Reviewerly/exHarmony huggingface dataset:
High-Order Graph Convolutional Network for Skeleton-Based Human Action Recognition

Skeleton-based action recognition plays an important role in the field of human action recognition. Recently, with the introduction of Graph Convolution Network (GCN), GCN has achieved superior performance in the field of skeleton-based human action recognition. In this work, we propose a high-order GCN model. In this model, we introduce the expression of high-order skeletons and establish a new high-order adjacency matrix. Through this matrix, the relationship between skeleton nodes and non-neighbor nodes has being established. In addition, based on the degree of node association of different hierarchical neighborhoods, the value of the matrix expresses the importance of different hierarchies. As a result, the proposed model extracts the co-occurrence feature of the skeleton which is superior to the local features and improves the recognition rate. We evaluate our model on two human skeleton action datasets, Kinetics-skeleton and NTU RGB+D, and then further explore the influence of skeleton nodes based on different hierarchies on the recognition results.
Super-Resolution of Sentinel-2 Images Based on Deep Channel-Attention Residual Network

Sentinel-2 data has become an important tool for current and future earth observation due to its high quality, free availability and world-wide coverage. However, some of the spectral bands are sensed at reduced resolution due to design considerations and sensor hardware limitations. So in this paper we present a super-resolution method based on Convolutional Neural Networks (CNNs) to infer all the 20m spectral bands in the highest available resolution. This is accomplished by using an improved residual network and meanwhile we propose a channel attention mechanism to adaptively rescale the characteristics of the channels by considering the interdependencies among the channels. The proposed solution compares against several alternative methods according to different quality indexes. Our network provides the best results and a compelling visual effect on the sentinel-2 images.
Designing for Multispecies Collaboration and Cohabitation

Human-nature interaction, joining the agendas of sustainable interaction design and nonanthropocentric HCI, takes up the challenges presented by climate change and environmental crisis. My dissertation focuses on studying sustainable farming practices through posthuman concepts to experiment ways of supporting multispecies collaborative works. Specifically, my research aims to discover and develop alternative design paradigms and practices for sustaining both human-nature collaboration, cohabitation, and co-creation.
Codit

As Blockchain-as-a-Service (BaaS) has witnessed a growing interest in enterprises, many BaaS providers have emerged. However, current BaaS providers can pose a potential security threat in the context of a centralized service provider and clients that depend on the provider.
Super-Resolution of Low-Quality Images Based on Compressed Sensing and Sequence Information

Image super-resolution (SR) plays a significant role in Internet of Vehicles (IoV), and is widely used in many important applications, such as object recognition and vehicle identification. However, in certain cases, the quality of acquired images is low and general SR algorithms are inapplicable. Aiming at the disadvantages of the traditional image super- resolution methods which are mainly based on interpolation and example learning, this paper utilizes compressed sensing (CS) and sequence information to present a new approach of image super-resolution. In order to keep high frequency information and reduce ringing and jagged artifacts, the proposed method takes advantage of the useful information between multiple image frames and jointly trains coupled dictionaries for the low- resolution (LR) and high-resolution (HR) image block pair. By fusing atoms of LR dictionary, the atom of HR dictionary is obtained and the HR image can be recovered in terms of the learned HR dictionary. The experimental results show that the proposed algorithm has better performance in both subjective assessment and objective standards, including Entropy and Average Gradient.
Effects of Team-based Problem-based Learning Combined with Smart Education: A Focus on High-risk Newborn Care

This study was conducted to examine the effects of team-based problem-based learning combined with smart education among nursing students.A quasi-experimental non-equivalent control group, pre-posttest design was used. The experimental group (n=36) received problem-based learning combined with smart education and lectures 7 times over the course of 7 weeks (100 minutes weekly). Control group (n=34) only received instructor-centered lectures 7 times over the course of 7 weeks (100 minutes weekly). Data were analyzed using the x2 test, the Fisher exact test, and the independent t-test with SPSS for Windows version 21.0.After the intervention, the experimental group reported increased learning motivation (t=2.70, p=.009), problem-solving ability (t=2.25, p=.028), academic self-efficacy (t=4.76, p<.001), self-learning ability (t=2.78, p<.001), and leadership (t=2.78, p=.007) relative to the control group.Team-based problem-based learning combined with smart education and lectures was found to be an effective approach for increasing the learning motivation, problem-solving ability, academic self-efficacy, self-learning ability, and leadership of nursing students.
Tight Integration of Rule-Based Tools in Game Development

