Reviewerly/exHarmony huggingface dataset:
Expanding Touch Interaction Capabilities for Smart-rings: An Exploration of Continual Slide and Microroll Gestures

As smart-rings emerge in both research and commercial markets, their limited physical size remains to restrict the interaction potential and input vocabulary possible. Thus, focusing on touch interaction for its natural and preferred input potential, this early work explores the combination of slide and microroll gestures performed by the thumb in continual motion on a smart-ring’s touch capacitive surface. We first capture over 3000 slide and microroll gesture instances, extract features, and generate and test machine learning models that are able to discern the slide and microroll gestures within the same touch instance. Through the use of 18 features, our Random Forest model provides a 92.4% accuracy. We conclude with demonstrations of potential applications utilizing continual slide and microroll gestures, and a short discussion which provides future research directions stemming from the positive results obtained from this preliminary work.
Mapping FoodHCI Futures

Recognizing the significant potential impact that HCI has on food practices and experiences, researchers and practitioners are undertaking a growing number of explorations of novel computing technology and food combinations. These explorations have so far primarily emphasized technology-driven systems and taken a human-centric perspective. We propose a Special Interest Group (SIG) in "foodHCI futures" that creates a space for researchers to discuss the boundaries of food incorporating HCI, and with the simultaneous aims of reconciling food with technology and extending our visions for human-food interactions towards anthropocentrism. Specifically, the SIG will be a beginning of developing a structured conceptual map of the possibilities for future technology interventions in food systems. In developing this map, we hope to encourage democratized debate, provoke new and divergent thoughts on the opportunities for foodHCI, and ultimately gain unique insights that contribute to preferable food futures.
Design of Rural Human Resource Management Platform Integrating IoT and Cloud Computing

With the advent of the Internet of Things era, these hot technologies such as distributed, parallel computing, network storage, and load balancing can provide a good application foundation for the Internet of Things, enabling real-time dynamic management and intelligent analysis of hundreds of millions of items in the Internet of Things to be possible. The Internet of Things has changed from a concept to a reality, quickly reaching every corner of society. On the other hand, with the enhancement of the mobility of social talents, the file management of the talent service center is becoming more and more difficult. However, the traditional management methods of human resources files have problems such as poor resource sharing, asymmetric resources, and heterogeneous information sharing, which can no longer meet the needs of both the supply and demand sides of human resources with diversified and multiple organizational structures. Cloud computing technology has powerful data collection functions, self-service functions, and unified resource scheduling functions. Introducing it into the human resources file management system can greatly improve management efficiency. In order to carry out information management of rural human resources, this paper develops a rural human resources management system based on the Internet of Things. This paper introduces the design scheme of rural human resource management platform based on Internet of Things technology and cloud computing technology. The design of this system mainly includes organization setting, post planning, personnel management, salary management, insurance benefits, recruitment and selection, training management, performance appraisal management, labor contract management, comprehensive inquiry, rules and regulations, employee self-help, system setting, and system management function modules. The research results show that the rural human resource management system based on cloud computing can provide a complete human resource management solution for the vast rural areas. It can only purchase services, save a lot of development and maintenance costs, and also customize functions, so as to better meet the needs of use.
Optimized realization of Quantum Fourier Transform for domestic DCU accelerator

Abstract Quantum Fourier Transform (QFT) is a key part of quantum algorithms such as Shor algorithm, Grover algorithm, and quantum phase estimation algorithm. The “Songshan” supercomputer system adopts the heterogeneous architecture of domestic CPU+DCU. The simulation implementation of the quantum Fourier transform on the “Songshan” supercomputer can promote the research and optimization of related quantum algorithms. Based on the HIP heterogeneous programming model, this paper first maps the phase shift transformation R gate operation of the QFT transformation to the DCU accelerator, and initially constructs the heterogeneous implementation of QFT on the “Songshan” supercomputer; then, the Hadamard gate transformation operation originally implemented on the host side was implemented on the DCU, which greatly reduces the communication overhead caused by the quantum information data transmission between the CPU and the DCU device; finally, the quantum logic gate is analyzed, and based on the operation of the phase shift transformation R gate on the probability amplitude information in the simulation algorithm, an optimization scheme to improve the activity of the DCU thread is proposed. During the experimental verification process, the realized 28-qubit quantum Fourier transform optimization has achieved 13.440 speedup compared with the serially realized QFT simulation on the CPU. The basic quantum gate optimization method based on DCU acceleration equipment in this paper can provide a reference for the efficient simulation of other quantum algorithms on domestic heterogeneous platforms.
Improved Deep Neural Network for Cross-Media Visual Communication

