Reviewerly/exHarmony huggingface dataset:
A Comparison of Transformer-Based Language Models on NLP Benchmarks

Since the advent of BERT, Transformer-based language models (TLMs) have shown outstanding effectiveness in several NLP tasks. In this paper, we aim at bringing order to the landscape of TLMs and their performance on important benchmarks for NLP. Our analysis sheds light on the advantages that some TLMs take over the others, but also unveils issues in making a complete and fair comparison in some situations.
Dynamic Big Data Drift Visualization of CPU and Memory Resource Usage in Cloud Computing

Drift Visualization gives better insight into the nature of changes in the data distribution. Cloud trace is dynamic and generated at a very high pace, it’s a serious problem that needs more deep discussions. Drift in cloud resource usage can cause low resource efficiency. In order to achieve optimal resource utilization, cloud provider needs a prediction model based on the data insights. Efficient drift detection optimizes the model prediction. Changes in data can cause these models to reduce their accuracy over a period of time. The focus of this research is to visualize the drift in the cloud at the cluster level. These visualizations will help cloud providers in understanding major factors contributing to the drift. In this paper, Cluster-based visualization using k-means is used to show the drift in the cloud.
Same Speaker Identification with Deep Learning and Application to Text-Dependent Speaker Verification

In human-based visual and auditory forensic speaker recognition or comparison, experts make judgments on whether or not two speech samples are from the same speaker. They compare specific parts of the same phoneme where the speaker's characteristics appear. Inspired by this process, we propose a Same Speaker Identification Deep Neural Network (SSI-DNN) that identifies whether or not two speech samples are uttered by the same speaker by focusing on the same phonemes, which have very short durations. The architecture of the proposed SSI-DNN is based on ResNet, with the input extended to be two speech samples, and the output is the identification result. Because the proposed method does not use speaker embedding or speaker models, it can be trained on a small number of utterances for each speaker. To evaluate the verification performance, we conducted speaker verification experiments for five Japanese single vowels, which are 0.12 s each, and a short phrase "oi," which means "hey," in a speech database for the forensic speaker recognition constructed by the National Research Institute of Police Science in Japan. The experimental results showed that when two speech samples were uttered 30 min apart, the equal error rate (EER) for single vowels was 7.4%, 1.2% for the five-vowel score combination, and 4.0% for "oi." With about a three-month interval, the EER was 12.8% for single vowels, 4.2% for the five-vowel score combination, and 8.1% for "oi."
Gaussian Optimized Deep Learning-based Belief Classification Model for Breast Cancer Detection

With the rapid increase of new cases with an increased mortality rate, cancer is considered the second and most deadly disease globally. Breast cancer is the most widely affected cancer worldwide, with an increased death rate percentage. Due to radiologists’ processing of mammogram images, many computer-aided diagnoses have been developed to detect breast cancer. Early detection of breast cancer will reduce the death rate worldwide. The early diagnosis of breast cancer using the developed computer-aided diagnosis (CAD) systems still needed to be enhanced by incorporating innovative deep learning technologies to improve the accuracy and sensitivity of the detection system with a reduced false positive rate. This paper proposed an efficient and optimized deep learning-based feature selection approach with this consideration. This model selects the relevant features from the mammogram images that can improve the accuracy of malignant detection and reduce the false alarm rate. Transfer learning is used in the extraction of features initially. Na ext, a convolution neural network, is used to extract the features. The two feature vectors are fused and optimized with enhanced Butterfly Optimization with Gaussian function (TL-CNN-EBOG) to select the final most relevant features. The optimized features are applied to the classifier called Deep belief network (DBN) to classify the benign and malignant images. The feature extraction and classification process used two datasets, breast, and MIAS. Compared to the existing methods, the optimized deep learning-based model secured 98.6% of improved accuracy on the breast dataset and 98.85% of improved accuracy on the MIAS dataset.
An Effective Signcryption with Optimization Algorithm for IoT-enabled Secure Data Transmission

Internet of Things (IoT) allows several low resources and controlled devices to interconnect, calculate processes and make decisions in the communication network. In the heterogeneous environment for IoT devices, several challenging issues such as energy, storage, efficiency, and security. The design of encryption techniques enables the transmission of the data in the IoT environment in a secured way. The proper selection of optimal keys helps to boost the encryption performance. With this motivation, the study presents a signcryption with quantum chaotic krill herd algorithm for secured data transmission (SCQCKH-SDT) in IoT environment. The proposed SCQCKH-SDT technique aims to effectively encrypts the data by the use of optimal keys generated by the CQKH algorithm. The proposed SCQCKH-SDT technique initially employs the signcryption technique for the encryption of data. In order to optimize the secrecy, the optimal key generation process is carried out using Chaotic Krill Herd (CQKH) algorithm. The CQKH algorithm incorporates the concept of quantum computing and chaotic theory into the traditional KH algorithm. The performance validation of the SCQCKH-SDT technique is performed using benchmark dataset. An extensive comparative analysis reported the superior performance of the SCQCKH-SDT technique over the recent approaches.
A Virtual Reality Scaffolding Prototype for College Students Self-directed Learning in STEAM