In the wider perspective of narrowing down some of the gaps that prevent the adoption of declarative logic programming within highly dynamically changing environments, we focus in this paper on the context of integrating reasoning modules in real-time videogames. Integrating rule-based AI within the commercial game development life-cycle poses a number of unsolved challenges, each with non-obvious solution. For instance, it is necessary to cope with strict time performance requirements; the duality between procedural code and declarative specifications prevents easy integration; the concurrent execution of reasoning tasks and game updates requires proper information passing strategies between the two involved sides. In this work we illustrate our recent progress on how to embed rule-based reasoning modules into the well-known Unity game development engine. To this end, we report about ThinkEngine, a framework in which a tight integration of declarative formalisms within the typical game development workflow is made possible. We prove the viability of our approach by developing a proof-of-concept Unity game that makes use of ASP-based AI modules.
Incremental Layers Resection: A Novel Method to Compress Neural Networks

In recent years, deep neural networks (DNNs) have been widely applied in many areas, such as computer vision and pattern recognition. However, we observe that most of the DNNs include redundant layers. Hence, in this paper, we introduce a novel method named incremental layers resection (ILR) to resect the redundant layers in DNNs, while preserving their learning performances. ILR uses a multistage learning strategy to incrementally resect the inconsequential layers. In each stage, it preserves the data representations learned by the original network, while connecting the two nearby layers of each resected one. Particularly, based on a teacher-student knowledge transfer framework, we have designed the layer-level learning and overall learning procedures to enforce the resected network performing similarly with the original one. Extensive experiments demonstrate that, compared to the original networks, the compressed ones by ILR need only about half of the storage space and have higher inference speed. More importantly, they even deliver higher classification accuracy than the original networks.
Comparison of mathematical morphology with the local multifractal description applied to the image samples processing

This paper presents the results of a preliminary comparison of two methods which are based on the mathematical approach, Mathematical Morphology and the Local Multifractal Description. Both methods are characterized by the need for input parameters defining. Those parameters are engaged in the model development, allowing to study the objects presented on the images in respect to the intended purpose of the analysis. Due to the initial visual interpretation and image objects descriptors definition, both methods are considered as a semi-automatic. Tests were performed based on the selected Brodatz textures and artificially generated noisy images, aiming at the indication of strong and weak points of both methods while applied to the edge/object detection tasks.
CloneCompass: Visualizations for Exploring Assembly Code Clone Ecosystems

Assembly code analysis is an intensive process undertaken by security analysts and reverse engineers to discover vulnerabilities in existing software when source code is unavailable. Kam1n0 is an efficient code clone search engine that facilitates assembly code analysis. However, Kam1n0 search results can contain millions of function-clone pairs, and efficiently exploring and comprehensively understanding the resulting data can be challenging. This paper presents a design study whereby we collaborated with analyst stakeholders to identify requirements for a tool that visualizes and scales to millions of function-clone pairs. These requirements led to the design of an interactive visual tool, CloneCompass, consisting of novel TreeMap Matrix and Adjacency Matrix visualizations to aid in the exploration of assembly code clones extracted from Kam1n0. We conducted a preliminary evaluation with the analyst stakeholders and show how CloneCompass enables these users to visually and interactively explore code clone data generated from software systems with suspected vulnerabilities.
New Financial Technologies, Cryptocurrencies, Blockchain, and Challenges

As technological innovation transforms our economies, companies and start-ups all over the world are performing developments on financial technologies called “FinTech/fintech” for a chance to thrive. It even sparked the invention of blockchain and the inception of cryptocurrencies (digital/virtual money) such as Bitcoin. The blockchain technology provides Bitcoin's public ledger, an ordered and timestamped record of transactions. Blockchain is one of a kind decentralized technology mainly used by fintechs and it is a distributed as well as decentralized ledger that presents a radical, new, modern, and disruptive way of conducting all manner of transactions over the internet. Blockchain-based applications provide many opportunities to create a more sustainable world. With this research agenda, this chapter contributes to the discussion on future avenues for sustainability and information systems research on fintechs, especially cryptocurrencies and blockchain-based platforms and services.
Using voice assistant skills in family life

Voice assistants are increasingly prevalent in family life, being used, for example,for listening to music, finding out information, asking jokes and playing games together.However, little research shows how such technology influences dynamic family interactionsin the home over time. An in-the-wild study was conducted in six family homes over threeweeks. An Alexa, with a number of skills, was set up in each home for the families to use. Thefindings showed differences in use over time. To begin, family cohesion behavior and familyrituals were most prevalent. At the end of the study, the skills were found to motivate distinctfamily interaction patterns: including more collaboration to manage Alexa and scaffolding ofchildren’s interactions with Alexa, given developmental differences in users’ grasp of Alexa’scapabilities and limitations. We discuss how voice assistants support different interactionpatterns and potentially, offer different learning opportunities.
A Lite Asymmetric DenseNet for effective object detection based on convolutional neural networks (CNN)

