Reviewerly/exHarmony huggingface dataset:
Algorithm-based fault tolerance for discrete wavelet transform implemented on GPUs

An important application for discrete wavelet transform (DWT) is the image compression such as the JPEG2000 Standard, and GPU is a good choice for the implementation of the DWT. For the application of image compression in the space environment, this paper has discussed the fault tolerance for the forward DWT (FDWT) implemented on GPUs, to deal with the silent data corruption (SDC) errors caused by single event upsets (SEUs). This paper has analyzed the matrix descriptions for the procedures of the forward DWT in the JPEG2000 Standard. Using the matrix descriptions, the propagation of SDC errors in the multi-level forward DWT has been analyzed, and an algorithm-based fault tolerance (ABFT) method has been proposed, in addition, by analyzing the relationship between the data type and the SDC errors, an on-line fast error detection and correction method has been proposed to suppress the propagation of the logical value overflow errors. Finally, the performance of the program with ABFT has been evaluated and the overhead of memory space caused by the checksum data has been further analyzed.
STR-SA: Session-based Thread Recommendation for Online Course Forum with Self-Attention

In recent years, along with the rapid development of Massive Open Online Courses (MOOCs), a large number of students participate in MOOC courses. MOOC course forum is very important for students discussing questions related to online courses when they don’t have enough offline resources to get help from. There are a lot of qualified discussion threads in MOOC courses which are helpful for understanding the course materials. However, students are always overwhelmed by the enormous amount of threads. A solution to alleviate this problem is to recommend a list of threads for each student that she/he may be interested in. Existing thread recommender systems are insufficient to capture the complex relationships among the threads in a student’s visit session, and do not simultaneously take into account the student’s global preference, as well as her/his current interest in that session. In this work, we propose a novel neural network framework for session-based thread recommendation (STR-SA for short). The proposed method, which recommends threads to a student based on the threads viewed by the student in the current session, learns the relationships among threads by applying self-attention mechanism. Furthermore, we capture the global preference of the student by combining the viewed threads, and consider the latest-viewed thread as the current interest of the student. Extensive experiments conducted on three course-forum datasets show that the proposed model STR-SA significantly outperforms other representative methods for MOOC thread recommendation.
Inclusion of End User Playback-Related Interactions in YouTube Video Data Collection and ML-Based Performance Model Training

Solutions relying on machine learning (ML) models that address the challenge of in-network QoE estimation for HTTP adaptive video streaming often neglect user behavior and its impact on performance estimation. End user playback-related interactions impact network traffic characteristics, thus having a (predominantly negative) impact on the performance of models that estimate Key Performance Indicators (KPIs) from encrypted traffic. The biggest challenge in incorporating user interactions when training and testing ML models lies in the wide range of different potential interactions, multiple interaction occurrences, various combinations of different interactions, and different time points of execution spanning across a video streaming session. With the aim of training models applicable for deployment in real networks, but also in an effort to optimize the overall process of model training, we systematically investigate the relationship between classification accuracy of models trained on data with and without certain user interactions. Our results for YouTube videos, played using the native YouTube app on a mobile device under emulated broadband network conditions, show that the impact of interactions on model performance highly depends on the target KPI being classified. In certain cases, the model training process may be simplified by reducing the need to consider a wide range of interaction scenarios.
Optimizing OpenCL-Based CNN Design on FPGA with Comprehensive Design Space Exploration and Collaborative Performance Modeling

Recent success in applying convolutional neural networks (CNNs) to object detection and classification has sparked great interest in accelerating CNNs using hardware-like field-programmable gate arrays (FPGAs). However, finding an efficient FPGA design for a given CNN model and FPGA board is not trivial since a strong background in hardware design and detailed knowledge of the target board are required. In this work, we try to solve this problem by design space exploration with a collaborative framework. Our framework consists of three main parts: FPGA design generation, coarse-grained modeling, and fine-grained modeling. In the FPGA design generation, we propose a novel data structure, LoopTree, to capture the details of the FPGA design for CNN applications without writing down the source code. Different LoopTrees, which indicate different FPGA designs, are automatically generated in this process. A coarse-grained model will evaluate LoopTrees at the operation level, e.g., add, mult, and so on, so that the most efficient LoopTrees can be selected. A fine-grained model, which is based on the source code, will then refine the selected design in a cycle-accurate manner. A set of comprehensive OpenCL-based designs have been implemented on board to verify our framework. An average estimation error of 8.87% and 4.8% has been observed for our coarse-grained model and fine-grained model, respectively. This is much lower than the prevalent operation-statistics-based estimation, which is obtained according to a predefined formula for specific loop schedules.
Analogical Image Translation for Fog Generation

