Reviewerly/exHarmony huggingface dataset:
The Relation of the Perceptions of Aesthetics and Usability

Aesthetics and usability both play critical roles in product design. But how might measurement of these two conceptually-different features of products interfere with one another? The current research study examines the effect of differences in aesthetics on perceived usability. Participants completed three tasks on a simulated website with a low usability interface. One group of participants used an interface with high aesthetics, whereas a second group interacted with an interface with poor aesthetics. Both groups rated the usability and aesthetics of the interface after completing the tasks. The aesthetics manipulation was effective in that the high aesthetics group provided higher ratings on two aesthetics scales than did the low aesthetics group; however, differences in aesthetics had no significant effect on usability as measured by the System Usability Scale (SUS). These findings support the idea that users make independent judgments of usability and aesthetics.
Towards a Lightweight Continuous Authentication Protocol for Device-to-Device Communication

Continuous Authentication (CA) has been proposed as a potential solution to counter complex cybersecurity attacks that exploit conventional static authentication mechanisms that authenticate users only at an ingress point. However, widely researched human user characteristics-based CA mechanisms cannot be extended to continuously authenticate Internet of Things (IoT) devices. The challenges are exacerbated with the increased adoption of device-to-device (d2d) communication in critical infrastructures. Existing d2d authentication protocols proposed in the literature are either prone to subversion or are computationally infeasible to be deployed on constrained IoT devices. In view of these challenges, we propose a novel, lightweight and secure CA protocol that leverages communication channel properties and a tunable mathematical function to generate dynamically changing session keys. Our preliminary informal protocol analysis suggests that the proposed protocol is resistant to known attack vectors and thus has strong potential for deployment in securing critical and resource-constrained d2d communication.
Performance Evaluation of Distributed Training in Tensorflow 2

Deep learning (DL) is most interested in Artificial Intelligence (AI) because it brings many interesting results in object identification, natural language processing, speech processing, application algorithms in autonomous vehicles, etc. DL also use neutron network. A useful neural architecture for an application may have tens or hundreds of millions of numbers that need to be selected appropriately during the training process. The most popular technique for training this architecture is a gradient-based iteration technique. To complete the training process, it needs to iterate over large amounts of data, which is why the training process is so slow. Without high performance computing (HPC) technology, it will take weeks or months to complete the training of network architecture. Training DL by distributed training is one of the solutions to improve the learning process faster and reduce computing time. In this paper, we study performance evaluation for distributed training strategy of the Tensorflow 2.2 on some GPUs.
MTQA: Text-Based Multitype Question and Answer Reading Comprehension Model

Text-based multitype question answering is one of the research hotspots in the field of reading comprehension models. Multitype reading comprehension models have the characteristics of shorter time to propose, complex components of relevant corpus, and greater difficulty in model construction. There are relatively few research works in this field. Therefore, it is urgent to improve the model performance. In this paper, a text-based multitype question and answer reading comprehension model (MTQA) is proposed. The model is based on a multilayer transformer encoding and decoding structure. In the decoding structure, the headers of the answer type prediction decoding, fragment decoding, arithmetic decoding, counting decoding, and negation are added for the characteristics of multiple types of corpora. Meanwhile, high-performance ELECTRA checkpoints are employed, and secondary pretraining based on these checkpoints and an absolute loss function are designed to improve the model performance. The experimental results show that the performance of the proposed model on the DROP and QUOREF corpora is better than the best results of the current existing models, which proves that the proposed MTQA model has high feature extraction and relatively strong generalization capabilities.
Quantum Annealing Approach: Feature Extraction and Segmentation of Synthetic Aperture Radar Image

The Markov Random Field (MRF) is used for extracting feature information in images and is formed as an Ising-like model. The quantum annealing is a novel method to optimize objective functions, and objective functions have to be expressed in terms of the Ising model. Hence, the MRF can be embedded into the the quantum annealing method, and feature information of remote sensing images then can be extracted using a quantum annealing computer. Extracted information or features are used to segment an image.
What’s limbs got to do with it? Real-world movement correlates with feelings of ownership over virtual arms during object interactions in virtual reality