Recently, convolutional neural networks (CNN) have been widely used in object detection and image recognition for their effectiveness. Many highly accurate classification models based on CNN have been developed for various machine learning applications, but they generally computationally costly and require a hardware-based platform with super computing power and memory resources to implement the algorithm. In order to accurately and efficiently achieve object detection tasks using CNN on a system with limited resources such as a mobile device, we propose an innovative type of DenseNet, which is a lightweight convolutional neural network algorithm called Lite Asymmetric DenseNet (LADenseNet). Aiming to compress the CNN model complexity, we replace the 7 x 7 convolution and 3 x 3 max-pool with multiple 3 x 3 convolutions and a 2 x 2 max-pool in the initial down-sampling process to significantly reduce the computing cost. In the design of the dense blocks, channel splitting and channel shuffling are employed to enhance the information exchange of feature maps and improve the expressive ability of the network. We decompose the 3 x 3 convolution in the dense block into a combination of 3 x 1 and 1 x 3 convolutions, which can speed up the computations and extract more spatial features by using asymmetric convolutions. To evaluate the performance of the proposed approach we develop an experimental system in which LA-DenseNet is used to extract features and Single Shot MultiBox Detector (SSD) is used to detect objects. With VOC2007+12 as training and testing datasets, our model achieves comparable detection accuracy as YOLOv2 with a fraction of its computational cost and memory usage.
4Kdump

This paper presents an exfiltration attack that targets systems with tight security measures; no connectivity and no ability to mount external devices. Our approach utilizes a mobile camera or a screen recorder to capture video while hexdump'ing a file using pre-installed utilities found in most operating systems. The captured video is analyzed at the attacker side using optical character recognition engines and the recognized letters are used to reconstruct the files. Our approach provides a 100% reconstruction rate given that console fonts can be perfectly recognized by OCR engines. We measure the exfiltration speed of our attack and show that we achieve up to 410 KBps when capturing video via mobile phones. We propose a set of defenses that can negate the attack while maintaining usability of the console.
Security System for Data Location of Travelling User using RSA based on Group Signature

Today, one of the popular products is mobile device because this device can be used anywhere and anytime. One of these mobile devices is a smartphone equipped with GPS services. In a condition, when a tour group is traveling sometimes there is one member missing. This paper proposes a solution by develope an application on a smartphone that can track the position of members by utilizing GPS technology. To maintain data security when sending GPS data, data is secured using RSA cryptography, IB-mRSA and group signatures so that only those members are able to access member GPS data. Group signatures is used to authenticate whether the lost members are part of the same group as other members or not. The Identity Based Mediated RSA algorithm (IB-mRSA) uses the ID of the member to do public key generation and private keys.
Bitrate and Transmission Resolution Determination Based on Perceptual Video Quality

As more multimedia data is delivered over networks, video data constitutes a significant amount of network traffic. On the other hand, consumers use a variety of displays to watch multimedia services delivered over networks. These devices include traditional TV monitors, smartphones, tablets and computer monitors. Consequently, screen sizes and viewing conditions (e.g., viewing distances, viewing environment illumination, etc.) may affect the perceptual video quality. Depending on the viewing environment and content characteristics, transmitting video data at high bitrates or high resolutions may not improve the perceptual video quality. In this paper, we investigate how various aspects of the viewing environment influence perceptual video quality and propose some methods to select bitrates and transmission resolutions based on perceptual video quality and viewing conditions for available bandwidth.
Development of Conversation Application as English Learning using WebRTC

The conversation is a communication activity consists of two or more people. The conversation is essentially the same as dialogue and can be used interchangeably. Face-to-face conversations are universal and involve human culture and interactive contexts. But there are flaws in the face-to-face conversation, namely the problem of distance and time. The development of technology is now growing so it is expected that new innovations to overcome these shortcomings as in learning are now beginning to be applied to distance learning systems or based online. One of them uses WebRTC technology in which there is a video conference feature making it easier for people to exchange ideas and information without the need to meet. Therefore, in this research, a conversation application will be developed as an English learning media using WebRTC technology, which also supports video conference features.
An Automated Face Retrieval System Using Grasshopper Optimization Algorithm-Based Feature Selection Method

Facial image retrieval using its contents is one of the major areas of research because of the exponential increase of multimedia data over the Internet. However, due to high dimensional features and different variations available in the images, it becomes a challenging task to obtain the relevant and non-redundant features. Therefore, for making the facial retrieval system more accurate and computationally efficient, the selection of prominent features is an important phase. In this paper, the grasshopper optimization algorithm has been used to obtain the relevant attributes from the high dimensional features vector. For the same, Oracle Research Laboratory database of faces is used. The experimental values show the efficacy of the proposed method of feature selection which eliminates the maximum 83% features among the other considered methods and the accuracy of the facial retrieval system also increases to 91.5%.
Digital native levels of Indonesia pre-service teachers

