Reviewerly/exHarmony huggingface dataset:
Self-Supervised CycleGAN for Object-Preserving Image-to-Image Domain Adaptation

Recent generative adversarial network (GAN) based methods (e.g., CycleGAN) are prone to fail at preserving image-objects in image-to-image translation, which reduces their practicality on tasks such as domain adaptation. Some frameworks have been proposed to adopt a segmentation network as the auxiliary regularization to prevent the content distortion. However, all of them require extra pixel-wise annotations, which is difficult to fulfill in practical applications. In this paper, we propose a novel GAN (namely OP-GAN) to address the problem, which involves a self-supervised module to enforce the image content consistency during image-to-image translations without any extra annotations. We evaluate the proposed OP-GAN on three publicly available datasets. The experimental results demonstrate that our OP-GAN can yield visually plausible translated images and significantly improve the semantic segmentation accuracy in different domain adaptation scenarios with off-the-shelf deep learning networks such as PSPNet and U-Net.
Efficient Design of Pruned Convolutional Neural Networks on FPGA

Convolutional Neural Networks (CNNs) have improved several computer vision applications, like object detection and classification, when compared to other machine learning algorithms. Running these models in edge computing devices close to data sources is attracting the attention of the community since it avoids high-latency data communication of private data for cloud processing and permits real-time decisions turning these systems into smart embedded devices. Running these models is computationally very demanding and requires a large amount of memory, which are scarce in edge devices compared to a cloud center. In this paper, we proposed an architecture for the inference of pruned convolutional neural networks in any density FPGAs. A configurable block pruning method is proposed together with an architecture that supports the efficient execution of pruned networks. Also, pruning and batching are studied together to determine how they influence each other. With the proposed architecture, we run the inference of a CNN with an average performance of 322 GOPs for 8-bit data in a XC7Z020 FPGA. The proposed architecture running AlexNet processes 240 images/s in a ZYNQ7020 and 775 images/s in a ZYNQ7045 with only 1.2% accuracy degradation.
Classical Homomorphic Encryption for Quantum Circuits

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 6 December 2018Accepted: 08 September 2020Published online: 15 December 2020Keywordshomomorphic encryption, blind computing, learning with errorsAMS Subject Headings81P68, 68Q12, 81P94, 94A60Publication DataISSN (print): 0097-5397ISSN (online): 1095-7111Publisher: Society for Industrial and Applied MathematicsCODEN: smjcat
A Spin Glass Model for the Loss Surfaces of Generative Adversarial Networks

We present a novel mathematical model that seeks to capture the key design feature of generative adversarial networks (GANs). Our model consists of two interacting spin glasses, and we conduct an extensive theoretical analysis of the complexity of the model's critical points using techniques from Random Matrix Theory. The result is insights into the loss surfaces of large GANs that build upon prior insights for simpler networks, but also reveal new structure unique to this setting which explains the greater difficulty of training GANs.
Monotone Fuzzy Rule Interpolation for Practical Modeling of the Zero-Order TSK Fuzzy Inference System

Formulating a generalized monotone fuzzy rule interpolation (MFRI) model is difficult. A complete and monotone fuzzy rule-base is essential for devising a monotone zero-order Takagi–Sugeno–Kang (TSK) fuzzy inference system (FIS) model. However, such a complete and monotone fuzzy rule-base is not always available in practice. In this article, we develop an MFRI modeling scheme for generating a monotone zero-order TSK FIS from a monotone and incomplete fuzzy rule-base. In our proposal, a monotone-ordered fuzzy rule-base that consists of the available fuzzy rules from a monotone and incomplete fuzzy rule-base and those derived from the MFRI reasoning is formed. We outline three important properties that the MFRI's deduced fuzzy rules should satisfy to ensure a monotone-ordered fuzzy rule-base. A Lagrangian function for the MFRI scheme, together with its Karush–Kuhn–Tucker optimality conditions, is formulated and analyzed. The key idea is to impose constraints that guide the MFRI inference outcomes. An iterative MFRI algorithm that adopts an augmented Lagrangian function is devised. The proposed MFRI algorithm aims to achieve an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\boldsymbol{\varepsilon }}$</tex-math></inline-formula> -optimality condition and to produce an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\boldsymbol{\varepsilon }}$</tex-math></inline-formula> -optimal solution, which is geared for practical applications. We apply the MFRI algorithm to a failure mode and effect analysis case study and a tanker ship heading regulation problem. The results indicate the effectiveness of MFRI for generating monotone TSK FRI models in tackling practical problems.
Improved Entity Linking for Simple Question Answering Over Knowledge Graph