Image-to-image translation is to map images from a given style to another given style. While exceptionally successful, current methods assume the availability of training images in both source and target domains, which does not always hold in practice. Inspired by humans' reasoning capability of analogy, we propose analogical image translation (AIT) that exploit the concept of gist, for the first time. Given images of two styles in the source domain: A and A', along with images B of the first style in the target domain, learn a model to translate B to B' in the target domain, such that A:A' :: B:B'. AIT is especially useful for translation scenarios in which training data of one style is hard to obtain but training data of the same two styles in another domain is available. For instance, in the case from normal conditions to extreme, rare conditions, obtaining real training images for the latter case is challenging. However, obtaining synthetic data for both cases is relatively easy. In this work, we aim at adding adverse weather effects, more specifically fog, to images taken in clear weather. To circumvent the challenge of collecting real foggy images, AIT learns the gist of translating synthetic clear-weather to foggy images, followed by adding fog effects onto real clear-weather images, without ever seeing any real foggy image. AIT achieves zero-shot image translation capability, whose effectiveness and benefit are demonstrated by the downstream task of semantic foggy scene understanding.
Making without fabrication: Do-it-yourself activities for IT security in an open lab

Do-it-yourself activities of hackers or makers are accompanied by fundamental claims about the sovereignty of users in their treatment of technology. In Fab Labs and Maker Spaces, such claims are realised by giving users access to hardware for the fabrication and alteration of artefacts. In open laboratories where no such hardware is available, users establish sovereignty in other ways as they step beyond the design and construction of artefacts and get involved in a wider scope of sense-making and agenda-setting behaviours. Using the example of an open lab project on IT security for critical infrastructures, this paper tries to gain a better understanding of these behaviours. Due to the sensitivity of IT security, visitors of the lab are not allowed to get directly involved in the fabrication or alteration of technical architectures, algorithms, etc. Nevertheless, the visitors engage in a variety of other do-it-yourself activities in their approach to the subject matter and the project itself that add new facets to the notion of user sovereignty in hacking and making.
Video Description Model Based on Temporal-Spatial and Channel Multi-Attention Mechanisms

Video description plays an important role in the field of intelligent imaging technology. Attention perception mechanisms are extensively applied in video description models based on deep learning. Most existing models use a temporal-spatial attention mechanism to enhance the accuracy of models. Temporal attention mechanisms can obtain the global features of a video, whereas spatial attention mechanisms obtain local features. Nevertheless, because each channel of the convolutional neural network (CNN) feature maps has certain spatial semantic information, it is insufficient to merely divide the CNN features into regions and then apply a spatial attention mechanism. In this paper, we propose a temporal-spatial and channel attention mechanism that enables the model to take advantage of various video features and ensures the consistency of visual features between sentence descriptions to enhance the effect of the model. Meanwhile, in order to prove the effectiveness of the attention mechanism, this paper proposes a video visualization model based on the video description. Experimental results show that, our model has achieved good performance on the Microsoft Video Description (MSVD) dataset and a certain improvement on the Microsoft Research-Video to Text (MSR-VTT) dataset.
Modified color CCD moiré method and its application in optical distortion correction

The characterization and correction of lens distortion is an important part of optical measurement. This work analyzes the formation mechanism of color fringes and multiplication moiré, and discusses modification of the color CCD moiré method for correcting lens distortion. A demosaicing algorithm was used to simulate the color fringes in a CCD moiré pattern. We then performed a detailed analysis of the carrier field, and adopted a universal polynomial distortion model to characterize lens distortion, which increased the accuracy of distortion correction. Comparison experiments verified the feasibility of the modified method and the correctness of the formation mechanism for color CCD moiré. The modified method was then applied to lens distortion correction in the DIC measurement process, effectively reducing errors in displacement measurement.
Cross Data Set Performance Consistency of Objective Quality Assessment Methods for Light Fields