Cross-media visual communication is an extremely complex task. In order to solve the problem of segmentation of visual foreground and background, improve the accuracy of visual communication scene reconstruction, and complete the task of visual real-time communication. We propose an improved generative adversarial network. We take the generative adversarial network as the basis and add a combined codec package to the generator, while configuring the generator and discriminator as a cascade structure, preserving the feature upsampling and downsampling convolutional layers of visual scenes with different layers through correspondence. To classify features with different visual scene layers, we add a new auxiliary classifier based on convolutional neural networks. With the help of the auxiliary classifier, similar visual scenes with different feature layers have a more accurate recognition rate. In the experimental part, to better distinguish foreground and background in visual communication, we perform performance tests on foreground and background using separate datasets. The experimental results show that our method has good accuracy in both foreground and background in cross-media communication for real-time visual communication. In addition, we validate the efficiency of our method on Cityscapes, NoW, and Replica datasets, respectively, and experimentally demonstrate that our method performs better than traditional machine learning methods and outperforms deep learning methods of the same type.
Automatic Classification of Normal and Infected Blood Cells for Leukemia Through Color Based Segmentation Technique Over Innovative CNN Algorithm and Comparing the Error Rate with RNN

To classify the normal infected blood cells through color-based segmentation for leukemia by comparing the error rate for the innovative Convolutional Neural Network and Recurrent Neural Network algorithm. Materials and Methods: Convolutional Neural Network algorithm, which has been taken as an input image and differentiating according to the properties of the image. Here the white blood cells acted as the major parameter for detecting the disease. Result: Data collection was carried out and the analysis could have been done by using blood cell sample images to detect the result and error rate of a particular algorithm. Here in this proposed work, the error rate was reduced in innovative Convolutional Neural Networks compared to Recurrent Neural Networks. Conclusion: The data was collected from various resources for the usage of disease detection. The reduced error rate for the Convolutional Neural Network (87.02%) was used as an algorithm for the whole disease detection process for reduced error rate results compared to the Recurrent Neural Network (89.42%).
Fight Hardware with Hardware: Systemwide Detection and Mitigation of Side-channel Attacks Using Performance Counters

We present a kernel-level infrastructure that allows systemwide detection of malicious applications attempting to exploit cache-based side-channel attacks to break the process confinement enforced by standard operating systems. This infrastructure relies on hardware performance counters to collect information at runtime from all applications running on the machine. High-level detection metrics are derived from these measurements to maximize the likelihood of promptly detecting a malicious application. Our experimental assessment shows that we can catch a large family of side-channel attacks with a significantly reduced overhead. We also discuss countermeasures that can be enacted once a process is suspected of carrying out a side-channel attack to increase the overall tradeoff between the system’s security level and the delivered performance under non-suspected process executions.
Case representation and retrieval for complex product design based on case-based reasoning

Reusing design knowledge of products is a useful way to solve the efficiency issue of complex product design. The design knowledge is tacit, empirical, and unstructured and there exists insufficient case matching and inefficient design reuse in complex products design process. Aiming at these problems, this paper presents an improved case-based reasoning methodology combining ontology with two-stage retrieval. Firstly, a knowledge domain ontology model of complex product design is constructed, and the technology of ontology-based data access is introduced to automatically generate a case knowledge base with semantic information. Then, a new two-stage case retrieval method integrated semantic query with similarity calculation is proposed. The case subset is selected by query statements. It has the characteristic of isomorphism with design problem. The retrieval mechanism is applied to compress the traversal space, reduce the redundancy of semantic similarity calculation, improve the retrieval efficiency, and fulfill the target of case reuse. Finally, a variant design of the chiller unit as an example is executed to illustrate the use of the proposed method, and experiments are organized to evaluate its performance. The result shows that the proposed approach has an average precision of 92% and high stability, outperforming existing methods.
Amer: A New Attribute-Missing Network Embedding Approach