Self-directed learning (SDL) is an effective method to model the core technology for STEAM college students. However, STEAM includes immersion technologies and many fields, and learning usually crosses semesters, which is difficult for students to construct personal skill models. The VR scaffold developed based on SDL theory, hints, and expert modeling visualization help students construct their core technical models. The prototype is editable and sets up tasks and levels in a gamified way, and finally shows the learning results.
Efficient Data Management and Interoperability Middleware in Business-Oriented Smart Port Use Cases

The interoperability of critical infrastructures, such as ports, has become a primary concern of EU in recent years. Information systems that have the control of these infrastructures are continuously evolving and handle heterogeneous collections of data, processes, and people. Moreover, cross-infrastructure dependencies may give rise to cascading and escalating data model discrepancies across interconnected systems. In this article, we present a data model -following the newest technology standards- that tries to consolidate APIs and services of highly complex infrastructures. Port environments are a characteristic example of them, since massive amount of data and services from different sources are processed and used. We adopt a bottom-up approach, considering every service interconnection as an independent entity, which must be aligned with the proposed common vocabulary and data model. The strict guidelines that are injected into the lifecycle of a service/component development, lead to explicitly enforce interoperability between each one service that lives inside the ecosystem of the port. That is -and should be- a step towards “the cognitive port of the future”, where developers, SMEs and huge vendors can exchange and reuse data from a shared repository. Consequently, ports will play key role in the new information system model, as in-house marketplaces will be developed, for companies to disseminate and exploit their data and services.
Enabling Trimap-Free Image Matting With a Frequency-Guided Saliency-Aware Network via Joint Learning

This paper presents a strategic approach to tackling trimap-free natural image matting. Specifically, to address the false detection issue of existing trimap-free matting algorithms when the foreground object is not uniquely defined, we design a novel tangled structure (TangleNet) to handle foreground detection and matting prediction simultaneously. TangleNet enables information exchange between foreground segmentation and alpha prediction, producing high-quality alpha mattes for the most salient foreground object based on RGB inputs alone. TangleNet boosts network performance with a frequency-guided attention mechanism utilizing wavelet data. Additionally, we pretrain for salient object detection to aid in the foreground segmentation. Experimental results demonstrate that TangleNet is on par with the state-of-the-art matting methods requiring additional inputs, and outperforms all previous trimap-free algorithms in terms of both qualitative and quantitative results.
Revitalize Qiang Language and Culture by Designing Serious Games Based on Interactive Projection

The Qiang people is an ancient minority in western China. However, Qiang language, as the carrier of Qiang culture and spirit, is rapidly fading as less and fewer people can speak it. Despite the fact that the serious game of indigenous language has been widely discussed, there is still a dearth of research on the Qiang language and culture. This research aims to determine the efficacy of serious games based on interactive projection to encourage people's interest in Qiang language and culture. To better understand the Qiang language and cultural heritage's current state and challenges, we conducted a three-day field investigation in the Qiang village. Based on the field study's findings, we created "WhiteStone", an interactive projection device based on the heroic epic of Qiang. Our user research consists of remote verification and user testing, indicating that serious games based on projected interaction have the potential to create engrossed and interesting experiences, which can attract young people's interest in the Qiang language and culture. This paper examines the design opportunities for serious games based on interactive projection to revitalize the Qiang language and culture.
Improving Energy-Based Out-of-Distribution Detection by Sparsity Regularization