Abstract Humans will initially move awkwardly so that the end-state of their movement is comfortable. But, what is comfortable? We might assume it refers to a particular physical body posture, however, humans have been shown to move a computer cursor on a screen with an out-of-sight hand less efficiently (curved) such that the visual representation appears more efficient (straight). This suggests that movement plans are made in large part to satisfy the demands of their visual appearance, rather than their physical movement properties. So, what determines if a body movement is comfortable—how it feels or how it looks? We translated an object-interaction task from the real-world into immersive virtual reality (IVR) to dissociate a movement from its visual appearance. Participants completed at least 20 trials in two conditions: Controllers—where participants saw a visual representation of the hand-held controllers and Arms—where they saw a set of virtual limbs. We found participants seeing virtual limbs moved in a less biomechanically efficient manner to make the limbs look similar to if they were interacting with a real-world object. These movement changes correlated with an increase in self-reported feelings of ownership over the limbs as compared to the controllers. Overall this suggests we plan our movements to provide optimal visual feedback, even at the cost of being less efficient. Moreover, we speculate that a detailed measurement of how people move in IVR may provide a new tool for assessing their degree of embodiment. There is something about seeing a set of limbs in front of you, doing your actions, that affects your moving, and in essence, your thinking.
Benchmarking in Cluster Analysis: A Study on Spectral Clustering, DBSCAN, and K-Means

We perform a benchmarking study to identify the advantages and the drawbacks of Spectral Clustering and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). We compare the two methods with the classic K-means clustering. The methods are performed on five simulated and three real data sets. The obtained clustering results are compared using external and internal indices, as well as run times. Although there is not one method that performs best on all types of data sets, we find that DBSCAN should generally be reserved for non-convex data with well-separated clusters or for data with many outliers. Spectral Clustering has better overall performance but with higher instability of the results compared to K-means, and longer run time.
Comparative Analysis of Various Simulation Tools Used in a Cloud Environment for Task-Resource Mapping

In a cloud environment, the IaaS model comprises of successful execution of a wide range of client applications dependent on resource planning. Broad research on all issues identified with cloud computing in a physical world is irksome on the grounds that it anticipates that researchers should think about the system structure and the conditions related with it, which might be uncontrollable. Essentially, the condition of the framework can’t be assessed or even overseen. Moreover, determining the performance of service models at various loads and assessing the cloud user’s methodologies under various conditions is annoying. These issues can be resolved by creating virtual cloud test systems. In the present paper, six cloud simulators are audited to create and test different cloud applications, with a view to utilize them in future. This paper gives a thought about different cloud simulators and compared them dependent on different parameters, for example, programming languages, availability and SLA support, and so forth. Finally, based on analysis, it is concluded that cloudsim is an effective and efficient simulator among all and can be used for further research and explorations.
Perceptual-Similarity-Aware Deep Speaker Representation Learning for Multi-Speaker Generative Modeling

We propose novel deep speaker representation learning that considers perceptual similarity among speakers for multi-speaker generative modeling. Following its success in accurate discriminative modeling of speaker individuality, knowledge of deep speaker representation learning (i.e., speaker representation learning using deep neural networks) has been introduced to multi-speaker generative modeling. However, the conventional discriminative algorithm does not necessarily learn speaker embeddings suitable for such generative modeling, which may result in lower quality and less controllability of synthetic speech. We propose three representation learning algorithms that utilize a perceptual speaker similarity matrix obtained by large-scale perceptual scoring of speaker-pair similarity. The algorithms train a speaker encoder to learn speaker embeddings with three different representations of the matrix: a set of vectors, the Gram matrix, and a graph. Furthermore, we propose an active learning algorithm that iterates the perceptual scoring and speaker encoder training. To obtain accurate embeddings while reducing costs of scoring and training, the algorithm selects unscored speaker-pairs to be scored next on the basis of the sequentially-trained speaker encoder's similarity prediction results. Experimental evaluation results show that 1) the proposed representation learning algorithms learn speaker embeddings strongly correlated with perceptual speaker-pair similarity, 2) the embeddings improve synthetic speech quality in speech autoencoding tasks better than conventional d-vectors learned by discriminative modeling, 3) the proposed active learning algorithm achieves higher synthetic speech quality while reducing costs of scoring and training, and 4) among the proposed similarity {vector, matrix, graph} embedding algorithms, the first achieves the best speaker similarity for synthetic speech and the third gives the most improvement in the synthetic speech naturalness.
Exploring Tangible Algorithmic Imaginaries in Movie Recommendations

