mainly utilized 2D phase retrieval to retrieve ciphertexts, and the relationship between the plaintext and the ciphertext is very simple, which makes those cryptosystems vulnerable to several attacks. In this paper, we propose a security enhanced optical image cryptosystem based on phase encoding by generating a sparse volumetric ciphertext. Several complementary binary amplitude masks (BAMs) without overlapping are used as constraints to build a sparse volume field, and a sparse volumetric ciphertext can be retrieved by a novel volumetric phase retrieval algorithm. Afterwards, all pixels of the volumetric ciphertext are multiplexed into one 2D ciphertext for convenient storage and transmission. For decryption, the pixels should first be redistributed into the volume field. Once a beam with correct wavelength crosses the volumetric field sequentially, the decrypted image can be obtained by recording an intensity pattern. In this scheme, the parameters of the volume field serve as additional keys, which enlarges the key space and confuses the relationship between the plaintext and the ciphertext to realize much higher security. The effectiveness and security of this scheme is verified by some numerical simulations.
Fault Diagnosis in Regenerative Braking System of Hybrid Electric Vehicles by Using Semigroup of Finite-State Deterministic Fully Intuitionistic Fuzzy Automata

Regenerative braking is one of the most promising and ecologically friendly solutions for improving energy efficiency and vehicle stability in electric and hybrid electric cars. This research describes a data-driven method for detecting and diagnosing issues in hybrid electric vehicle regenerative braking systems. Early fault identification can help enhance system performance and health. This study is centered on the construction of an inference system for fault diagnosis in a generalized fuzzy environment. For such an inference system, finite-state deterministic fully intuitionistic fuzzy automata (FDFIFA) are established. Semigroup of FDFIFA and its algebraic properties including substructures and structure-preserving maps are investigated. The inference system uses FDFIFA semigroups as variables, and FDFIFA semigroup homomorphisms are employed to illustrate the relationship between variables. The newly established model is then applied to diagnose the possible fault and their nature in the regenerative braking systems of hybrid electric vehicles by modeling the performance of superchargers and air coolers. The method may be used to evaluate faults in a wide range of systems, including autos and aerospace systems.
Block chain Based Secure Data Transmission among Internet of Vehicles

To address the issues that traditional Internet of Vehicles (IoV) data is readily manipulated with and access control is inflexible, a security sharing method for IoV data is presented that utilizes blockchain technology and weighted cipher text policy attribute-based encryption. Maintain the production, verification, and storage of blocks, implement distributed data storage, and secure the integrity of data; access control of data on the chain based on characteristics to ensure that only authorized visitors may access data content; For the Internet of Vehicles data access, a multi-attribute-based hierarchical access policy formulation approach is built by mining the association relationship between points and permissions between roles. This method simplifies the complexity of access control policies. The built-in hierarchical access policy formulation approach efficiently reduces the computation and transmission overhead of cars while meeting the needs of the Internet of Vehicles scenario for access to various entities and different roles.
Self-supervised Video Representation Learning with Cascade Positive Retrieval

Self-supervised video representation learning has been shown to effectively improve downstream tasks such as video retrieval and action recognition. In this paper, we present the Cascade Positive Retrieval (CPR) that successively mines positive examples w.r.t. the query for contrastive learning in a cascade of stages. Specifically, CPR exploits multiple views of a query example in different modalities, where an alternative view may help find another positive example dissimilar in the query view. We explore the effects of possible CPR configurations in ablations including the number of mining stages, the top similar example selection ratio in each stage, and progressive training with an incremental number of the final Top-k selection. The overall mining quality is measured to reflect the recall across training set classes. CPR reaches a median class mining recall of 83.3%, outperforming previous work by 5.5%. Implementation-wise, CPR is complementary to pretext tasks and can be easily applied to previous work. In the evaluation of pretraining on UCF101, CPR consistently improves existing work and even achieves state-of-the-art R@1 of 56.7% and 24.4% in video retrieval as well as 83.8% and 54.8% in action recognition on UCF101 and HMDB51. The code is available at https://github.com/necla-ml/CPR.
Jitsi360: Using 360 Images for Live Tours

In this poster, we present a system for sharing immersive 360 images for live tours. While the sharing of prerecorded 360 video and images is becoming commonplace, there have been fewer systems presented that support live-sharing of 360 images. We customised a video conferencing platform to enable dozens of people to see the same 360 content together while having a live call. We describe our system and pilot user study results from using it for a virtual guided tour. Compared to sharing non 360 images on Zoom, our system was felt to be more immersive, enjoyable, and easy to use.
Translation of Natural Language Requests to API

Natural language interface or NLI was analyzed concerning its use in software packages and solutions. The paper highlights advantages, strategies and different methods of building NLI for Application programming interface or API. The methods considered for building structure of NLI are Markov decision strategy, IFTTT methodology, and the modern solution of NL2API. The paper describes the three stages of NLI processing. An example prototype of implementation of the request for receiving data for further processing is shown.
Macro-charcoal in Carbonaceous Strata of The Lower Cretaceous Of Northwest India: Remains from the Than Formation, Saurashtra Basin, Gujarat