Question Answering systems over Knowledge Graphs (KG) answer natural language questions using facts contained in a knowledge graph, and Simple Question Answering over Knowledge Graphs (KG-SimpleQA) means that the question can be answered by a single fact. Entity linking, which is a core component of KG-SimpleQA, detects the entities mentioned in questions, and links them to the actual entity in KG. However, traditional methods ignore some information of entities, especially entity types, which leads to the emergence of entity ambiguity problem. Besides, entity linking suffers from out-of-vocabulary (OOV) problem due to the limitation of pre-trained word embeddings. To address these problems, we encode questions in a novel way and encode the features contained in the entities in a multilevel way. To evaluate the enhancement of the whole KG-SimpleQA brought by our improved entity linking, we utilize a relatively simple approach for relation prediction. Besides, to reduce the impact of losing the feature during the encoding procedure, we utilize a ranking algorithm to re-rank (entity, relation) pairs. According to the experimental results, our method for entity linking achieves an accuracy of 81.8% that beats the state-of-the-art methods, and our improved entity linking brings a boost of 5.6% for the whole KG-SimpleQA.
SketCHI 2.0

Sketching is universal. It enables us to work through problems, communicate complexity, work with people who have diverse needs, and document work processes we employ within Human-Computer Interaction. Increased interest in sketching as a methodology within HCI has led to increased attendance of interactive courses, meet-ups, and discussion groups, from those who are complete beginners, to seasoned researchers with the skills and knowledge to support others. By bringing together these individuals, we are able to advance the understanding of how sketching underpins research, and how we might work with sketching as technology advances. SketCHI 2.0 aims to support ongoing discussions and collaborations around sketching in HCI, and further build the Sketching HCI community. As well as drawing on location, feedback, and discussion, we will form collaborative working groups to further our collective interest in this area and conduct high-level discussions about the practical applications and outputs of sketching in HCI.
Constrained voting extreme learning machine and its application

Extreme learning machine (ELM) has been proved to be an effective pattern classification and regression learning mechanism by researchers.However, its good performance is based on a large number of hidden layer nodes.With the increase of the nodes in the hidden layers, the computation cost is greatly increased.In this paper, we propose a novel algorithm, named constrained voting extreme learning machine (CV-ELM).Compared with the traditional ELM, the CV-ELM determines the input weight and bias based on the differences of between-class samples.At the same time, to improve the accuracy of the proposed method, the voting selection is introduced.The proposed method is evaluated on public benchmark datasets.The experimental results show that the proposed algorithm is superior to the original ELM algorithm.Further, we apply the CV-ELM to the classification of superheat degree (SD) state in the aluminum electrolysis industry, and the recognition accuracy rate reaches 87.4%, and the experimental results demonstrate that the proposed method is more robust than the existing state-of-the-art identification methods.
ReadNet: A Hierarchical Transformer Framework for Web Article Readability Analysis

Analyzing the readability of articles has been an important sociolinguistic task. Addressing this task is necessary to the automatic recommendation of appropriate articles to readers with different comprehension abilities, and it further benefits education systems, web information systems, and digital libraries. Current methods for assessing readability employ empirical measures or statistical learning techniques that are limited by their ability to characterize complex patterns such as article structures and semantic meanings of sentences. In this paper, we propose a new and comprehensive framework which uses a hierarchical self-attention model to analyze document readability. In this model, measurements of sentence-level difficulty are captured along with the semantic meanings of each sentence. Additionally, the sentence-level features are incorporated to characterize the overall readability of an article with consideration of article structures. We evaluate our proposed approach on three widely-used benchmark datasets against several strong baseline approaches. Experimental results show that our proposed method achieves the state-of-the-art performance on estimating the readability for various web articles and literature.
Analysis on Academic Benchmark Design and Teaching Method Improvement under Artificial Intelligence Robot Technology

To allow robots to better complete teaching tasks in the education field, based on artificial intelligence robot technology, the academic benchmark is designed for information technology teaching. First, LEGO MINDSTORMS EV3 is introduced for statement, which is developed through information technology courses based on robot education. Then, according to the sensor teaching in robot education, the teaching contents are designed for the information technology course. After that, the teaching method is improved based on sensor teaching for the information technology course. Finally, the feasibility is evaluated of the existing of teaching resources, and the effect of information technology teaching is analyzed through a questionnaire survey. The results show that more than 80% of students are interested in robot teaching, and more than 70% of students can master relevant theoretical knowledge in practical operation, which proves that robot education can arouse students' interest in information technology courses. In conclusion, the learning methods proposed in this study can enable students to better master related theories, and improve students' operation and innovation abilities. The study provides a reference for the application of artificial intelligence robot technology in the field of education.
The impact of environmental stochasticity on value-based multiobjective reinforcement learning