Network embedding which aims to learn a low dimensional representation of nodes is a powerful technique for network analysis. While network embedding for networks with complete attributes has been widely investigated, in many real-world applications the attributes of partial nodes are unobserved (i.e., missing) due to privacy concern or resource limit. Very recently, several network embedding methods have been proposed for attribute-missing networks. They first complete the missing attributes and then use the complemented network to learn network embedding. The parameters of these two processes cannot be adjusted by each other, resulting in compromised results. To address this problem, we propose a unified model in which the process of completing missing attributes and the process of learning embedding are not separated but closely intertwined. Being specific, completing missing attributes is under the guidance of learning network representation via mutual information maximization, and the complemented attributes directly enter network representation module which will generate further feedback for completing missing attributes. We further impose attribute-structure relationship constraint for completing missing attributes by designing a new generative adversarial networks (GANs) model. To the best of our knowledge, this is the first unified model for attribute-missing network embedding. Empirical results on real-world datasets show the superiority of our new method over other state-of-the-art methods on four network analysis tasks, including node classification, node clustering, link prediction, and network visualization.
A Hybrid Tucker-VQ Tensor Sketch decomposition model for coding and streaming real world light fields using stack of differently focused images

Computational multi-view displays involving light fields are a fast emerging choice for 3D presentation of real-world scenes. Tensor autostereoscopic glasses-free displays use just few light attenuating layers in front of a backlight to output high quality light field. We propose three novel schemes, Focal Stack - Hybrid Tucker-TensorSketch Vector Quantization (FS-HTTSVQ), Focal Stack - Tucker-TensorSketch (FS-TTS), and Focal Stack - Tucker Alternating Least-Squares (FS-TALS), for efficient representation, streaming and coding of light fields using a stack of differently focused images. Working with a focal stack instead of the entire light field majorly reduces the data acquisition cost as well as the computation and processing cost. Extensive experiments with real world light field focal stacks demonstrate that proposed novel one-pass Tucker decomposition using TensorSketch with hybrid vector quantization in FS-HTTSVQ, compactly represents the approximated focal stack in codebook form for better transmission and streaming. Encoding with High Efficiency Video Coding (HEVC) eliminates all intrinsic redundancies present in the approximated focal stack. Resultant low-rank approximated and coded focal stack is then employed to analytically optimize layer patterns for the tensor display. The complete end-to-end light field processing pipelines flexibly work for multiple bitrates and are adaptable for a variety of multi-view autostereoscopic platforms. Our schemes exhibit note-worthy performances on focal stacks compared to direct encoding of an entire light field using a standard codec like HEVC.
Comparative Study of Multiclass Text Classification in Research Proposals Using Pretrained Language Models

Recently, transformer-based pretrained language models have demonstrated stellar performance in natural language understanding (NLU) tasks. For example, bidirectional encoder representations from transformers (BERT) have achieved outstanding performance through masked self-supervised pretraining and transformer-based modeling. However, the original BERT may only be effective for English-based NLU tasks, whereas its effectiveness for other languages such as Korean is limited. Thus, the applicability of BERT-based language models pretrained in languages other than English to NLU tasks based on those languages must be investigated. In this study, we comparatively evaluated seven BERT-based pretrained language models and their expected applicability to Korean NLU tasks. We used the climate technology dataset, which is a Korean-based large text classification dataset, in research proposals involving 45 classes. We found that the BERT-based model pretrained on the most recent Korean corpus performed the best in terms of Korean-based multiclass text classification. This suggests the necessity of optimal pretraining for specific NLU tasks, particularly those in languages other than English.
Travelogue: Representing Indoor Trajectories as Informative Art

In this work, we explore if informative art can represent a user's indoor trajectory and promote user's self-reflection, creating a new type of interactive space. Under the assumption that the simplicity of a digital picture frame can be an appealing way to represent indoor activities and further create a dyadic relationship between users and the space they occupy, we present Travelogue, a picture-frame like self-contained system which can sense human movement using wireless signal reflections in a device free manner. Breaking away from traditional dashboard-based visualization techniques, Travelogue only renders the high-level extent and location of users' activities in different informative arts. Our preliminary user study with 12 participants shows most users found Travelogue intuitive, unobtrusive, and aesthetically pleasing, as well as a desired tool for self-reflection on indoor activity.
“It was hard to find the words”: Using an Autoethnographic Diary Study to Understand the Difficulties of Smart Home Cyber Security Practices