For correspondence
Transformer-Based Person Search Model with Symmetric Online Instance Matching

Person search is a challenging retrieval problem which aims at matching pedestrians with the same identity over non-overlapping camera views. In this paper, we adopt Swin Transformer as the backbone network to extract discriminative features. We propose a symmetric online instance matching loss which transfers the symmetric idea from KL divergence to the online instance matching loss. The purpose is to strengthen the robustness of the person search model under the condition of limited training identities. We compared with the state-of-the-arts on two mainstream benchmarks: CUHK-SYSU and PRW datasets. Experimental results demonstrate the effectiveness of our method. Especially, we achieve better performance on the PRW dataset with an improvement of 6.5% and 3.5% at the mAP and top-1 accuracy, respectively.
A facial expression recognizer using modified ResNet-152

In this age of artificial intelligence, facial expression recognition is an essential pool to describe emotion and psychology. In recent studies, many researchers have not achieved satisfactory results. This paper proposed an expression recognition system based on ResNet-152. Statistical analysis showed our method achieved 96.44% accuracy. Comparative experiments show that the model is better than mainstream models. In addition, we briefly described the application of facial expression recognition technology in the IoT (Internet of things).
NEX<sup>+</sup>: Novel View Synthesis with Neural Regularisation Over Multi-Plane Images

We propose Nex <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> , a neural Multi-Plane Image (MPI) representation with alpha denoising for the task of novel view synthesis (NVS). Overfitting to training data is a common challenge for all learning-based models. We propose a novel solution for resolving such issue in the context of NVS with signal denoising-motivated operations over the alpha coefficients of the MPI, without any additional requirements for supervision. Nex <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> contains a novel 5D Alpha Neural Regulariser (ANR), which favors low-frequency components in the angular domain, i.e., the alpha coefficients’ signal sub-space indicating various viewing directions. ANR’s angular low-frequency property derives from its small number of angular encoding levels and output basis. The regularised alpha in Nex <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> can model the scene geometry more accurately than Nex, and outperforms other state-of-the-art methods on public datasets for the task of NVS.
Explainable Artificial Intelligence for Authorship Attribution on Social Media

One of the major modern threats to society is the propagation of misinformation — fake news, science denialism, hate speech — fueled by social media’s widespread adoption. On the leading social platforms, millions of automated and fake profiles exist only for this purpose. One step to mitigate this problem is verifying the authenticity of profiles, which proves to be an infeasible task to be done manually. Recent data-driven methods accurately tackle this problem by performing automatic authorship attribution, although an important aspect is often overlooked: model interpretability. Is it possible to make the decision process of such methods transparent and interpretable for social media content considering its specificities? In this work, we extend upon LIME — a model-agnostic interpretability technique — to improve the explanations of the state-of-the-art methods for authorship attribution on social media posts. Our extension allows us to employ the same input representation of the model as interpretable features, identifying important elements for the authorship process. We also allow coping with the lack of perturbed samples in the scenario of short messages. Finally, we show qualitative and quantitative evidence of these findings.
A Feature Regularization Based Meta-Learning Framework for Generalizing Prostate Mri Segmentation

Magnetic Resonance Imaging acquired by different operators and devices often vary greatly, causing the domain shift problem, where deep learning models trained from existing data sources perform poorly on other data sources. This paper proposes a novel feature regularization based meta learning framework to address this problem. In particular, we design a domain discriminator module to regularize the encoder to extract domain-invariant features, and an image reconstruction module to regularize the shape compactness of predictions for target domain data. We evaluate our method on three public prostate MRI datasets. Experimental results show that our approach has better segmentation performance and more powerful generalization performance.
Fusing Multimodal and Anatomical Volumes of Interest Features Using Convolutional Auto-Encoder and Convolutional Neural Networks for Alzheimer’s Disease Diagnosis

Alzheimer’s disease (AD) is an age-related disease that affects a large proportion of the elderly. Currently, the neuroimaging techniques [e.g., magnetic resonance imaging (MRI) and positron emission tomography (PET)] are promising modalities for AD diagnosis. Since not all brain regions are affected by AD, a common technique is to study some region-of-interests (ROIs) that are believed to be closely related to AD. Conventional methods used ROIs, identified by the handcrafted features through Automated Anatomical Labeling (AAL) atlas rather than utilizing the original images which may induce missing informative features. In addition, they learned their framework based on the discriminative patches instead of full images for AD diagnosis in multistage learning scheme. In this paper, we integrate the original image features from MRI and PET with their ROIs features in one learning process. Furthermore, we use the ROIs features for forcing the network to focus on the regions that is highly related to AD and hence, the performance of the AD diagnosis can be improved. Specifically, we first obtain the ROIs features from the AAL, then we register every ROI with its corresponding region of the original image to get a synthetic image for each modality of every subject. Then, we employ the convolutional auto-encoder network for learning the synthetic image features and the convolutional neural network (CNN) for learning the original image features. Meanwhile, we concatenate the features from both networks after each convolution layer. Finally, the highly learned features from the MRI and PET are concatenated for brain disease classification. Experiments are carried out on the ADNI datasets including ADNI-1 and ADNI-2 to evaluate our method performance. Our method demonstrates a higher performance in brain disease classification than the recent studies.
Population size influence on the energy consumption of genetic programming