Recommender algorithms play an active role in many everyday activities. However, personalized recommendations often produce negative experiences due to a lack of awareness, control, or transparency. Allowing users to materialize their algorithmic imaginaries exposes how they experience, perceive, and imagine recommender algorithms. Moreover, it can unearth novel and previously unattended design opportunities for tangible interactions with algorithms. Therefore, we explored how 15 users of a famous movie recommender system materialized tangible designs to reflect and discuss their algorithmic imaginaries during co-design workshops and interviews. Using thematic analysis, we identified two forms of algorithmic imaginaries that can inspire tangible interactions with recommender algorithms: metaphoric and datafied representations. Complementary themes exposed the influence of contextual factors and diverse negative attitudes towards personalized movie recommendations. Based on these findings, we suggest design opportunities and suggestions for improving the algorithmic experience of movie recommendations and similar systems through tangible user interfaces.
Research on the Integration Method of Ideological and Political Course Resources Based on Mobile Learning

The following topics are dealt with: feature extraction; structural engineering computing; roads; bridges (structures); fuzzy set theory; data mining; concrete; geotechnical engineering; Internet; neural nets.
Experimental Demonstration of Optoelectronic Equalization for Short-reach Transmission with Reservoir Computing

A receiver with shared complexity between optical and digital domains is experimentally demonstrated. Reservoir computing is used to equalize up to 4 directly-detected optically filtered spectral slices of a 32 GBd OOK signal over up to 80 km of SMF.
A Profile Matching Scheme based on Private Set Intersection for Cyber-Physical-Social Systems

The cyber-physical-social system (CPSS) is a three-layer system framework that combines the human society on the basis of the cyber-physical system (CPS), so that the human society, the cyber world and the physical world are interconnected. In the CPSS, similar profile attributes are matched to socialize and ultimately achieve the purpose of information sharing. However, some personal information may be included in the profile attributes, thus the users' privacy cannot be protected during the process. To meet this challenge, a privacy-preserving profile matching scheme based on private set intersection is proposed in this paper. Multi-tag is utilized to partition the dataset of users to achieve fine-grained profile matching. In addition, the privacy of users is protected by re-encryption technique. Security analysis shows that our scheme is secure against the semi-honest adversary and theoretical analysis of the experiment shows that that the scheme is efficient for profile matching in the CPSS.
Utilization of ICT-Based Learning Media in Local History Learning

Abstract This research aims to understand how students’ perceptions and lecturers regarding the use of ICT in learning Local History at the Historical Education Study Program of the Faculty of Social Sciences, Makassar State University. This research uses a qualitative approach with a case study design. Data collection techniques carried out, including observations, interviews, documentation and literature. Data analysis was carried out using the model developed by Mile and Huberman, which is collecting data, data reduction, data display, drawing conclusions. Local history learning is considered not able to have a positive impact on both cognitive and affective aspects of students. This happens because the learning media used do not present learning that is fun and seems conventional. From the results of the research concluded that using ICT-based learning media can help students to understand the material of the Gowa Kingdom’s resistance to the VOC properly and chronologically.
A Systematic Review of Human Factors Literature About Voice User Interfaces and Older Adults

We conducted a systematic literature review of the human factors literature at the intersection of voice user interfaces (VUI) and older adults among Human Factors publications. Our review was limited to research published in the past 50 years (1970 – 2020) in either the journal Human Factors or the Proceedings of the Human Factors and Ergonomics Society. While we included a broad array of search terms related to VUIs, we found very few articles about VUIs that were specifically focused on designing for older adults or used older adults as participants in studies. Of the 26 human factors publications we did find that were related to this topic, most found older adults take more time to operate VUIs and/or made more errors than younger adults, whereas a minority of publications found no age-related differences. We concluded that age-related differences in the use of VUIs are likely task specific.
Study of Effective Mining Algorithms for Frequent Itemsets

“Frequent Itemset Mining” is a domain where several techniques have been proposed in recent years. The most common techniques are tree-based, list-based, or hybrid approaches. Although each of these approaches was proposed with the intent of mining frequent itemsets efficiently, as the number of transactions increases, the performance of most of these algorithms gradually declines either in terms of time or memory. In addition, the presence of redundant itemsets is another crucial problem where a limited investigation has been carried out in recent years. There is thus a pressing need to develop more efficient algorithms that will address each of these concerns. This paper aims to survey the different approaches highlighting the advantages and disadvantages of each of them so that in future effective algorithms may be designed for extracting frequent items while addressing each of these concerns effectively.
A cascade-network framework for integrated registration of liver DCE-MR images