This study considers how well an autoethnographic diary study helps as a method to explore why families might struggle in the application of strong and cohesive cyber security measures within the smart home. Combining two human-computer interaction (HCI) research methods - the relatively unstructured process of autoethnography and the more structured diary study - allowed the first author to reflect on the differences between researchers or experts, and everyday users. Having a physical set of structured diary prompts allowed for a period of 'thinking as writing', enabling reflection upon how having expert knowledge may or may not translate into useful knowledge when dealing with everyday life. This is particularly beneficial in the context of home cyber security use, where first-person narratives have not made up part of the research corpus to date, despite a consistent recognition that users struggle to apply strong cyber security methods in personal contexts. The framing of the autoethnographic diary study contributes a very simple, but extremely powerful, tool for anyone with more knowledge than the average user of any technology, enabling the expert to reflect upon how they themselves have fared when using, understanding and discussing the technology in daily life.
Human–Robot Interactive Communication and Cognitive Psychology Intelligent Decision System Based on Artificial Intelligence — Case Study

Cognitive psychology is a science of human knowledge, which means that people perceive, acquire, memorize, think, and comprehend intellectual capabilities. The psychological strategy involves controlling every action and status of the human body. The problematic states of psychological facts include mental disorders like depression, stress, anxiety, and inferiority complex, leading to memory loss. The emerged technique of cognitive psychological managing framework using artificial intelligence (CPMF-AI) is introduced. The proposed framework is extended to forecast the psychological standards of the human brain for practical well-being. There are four methods to monitor memory power, stress, and other human mental disorders. They are distant neural systems (DNS), convolutional psychology tracking systems (CPTS), intelligent neural systems (INS), and memory-building strategies (MBS). Besides language aspects, physical aspects play a vital part in human–robot interaction (HRI) and make the difference compared to the more limited HRI communication. These methodologies are integrated into four case studies to detect neural passage systems for monitoring mental issues. The simulation analysis helps enhance the framework’s accuracy and minimize the error rate. Thus, the proposed system of cognitive technology is comparatively better than the existing methods.
A Unified Framework on Generalizability of Clinical Prediction Models

To be useful, clinical prediction models (CPMs) must be generalizable to patients in new settings. Evaluating generalizability of CPMs helps identify spurious relationships in data, provides insights on when they fail, and thus, improves the explainability of the CPMs. There are discontinuities in concepts related to generalizability of CPMs in the clinical research and machine learning domains. Specifically, conventional statistical reasons to explain poor generalizability such as inadequate model development for the purposes of generalizability, differences in coding of predictors and outcome between development and external datasets, measurement error, inability to measure some predictors, and missing data, all have differing and often complementary treatments, in the two domains. Much of the current machine learning literature on generalizability of CPMs is in terms of dataset shift of which several types have been described. However, little research exists to synthesize concepts in the two domains. Bridging this conceptual discontinuity in the context of CPMs can facilitate systematic development of CPMs and evaluation of their sensitivity to factors that affect generalizability. We survey generalizability and dataset shift in CPMs from both the clinical research and machine learning perspectives, and describe a unifying framework to analyze generalizability of CPMs and to explain their sensitivity to factors affecting it. Our framework leads to a set of signaling statements that can be used to characterize differences between datasets in terms of factors that affect generalizability of the CPMs.
Machine In The Middle: Exploring Dark Patterns of Emotional Human-Computer Integration Through Media Art

As our relationship with machines becomes evermore intimate, we observe increasing efforts in the quantification of human emotion, which has historically generated unintended consequences. We acknowledge an amplification of this trend through recent technological developments that aim toward human-computer integration, and explore the dark patterns that may arise for integrating emotions with machinic processes through “machine_in_the_middle”. Machine_in_the_middle is an interactive system in which a participant wears an electroencephalographic headset, and their neural activity is analysed to ascertain an approximation of their emotional state. Using electrical muscle stimulation their face is animated into an expression that corresponds with the output of the emotional recognition system. Through our work, we contribute the insight of three possible dark patterns that might emerge from emotional integration, including: reductionism of human emotion, disruptions of agency, and parasitic symbiosis. We hope that these insights inspire researchers and practitioners to approach human-computer integration more cautiously.