We report a previously unidentified issue with model-free, value-based approaches to multiobjective reinforcement learning in the context of environments with stochastic state transitions. An example multiobjective Markov Decision Process (MOMDP) is used to demonstrate that under such conditions these approaches may be unable to discover the policy which maximises the Scalarised Expected Return, and in fact may converge to a Pareto-dominated solution. We discuss several alternative methods which may be more suitable for maximising SER in MOMDPs with stochastic transitions.
Organizational-Based Self-Esteem and Organizational Identification as Predictors of Turnover Intention: Mediating Role of Organizational Trust

Purpose- This study aimed to examine the predictive role of organizational-based self-esteem and organizational identification on turnover intention while also studying the mediating effect of organizational trust among employees across five organizations. Design/Methodology- This study utilized the cross-sectional research design and quantitative approach for data collection. The study sample comprises 131 employees drawn from five organizations with a mean age of 33.15 years (SD, 7.97). Standardized instruments (questionnaires) were used for data collection. The IBM-SPSS Statistics and Hayes PROCESS macro (model 4) was used for testing the hypotheses and conducting the mediational analysis. Findings- The results of the study revealed a significant negative relationship between organizational-based self-esteem and turnover intention (β= -.33, p &lt; .01), and also a significant negative relationship between organizational identification and turnover intention (β=-.29, p &lt; .01). Organizational trust was also found to mediate both relationships. Practical Implications- The results of this study highlight the importance of organizational-based self-esteem, organizational identification and trust in reducing turnover intention. The study recommends that to keep employees in the organization, human resources management (HRM) needs to foster trust, build practice that will promote identification and attachment, and enhance the relationship between the organization and employees.
Unsupervised 3D Brain Anomaly Detection

Anomaly detection (AD) is the identification of data samples that do not fit a learned data distribution. As such, AD systems can help physicians to determine the presence, severity, and extension of a pathology. Deep generative models, such as Generative Adversarial Networks (GANs), can be exploited to capture anatomical variability. Consequently, any outlier (i.e., sample falling outside of the learned distribution) can be detected as an abnormality in an unsupervised fashion. By using this method, we can not only detect expected or known lesions, but we can even unveil previously unrecognized biomarkers. To the best of our knowledge, this study exemplifies the first AD approach that can efficiently handle volumetric data and detect 3D brain anomalies in one single model. Our proposal is a volumetric and high-detail extension of the 2D f-AnoGAN model obtained by combining a state-of-the-art 3D GAN with refinement training steps. In experiments using non-contrast computed tomography images from traumatic brain injury (TBI) patients, the model detects and localizes TBI abnormalities with an area under the ROC curve of ~75%. Moreover, we test the potential of the method for detecting other anomalies such as low quality images, preprocessing inaccuracies, artifacts, and even the presence of post-operative signs (such as a craniectomy or a brain shunt). The method has potential for rapidly labeling abnormalities in massive imaging datasets, as well as identifying new biomarkers.
Institutional Traps of Digitalization of Russian Higher Education

In 2016-2018 the state in Russia adopted a package of program documents, which implies the transfer of education to the large-scale introduction of digital technologies. This phenomenon has been called “digitalization of education”. In scientific literature, electronization and digitalization are increasingly called one of the institutional traps for the development of Russian universities, since the corresponding institutional environment has not yet been formed due to the forced nature of innovations. As a result, the processes of introducing new technologies into education are still not regulated. Within the framework of the purpose of the study, the manifestations of the trap of electronization and digitalization of Russian higher education were analyzed on the basis of sociological data, and the theoretical modeling of the process of adaptation of educational agents to the institution of digitalization was carried out. In the course of the study, the approaches were summarized that have been developed in discussions on educational digitalization. The article presents the author’s vision of the studied phenomenon as an institutional trap; as well as understanding of the institutional features and characteristics of electronization and digitalization in education. The research method is the analysis of estimates obtained in the course of an expert survey which was conducted by the Vologda Scientific Center of the Russian Academy of Sciences among the representatives of the teaching staff of state universities in the Vologda region. In the course of this analysis, the indicators of educational digitalization as an effective innovation were clarified such as an increased accessibility of educational resources; simplification of communication and the process of transferring knowledge from teacher to student; increased opportunities for training specialists for the new (digital) economy; improving the quality of education in universities, etc. Based on the results of the empirical study, it has been determined that the conditions for the development of digitalization in Russian universities are currently ambiguous, which is closely related to the level of competitiveness of the educational organization. The scientific novelty of the research consists in the presentation of an original matrix describing the process of university employees adaptation to the conditions of digital transformation of education. The matrix is proposed on the basis of a sociological analysis of the impact of the trap of electronization and digitalization on the activities of educational agents. The matrix can be taken into account in the practice of higher education management.
Engaging the Senses in Qualitative Research via Multimodal Coding: Triangulating Transcript, Audio, and Video Data in a Study With Sexual and Gender Minority Youth