With the emergence of light field (LF) imaging technology, various challenging problems for LF visualization, compression, and transmission have to be addressed. Measuring perceptual quality is of utmost importance to assess the impact of an LF processing step on the visual experience of the finally rendered content to the end-user. In particular, objective quality assessment (QA) plays a key role in the quality optimization of LF imaging systems. In this paper, we conducted a comprehensive experiment to evaluate the performance of different objective QA methods for LF application. To this end, we selected a total number of 250 LFs (more than 48000 perspective views) from three public data sets to evaluate 16 objective QA metrics. Moreover, the subjective scores from three test data sets were aligned to produce an integrated data set for quality evaluation. The performance results across the different data sets aid to choose the most reliable metrics that are consistently performing well under various distortion and content characteristic conditions.
On Sharpness of Error Bounds for Univariate Approximation by Single Hidden Layer Feedforward Neural Networks

Abstract A new non-linear variant of a quantitative extension of the uniform boundedness principle is used to show sharpness of error bounds for univariate approximation by sums of sigmoid and ReLU functions. Single hidden layer feedforward neural networks with one input node perform such operations. Errors of best approximation can be expressed using moduli of smoothness of the function to be approximated (i.e., to be learned). In this context, the quantitative extension of the uniform boundedness principle indeed allows to construct counterexamples that show approximation rates to be best possible. Approximation errors do not belong to the little-o class of given bounds. By choosing piecewise linear activation functions, the discussed problem becomes free knot spline approximation. Results of the present paper also hold for non-polynomial (and not piecewise defined) activation functions like inverse tangent. Based on Vapnik–Chervonenkis dimension, first results are shown for the logistic function.
Stabilization of body balance with Light Touch following a mechanical perturbation: Adaption of sway and disruption of right posterior parietal cortex by cTBS

Light touch with an earth-fixed reference point improves balance during quite standing. In our current study, we implemented a paradigm to assess the effects of disrupting the right posterior parietal cortex on dynamic stabilization of body sway with and without Light Touch after a graded, unpredictable mechanical perturbation. We hypothesized that the benefit of Light Touch would be amplified in the more dynamic context of an external perturbation, reducing body sway and muscle activations before, at and after a perturbation. Furthermore, we expected sway stabilization would be impaired following disruption of the right Posterior Parietal Cortex as a result of increased postural stiffness. Thirteen young adults stood blindfolded in Tandem-Romberg stance on a force plate and were required either to keep light fingertip contact to an earth-fixed reference point or to stand without fingertip contact. During every trial, a robotic arm pushed a participant's right shoulder in medio-lateral direction. The testing consisted of 4 blocks before TMS stimulation and 8 blocks after, which alternated between Light Touch and No Touch conditions. In summary, we found a strong effect of Light Touch, which resulted in improved stability following a perturbation. Light Touch decreased the immediate sway response, steady state sway following re-stabilization, as well as muscle activity of the Tibialis Anterior. Furthermore, we saw gradual decrease of muscle activity over time, which indicates an adaptive process following exposure to repetitive trials of perturbations. We were not able to confirm our hypothesis that disruption of the rPPC leads to increased postural stiffness. However, after disruption of the rPPC, muscle activity of the Tibialis Anterior is decreased more compared to sham. We conclude that rPPC disruption enhanced the intra-session adaptation to the disturbing effects of the perturbation.
The Effect of Quality Management and Big Data Management on Customer Satisfaction in Korea’s Public Sector

Data-driven decision making is needed in uncertain situations. Total quality management is the source of quality management activities and customer satisfaction. This study is related to the investigation into the application of total quality management based on big data management on the public sector in Korea. We developed a research model for total quality management, and investigated the role of moderating big data management between total quality management leadership and quality management. Moreover, this study has examined the relationships between the practices of the total quality management and using big data, including customer needs and wants. The research model is developed and tested to fit it is with the SEM (Structural Equation Model) analysis using data 250 samples in Korea’s public sector. The survey was conducted between 1 August and 30 August 2019. The results are as follow: Total quality leadership has significantly impacted total quality management. Customer satisfaction was found to be significantly affected by total quality management activities. In addition, the level of big data management has the moderation effect between total quality leadership and total quality management in Korea public sectors. It is necessary to systematically manage data management in a situation where the demand for improvement of public service is gradually increasing online in public sector.
Generalized type-2 fuzzy logic in galactic swarm optimization: design of an optimal ball and beam fuzzy controller

In this paper we present a modification based on generalized type-2 fuzzy logic to an algorithm that is inspired on the movement of large masses of stars and their attractive force in the universe, known as galactic swarm optimization (GSO). The modification consists on the dynamic adjustment of parameters in GSO using type-1 and type-2 fuzzy logic. The main idea of the proposed approach is the application of fuzzy systems to dynamically adapt the parameters of the GSO algorithm, which is then applied to parameter optimization of the membership functions of the bar and ball fuzzy controller. The experimentation was carried out using the original GSO algorithm, and the type-1 and type-2 fuzzy variants of GSO. In addition a disturbance was added to the bar and ball fuzzy controller plant to be able to validate the effectiveness of the proposed approach in optimizing fuzzy controllers. A formal comparison of results is performed with statistical tests showing that GSO with generalized type-2 fuzzy logic is the best method for optimizing the fuzzy controller.
Ivy