Evolutionary Algorithms (EAs) are routinely applied to solve a large set of optimization problems. Traditionally, their performance in solving those problems is analyzed using the fitness quality and computing time, and the effect of evolutionary operators on both metrics is routinely used to compare different versions of EAs. Nevertheless, scientists face nowadays the challenge of considering the energy efficiency in addition to computational time, which requires studying the energy consumption of algorithms. This paper discusses the interest of introducing power consumption as a new metric to analyze the performance of standard genetic programming (GP). Two well-studied benchmark problems are addressed on three different computing platforms, and two different approaches to measure the power consumption have been tested. Analyzing the population size, the results demonstrates its influence on the energy consumed: a non-linear relationship was found between size and energy required to complete an experiment. This analysis was extended to the cache memory and results show an exponential growth in the number of cache misses as the population size increases, which affects the energy consumed. This study shows that not only computing time or solution quality must be analyzed, but also the energy required to find a solution. Summarizing, this paper shows that when GP is applied, specific considerations on how to select parameter values must be taken into account if the goal is to obtain solutions while searching for energy efficiency. Although the study has been performed using GP, we foresee that it could be similarly extended to EAs.
Visualization and Analysis Model of Industrial Economy Status and Development Based on Knowledge Graph and Deep Neural Network

This paper adopts knowledge mapping combined with a deep neural network algorithm to conduct in-depth research and analysis on the current situation and development of the industrial economy and designs a visual analysis model of economic development based on knowledge mapping combined with a deep neural network algorithm. Cultivate the concept of coordinated development and legal system of the subject, improve the awareness of network security and integrity self-discipline of the subject, improve the level of network hardware equipment manufacturing, improve the level of network platform construction, build a network security technology prevention system, improve the repair system of network information alienation, set up a specialized agency setting for the coordinated development of network ecology and industrial economy, and increase the capital investment in network infrastructure and network information technology research and development. A framework of breadth and depth recommendation ranking based on a knowledge graph is proposed and implemented. This paper provides a visual analysis method to sort and classify multivariate data. The method first determines users’ preferences through their interactive operations, calculates the weights of each attribute according to the users’ preference model, then uses the obtained attribute weight sets to sort the whole data set, and finally completes the category classification according to the sorting results and the users’ markings on some data. The visual display allows users to intuitively perform data sorting and classification operations and quickly understand the characteristics and category features of the data. The framework achieves modeling and integration of knowledge graph neighborhood information from breadth dimension and depth dimension to realize personalized recommendation sorting and improves the F1 metrics by 8.59%, 14.36%, and 15.22% on the public datasets Amazon-book, Yelp2018, and ILast-FM compared with the previous optimal model.
A Systematic Review of Applications and Tools Used in Virtual Reality and Augmented Reality

In this survey, examine applications of augmented reality and virtual reality in different types of real life applications, such as education, health care, medical, cultural experience, museums and social learning. VR and AR technologies provide immersive experiences to the user by using Head Mounted Display (HMD) and some tools and techniques. In this research, the authors also explore software and hardware components used by the researchers and identified most widely used software tools and hardware tools in AR and VR applications interfaces. This paper aims for the review of existing works in the domain of AR, VR applications, and different types of tools and techniques used by the researchers. Finally, according to the outcomes of the review paper, future aim of the research requirements and guidelines are presented.
Chessboard Corner Detection Based on EDLines Algorithm

To improve the robustness and accuracy of the corner-detection algorithm, this paper proposes a camera-calibration method based on the EDLines algorithm for the automatic detection of chessboard corners. The EDLines algorithm is initially used to perform straight-line detection on the calibration image. The features of the broken straight lines at the corners are then used to filter the straight lines and remove the background straight lines outside the chessboard. The pixels in the rectangular area around the filtered straight line are sorted by the gray gradient. After using the sorted results to fit the straight line, the coordinates of the intersection of the straight lines are taken as the initial coordinates of the corners and perform subpixel optimization on them. Finally, the corner points are sorted by the conversion between pixel-coordinate systems. The camera exposure time changes and complex imaging-background experiments show that the algorithm has no missed detection and redundancy in corner detection. The average reprojection error is found to be less than 0.05 pixels, which can be used in actual calibration.