Abstract The generation classification based on technology proficiency skill consists of digital native and digital immigrant. Digital native assumed as superior in technology usage. Viewed from demographic aspects related to age, elementary school-age children of the current era tend to adopt the digital native characteristic. These characteristics may require teachers to integrate the learning methods and technology. This study aims to assess pre-service teachers’ digital native characters. They are projected to be teachers of digital native students. The assessment was made by using the Digital Native Assessment Scale developed by Teo in 2013. The use of the instrument was intended to pilot the DNAS in Indonesia education context. This study used a qualitative research design, since it involves qualitative data and analysis stages. The sample involved were 130 students (112 females and 18 males) of the Elementary School Preservice Teacher in Indonesia. Their age ranged from 18 to 24. Based on the study results, the digital native level of preservice teachers in Indonesia was high. If seen from a gender variable, the digital natives do not have any significant differences. Although this result cannot be fully generalized, it can be used as a reference both for curriculum development in teacher education.
P2PTV Traffic Classification and Its Characteristic Analysis Using Machine Learning

This paper proposes a classification method for peer-to-peer video streaming (P2PTV) traffic using machine learning. Since the user terminals (peers) share video data in P2PTV, P2PTV traffic is difficult to control and manage statically as both the number of peers sharing the same video data and the throughput vary with respect to contents. Although there exists a conventional method to classify and model P2PTV traffic by focusing on the number of peers and throughput, problems on the classification criteria and reproducibility remain in this method. In this paper, we use a clustering method that is considered as one of the machine learning methods and try to classify P2PTV traffic data into some categories. We extracted 18 features by analyzing P2PTV traffic of popular P2PTV applications: PPStream and PPTV; and then classified the traffic. The classification results show that about 400 traffic data sets were categorized into four clusters.
Soil Organic Matter Estimation Using Hyperspectral Remote Sensing Techniques in a Water-Level-Fluctuating Zone Around Guanting Reservoir, Beijing, China

The content of organic matter (OM) in soil has a unique spectral characteristic within the visible-near infrared wave range. We collected soil samples in the water-level-fluctuating zone (WLFZ) of Guanting Reservoir, Beijing. Field measured its spectra and collected in situ hyperspectral imaging data. Analysis of the characteristics of the spectrum waveform and wavelength variance showed that the influence of OM on soil reflectance was mainly within the spectral range of 400-1,358 nm. At wavelengths of 501 nm and 616 nm, the secondary differential spectrum of absorbance showed the highest correlation coefficient with OM, namely, 0.84 and -0.83, respectively. The validation result created with the PLSR (partial least squares regression) modeling technique showed that the second-order differential of absorbance could be used to effectively estimate the amount of OM. According to the best validation criterion, the optimal estimation model of the OM content was created with PLSR technique. Furthermore, by using hyperspectral imaging data, the hyperspectral inversion model was used to map soil OM content in a small-scale fluctuating region. The mapping result of soil OM was proved to be close to the measured data of OM samples, indicating that the in situ hyperspectral remote sensing imaging data was highly useful to accurately map soil OM content in the WLFZ.
Occluded Face Detection, Face in Niqab Dataset

The detection of faces with occlusion is one of the remaining challenges in the field of computer vision. Despite the high performance of current advanced face detection algorithms, occluded faces are under hot research and is still requires more investigation. There is a need of rich dataset for occluded faces to be used for enriching the training of cnn-deep learning models in order to boost the performance of face detection for occluded faces as in face in niqab. In this paper a dataset of occluded faces named faces in niqab is proposed. An experimental results indicated the poor performance of state-of-the-art face detection algorithms when tested in our dataset.
Intermediate deep-feature compression for multitasking

Collaborative intelligence is a new strategy to deploy deep neural network model for AI-based mobile devices, which runs a part of model on the mobile to extract features, the rest part in the cloud. In such case, feature data but not the raw image needs to be transmitted to cloud, and the features uploaded to cloud need have generalization capability to complete multitask. To this end, we design an encoder-decoder network to get intermediate deep features of image, and propose a method to make the features complete different tasks. Finally, we use a lossy compression method for intermediate deep features to improve transmission efficiency. Experimental results show that the features extracted by our network can complete input reconstruction and object detection simultaneously. Besides, with the deep-feature compression method proposed in our work, the quality of reconstructed image is good in visual and index of quantitative assessment, and object detection also has a good result in accuracy.