The variety of formats in which qualitative data may be collected have been explored within the methodological literature. Yet, the multiple options for coding these data formats have not been comprehensively detailed. While transcript analysis is widely used across disciplines, it may have limitations—particularly for research involving marginalized populations. This paper presents a multimodal coding approach as a methodological innovation for triangulating three data formats (transcript, audio, and video), detailed through the application of this analytic approach during a qualitative study exploring media engagement with sexual and gender minority youth (SGMY). Nineteen semi-structured interviews with SGMY were filmed and transcribed. Nine independent coders then utilized the innovative multimodal approach to code the three data formats using a constructivist grounded theory framework. Some codes were similar across modalities, such as those related to safety issues and finding identity and community through media. Others differed between modalities, such as those related to participant affect, perceived contradictions, discrepancies between verbal statements and body language, level of comfort and engagement, and distress when discussing traumatic experiences. Video coding captured the broadest range of emotions and experiences from marginalized youth, while transcripts provided the most straightforward form of data for coding. Multimodal coding may be applicable across qualitative approaches to enrich analyses and account for potential biases, thereby enhancing analytical lenses in qualitative inquiry. Methodological strategies for coding and integrating data types are discussed.
Research on the Path Planning Algorithm of Mobile Robot

Aiming at the problem of local deadlock and path redundancy when mobile robots are running in a complex and unknown environment, specific application design and simulation comparisons of several existing local path planning methods are carried out, and the planning process The problems that have arisen propose solutions. The principle and usage scenarios of three local path planning algorithms, artificial potential field method, Rapid-exploration Random Tree and bidirectional Rapid-exploration Random Tree are analyzed emphatically. Simulation tests are carried out on the MATLAB platform to verify the feasibility of the designed method Sex and effectiveness. This paper optimizes the RRT algorithm to get the B-RRT algorithm, so that the calculation time of path planning is greatly shortened.
Consequences, Schmonsequences! Considering the Future as Part of Publication and Peer Review in Computing Research

Research in computing is becoming increasingly concerned with understanding and mitigating unintended consequences of technology developments. However, those concerns are rarely reflected in how we submit, review, and publish our own work. Specifically, in talking about how our new apps, devices, and algorithms will change the world, we focus almost exclusively on positive consequences. There have been calls to require some speculation about negative impacts as part of the peer review process. This workshop will explore how to think about and report potential negative consequences in our papers in a way that's practical, inclusive, and achievable. The aim is to draw on scholarship around creative-yet-grounded speculation about technology futures and to consider how these might be applied to publication and peer review. The workshop aims to inspire the CHI conference and the computing research community to meaningfully consider and act upon the potential negative implications of their work.
VID-WIN: Fast Video Event Matching With Query-Aware Windowing at the Edge for the Internet of Multimedia Things

Efficient video processing is a critical component in many IoMT applications to detect events of interest. Presently, many window optimization techniques have been proposed in event processing with an underlying assumption that the incoming stream has a structured data model. Videos are highly complex due to the lack of any underlying structured data model. Video stream sources such as CCTV cameras and smartphones are resource-constrained edge nodes. At the same time, video content extraction is expensive and requires computationally intensive Deep Neural Network (DNN) models that are primarily deployed at high-end (or cloud) nodes. This paper presents VID-WIN, an adaptive 2-stage allied windowing approach to accelerate video event analytics in an edge-cloud paradigm. VID-WIN runs parallelly across edge and cloud nodes and performs the query and resource-aware optimization for state-based complex event matching. VID-WIN exploits the video content and DNN input knobs to accelerate the video inference process across nodes. The paper proposes a novel content-driven micro-batch resizing, queryaware caching and micro-batch based utility filtering strategy of video frames under resource-constrained edge nodes to improve the overall system throughput, latency, and network usage. Extensive evaluations are performed over five real-world datasets. The experimental results show that VID-WIN video event matching achieves ~2.3X higher throughput with minimal latency and ~99% bandwidth reduction compared to other baselines while maintaining query-level accuracy and resource bounds.