Out-of-distribution (OOD) detection is critical for safely deploying machine learning models in the open world. Recently, an energy-score based OOD detector was proposed for any pre-trained classification models. The energy score, which is less susceptible to overconfidence, proves to be a better substitute for the conventional approaches leveraging the softmax confidence score. However, current energy-score based methods rely heavily on large-scale auxiliary datasets and introduce several dataset-dependent hyperparameters. In this paper, we propose a simple yet effective sparsity-regularized learning objective for deep neural networks so that the energy-based detector works better. Our learning objective is parameter-free and its key idea is to enlarge the differences between network outputs of in-distribution data and OOD data by regularizing the networks to generate high sparsity representations for in-distribution data. We also contribute to a tiny auxiliary outlier dataset to replace the previous one, which reduces the volume size significantly (230G vs. 40M). Besides, a new energy-score based OOD detector named Sparsity-Regularized Outlier Exposure (SROE) is proposed to incorporate the proposed sparsity-regularized loss function into the traditional Outlier Exposure method. Experimental results show that the proposed sparsity-regularized loss strategy is effective, and the SROE OOD detector outperforms the other SOTA methods with a large margin. The source code and dataset are available at https://github.com/kuan-li/SparsityRegularization .
Research on the Design of Children’s Breath Training Products from the Perspective of Motivation Theory

Objective: In order to shorten the product development and design cycle, improve children’s breathing training experience, and help the healthy development of the children’s rehabilitation training industry. Method: Through literature research, field investigation and observation, expert group interviews and other methods, we can understand and analyze the users’ demand for breathing training products in detail, use the analytic hierarchy process to summarize the multi-level factors that affect the design of children’s breathing training products, and establish a design evaluation level indicator model. Based on the focus group and questionnaire evaluation, the judgment matrix is obtained, the weight value of each evaluation index is calculated, the importance of each index factor is determined, and the design suggestions are finally put forward. Conclusion: The evaluation results show that the most influential factors are the safety and rationality of the structural size, the continuous attractiveness during the treatment process, the ease of operation of the product, the safety of the material and the feedback of the rehabilitation effect. Therefore, when developing and optimizing children's breathing training products, based on the comprehensive consideration of multiple factors in the design evaluation model, the above factors can be considered making the product more suitable for users’ needs.
Event-Triggered Consensus LMB Filter for Distributed Multitarget Tracking

This correspondence investigates the problem of reducing energy and bandwidth consumption of a sensor network for distributed multitarget tracking by the labeled multi-Bernoulli filter. To this end, an event-triggered method is adopted together with a consensus strategy for each sensor node to transmit only Bernoulli components (BCs) that achieve enough information gain. Two message transmission strategies, i.e., joint and independent transmission, are devised. The possibility to separately trigger BCs allows the proposed method to better tradeoff tracking performance versus communication load with respect to existing distributed multitarget tracking approaches.
The Distance Learning Framework for Design-Related Didactic Based on Cognitive Immersive Experience

This research is considered as the transformation of cognitive immersion based on the theory by Liu et al. (2021), to describe the possibilities of immersive learning by distance teaching tools. It provides insights among the importance of distance learning for design education, especially under specific situations such as COVID-19 pandemic. The tools to support distance learning are categorized and discussed, degrees of immersion are compared among formal learning, informal learning and social learning courseware. The methods rely on an extensive secondary research and literature review, aims to transform the theoretical framework of cognitive immersive learning by online-based tools, also provide new thoughts for innovatively teaching design in the future. As the output, we establish a theoretical model based on online tools, also a design framework to help with course design or idea visualization for instructors.
Application of Artificial Intelligence Technology in Digital Image Processing

In the era of rapid development of social science and technology, artificial intelligence technology has gradually matured. At present, it has become one of the most concerned technologies in the world, bringing opportunities and challenges to the development of society. Based on artificial intelligence technology, the development of all walks of life has made certain progress. At the same time, artificial intelligence technology is also applied in all aspects of life, especially in digital image processing, which provides great convenience. Digital image processing is the use of computers to process images, and then realize more valuable applications with the support of artificial intelligence technology. The purpose of this article is to study the application of artificial intelligence technology in digital image processing. Under normal circumstances, the original images are collected from various disturbing objective environments, so there are many uncertain factors. In addition, the imaging tools and storage systems will also bring unnecessary noise, which will reduce the quality of the pictures. Therefore, this article uses artificial intelligence technology to digitally process the image. Based on artificial intelligence technology, this paper mainly studies the application of digital image processing in fingerprint recognition, face recognition and gesture recognition. Recognition mainly includes three parts, first is preprocessing, second is feature extraction, and last is distance judgment. The preprocessing stage is mainly to remove the noise of the image, and then use the different numerical ranges of the foreground and background in the color model space to extract the detailed features, and finally compare the distances for recognition. The experimental research results show that: the most prominent is the face recognition technology of the digital image processing system B, accounting for 45.08%, followed by the digital image processing system C, the most prominent is the gesture recognition technology, accounting for 39.27%, and fingerprint recognition the technology is more than 30% on average. It shows that the application of artificial intelligence technology in digital image processing needs to be improved.
Nonlinear Optimal Feedback Control of the Two-Level Open Non-Markovian Stochastic Quantum System