In this paper, we present and discuss Ivy, a critical artifact offering a novel design perspective on interventions that aim to reduce sedentary behavior in office workers. Ivy is an interactive office chair that represents the amount of sitting time through growing ivy strands. Using the matrix of common argument types by Bardzell et al., we propose a structured "reading" of Ivy, as an example supporting reasoned and accessible conversations about criticality in design. Our reading of Ivy emphasized that its criticality emerges mainly from data physicalization as a new form of interactivity intended to trigger reflectiveness. The insights of this design study contribute towards a critical perspective on designing interventions to reduce sedentary time and spark discussion amongst designers and researchers in the field of Human-Computer Interaction.
Disconnecting

Data collection from users' interactions with ubiquitous computing is a wicked problem for design. Despite legislation increasingly requiring "informed" consent, the apathy born from a constant bombardment of consent notifications means many users remain largely uninformed about the collection, manipulation and dissemination of their personal information. We explore existing semiotics as signposts that inform individual decisions, and report from a design activity where participants mapped out their daily personal data trails. Using these as inspiration we explore how to engage users with new or emergent data trails. This pictorial provides a visual argument for (1) engaging users with whole data trails, which may yield greater benefit than the optimisation of individual notifications, and (2) how a whole data trail perspective may be valuable in the development of semiotic conventions for acquainting users with emergent (future) data trails.
Energy Efficiency for Cloud Computing Applications: A Survey on the Recent Trends and Future Scopes

One of the upcoming technologies is ‘Cloud Computing’ which has seen enormous growth in the recent years with an aim to deliver computing as a utility. Fulfilling this technological demand is not an easy task. Huge data centers consisting large volume of data, large number of servers and various other auxiliary hard ware constitutes the infrastructure needed to support Cloud Computing. The datacenters are set up on a large-scale consisting of many computing nodes and use a tremendous quantity of electrical power which led to higher operational outlays. This increase in energy consumption, demands to rethink the energy efficiency of cloud infrastructures. Nevertheless, we need to analyze the current status prior to changing anything to the infrastructure. One of the key research issues that the cloud computing researchers have been trying to resolve is the reduction of the carbon emission by the data centers. This paper discusses the modern systems, which are developed for the improvement of energy efficiency in case of large scale cloud computing infrastructures while not compromising the quality of services and performance, by investigating all possible areas in the cloud infrastructure responsible for the considerable quantity of energy consumption. A comparative analysis of different survey papers published in recent times is carried out and their key contributions are also reported. It also discusses the various tools that are employed for the designing of energy-efficient schemes. Based on the recent trends of energy-efficient algorithms, futuristic scope in the domain is also provided for the cloud computing research community.
Beyond Human-Centered Design

In recent years, HCI design researchers increasingly involve plants in their interaction design research. Presently, there is no regulation of research with these vegetal subjects in HCI. Through the lens of Respect for Nature, a biocentric ethic, we propose the formulation of design considerations for ethical HCI interaction research involving plants.
The Importance of Data Curation for Data Physicalization

Creating physicalizations requires a high amount of decision taking, in particular in selecting and staging the data to be conveyed, which can be challenging. We give insight into the process of conceptualizing and building physical representations of data. Based on our practical experiences, we discuss preliminary recommendations on how to approach design of physicalizations.
On the expected behaviour of noise regularised deep neural networks as Gaussian processes

Recent work has established the equivalence between deep neural networks and Gaussian processes (GPs), resulting in so-called neural network Gaussian processes (NNGPs). The behaviour of these models depends on the initialisation of the corresponding network. In this work, we consider the impact of noise regularisation (e.g. dropout) on NNGPs, and relate their behaviour to signal propagation theory in noise regularised deep neural networks. For ReLU activations, we find that the best performing NNGPs have kernel parameters that correspond to a recently proposed initialisation scheme for noise regularised ReLU networks. In addition, we show how the noise influences the covariance matrix of the NNGP, producing a stronger prior towards simple functions away from the training points. We verify our theoretical findings with experiments on MNIST and CIFAR-10 as well as on synthetic data.