Registration of hepatic dynamic contrast-enhanced magnetic resonance images (DCE-MRIs) is an important task for evaluation of transarterial chemoembolization (TACE) or radiofrequency ablation by quantifying enhancing viable residue tumor against necrosis. However, intensity changes due to contrast agents combined with spatial deformations render technical challenges for accurate registration of DCE-MRI, and traditional deformable registration methods using mutual information are often computationally intensive in order to tolerate such intensity enhancement and shape deformation variability. To address this problem, we propose a cascade network framework composed of a de-enhancement network (DE-Net) and a registration network (Reg-Net) to first remove contrast enhancement effects and then register the liver images in different phases. In experiments, we used DCE-MRI series of 97 patients from Renji Hospital of Shanghai Jiaotong University and registered the arterial phase and the portal venous phase images onto the pre-contrast phases. The performance of the cascade network framework was compared with that of the traditional registration method SyN in the ANTs toolkit and Reg-Net without DE-Net. The results showed that the proposed method achieved comparable registration performance with SyN but significantly improved the efficiency.
Anchors vs Attention: Comparing XAI on a Real-Life Use Case

Recent advances in eXplainable Artificial Intelligence (XAI) led to many different methods in order to improve explainability of deep learning algorithms. With many options at hand, and maybe the need to adapt existing ones to new problems, one may find in a struggle to choose the right method to generate explanations. This paper presents an objective approach to compare two different existing XAI methods. These methods are applied to a use case from literature and to a real use case of a French administration.
Unreliable-to-Reliable Instance Translation for Semi-Supervised Pedestrian Detection

Generating realistic pedestrian instances in a semi-supervised setting is promising but challenging due to the limited labeled data. We propose an unreliable-to-reliable instance translation model (Un2Reliab) conditioned on unreliable instances which poorly align with pedestrians. Un2Reliab mainly consists of an encoder-decoder-like generative network and a discriminative network, which are jointly trained in a minimax game. We adopt regularization to ensure that the synthesized instances are semantically similar to the corresponding ground truth. Furthermore, to preserve the identities of persons, we propose another regularization to ensure that the synthesized instances associated with the same person should be consistent in appearance. As a result, Un2Reliab learns to restore the missing parts of the original instances. As a side benefit, the synthesized instances are brought into better alignment. Inclusion of the synthesized data improves both the diversity and quality of training data, which eventually leads to better generalization performance. Extensive experiments indicate that Un2Reliab is able to synthesize high-fidelity pedestrian instances and improve the previous state-of-the-art results on multiple semi-supervised pedestrian detection benchmarks.
Restructuring of Hoeffding Trees for Trapezoidal Data Streams

Trapezoidal Data Streams are an emerging topic, where not only the data volume increases, but also the data dimension, i.e. new features emerge. In this paper, we address the challenges that arise from this problem by providing a novel approach to restructure and prune Hoeffding trees. We evaluate our approach on synthetic datasets, where we can show that the approach significantly improves the performance compared to the baseline of an adjusted Hoeffding tree algorithm without restructuring and pruning.
Scaling Up a Multispectral Resnet-50 to 128 GPUs

Similarly to other scientific domains, Deep Learning (DL) holds great promises to fulfil the challenging needs of Remote Sensing (RS) applications. However, the increase in volume, variety and complexity of acquisitions that are carried out on a daily basis by Earth Observation (EO) missions generates new processing and storage challenges within operational processing pipelines. The aim of this work is to show that High-Performance Computing (HPC) systems can speed up the training time of Convolutional Neural Networks (CNNs). Particular attention is put on the monitoring of the classification accuracy that usually degrades when using large batch sizes. The experimental results of this work show that the training of the model scales up to a batch size of 8,000, obtaining classification performances in terms of accuracy in line with those using smaller batch sizes.
U-High resolution network (U-HRNet): cloud detection with high-resolution representations for geostationary satellite imagery

(2021). U-High resolution network (U-HRNet): cloud detection with high-resolution representations for geostationary satellite imagery. International Journal of Remote Sensing: Vol. 42, No. 9, pp. 3511-3533.
A Method Aware of Concept Drift for Online Botnet Detection

Botnets deeply threaten cybersecurity due to their distributed and dynamic nature, causing attacks with severe consequences for users and companies, such as Distributed Denial of Service. Detecting botnets is challenging once they constantly evolve, resulting in fast behavior changes in network. Current techniques usually detect botnets without considering these changes and their fast adaptation to new behavior. Hence, this paper presents CONFRONT, a method aware of concept drift (fast changes in network behavior) for online botnet detection. Different from the literature, this paper introduces a new technique to detect concept drift and optimize botnet classification. CONFRONT employs features from network flow on the unsupervised concept drift detector and a supervised incremental botnet classifier. Results show CONFRONT feasibility, reaching 95% of accuracy in less than 1 ms.