In this paper, a nonlinear optimal feedback control based on the nonlinear state estimator for a two-level open non-Markovian stochastic quantum system is proposed. The proposed nonlinear state estimator is designed by using the state-dependent differential Riccati equation and constructed to optimally estimate the state. The estimated state is updated by the output data of continuous weak measurement of the controlled quantum system and used to design the nonlinear state feedback controller to track the output of the reference model. The output state of reference model is chosen as the desired performance. The numerical simulation results verify the achievability of the proposed state feedback control strategy, and the capability to steer the state of system from any arbitrary initial state to the final reference target state with high state transfer success rate.
Validation of Simulation-Based Testing: Bypassing Domain Shift with Label-to-Image Synthesis

Many machine learning applications can benefit from simulated data for systematic validation - in particular if real-life data is difficult to obtain or annotate. However, since simulations are prone to domain shift w.r.t. real-life data, it is crucial to verify the transferability of the obtained results.We propose a novel framework consisting of a generative label-to-image synthesis model together with different transferability measures to inspect to what extent we can transfer testing results of semantic segmentation models from synthetic data to equivalent real-life data. With slight modifications, our approach is extendable to, e.g., general multi-class classification tasks. Grounded on the transferability analysis, our approach additionally allows for extensive testing by incorporating controlled simulations. We validate our approach empirically on a semantic segmentation task on driving scenes. Transferability is tested using correlation analysis of IoU and a learned discriminator. Although the latter can distinguish between real-life and synthetic tests, in the former we observe surprisingly strong correlations of 0.7 for both cars and pedestrians.
Pruning CNNs for LiDAR-based Perception in Resource Constrained Environments

Deep neural networks provide high accuracy for perception. However they require high computational power. In particular, LiDAR-based object detection delivers good accuracy and real-time performance, but demands high computation due to expensive feature-extraction from point cloud data in the encoder and backbone networks. We investigate the model complexity versus accuracy trade-off using reinforcement learning based pruning for PointPillars, a recent LiDAR-based 3D object detection network. We evaluate the model on the validation dataset of KITTI (80/20-splits) according to the mean average precision (mAP) for the car class. We prune the original PointPillars model (mAP 89.84) and achieve 65.8% reduction in floating point operations (FLOPs) for a marginal accuracy loss. The compression corresponds to 31.7% reduction in inference time and 35% reduction in GPU memory on GTX 1080 Ti.
Peramalan Cuaca Menggunakan Metode Rantai Markov (Studi Kasus : Cuaca Harian Di Kota Ambon)

Weather is a state of the air at certain times in certain areas that are relatively narrow and in a short period of time. Weather forecasting is an activity carried out to produce a collection of information about upcoming weather conditions. One method used to predict these conditions is by using the Markov chain method. The Markov chain is a random process in which all information about the future is contained in the present state. The purpose of this study is to model and predict daily weather that will occur over the next week with the Markov Chain model. The data used in this study are Ambon City daily weather data from December 1, 2019, to January 31, 2020, by the Meteorology, Climatology, and Geophysics Agency (BMKG), Ambon City Meteorological Station. Daily weather forecasting results that occur are quite varied but the most dominant weather occurs during the next week that is Cloudy and Light Rain.
Contextual and Social Predictors of Scam Susceptibility and Fraud Victimization

Financial fraud targeting older adults is on the rise, with annual losses totaling in the billions of dollars. Prior cross-sectional and qualitative studies have reported that negative life events and social factors, such as poor psychological well-being and loneliness, are significant correlates of fraud, yet there is little research using longitudinal data to show that these social factors and life events precede (versus follow) victimization experiences, and no studies that examine the impact of modifying social variables on the risk of fraud and reducing scam susceptibility. In this study, we use repeated measures from the Rush Memory and Aging Project (MAP) decision making substudy to assess how negative life events and trajectories in social support, well-being, and loneliness affect susceptibility to scams and fraud victimization over the course of the study. Experiencing negative life events was not associated with the risk of self-reported fraud victimization, although negative life events were statistically significantly associated with greater scam susceptibility in unadjusted models. Using a causal inference analysis that simulates the impact of a social support intervention on the risk of fraud over time revealed that higher consistent social support increases the average probability of reporting fraud victimization over the study, contrary to study hypotheses. Although the magnitude of effects are small, consistent interventions that maximize psychological well-being and minimize loneliness significantly reduce average scam susceptibility. Effects are stronger for older adults who are divorced, widowed, or never married relative to those who are married or partnered.