Reviewerly/exHarmony huggingface dataset:
Transfer Learning based Search Space Design for Hyperparameter Tuning

The tuning of hyperparameters becomes increasingly important as machine learning (ML) models have been extensively applied in data mining applications. Among various approaches, Bayesian optimization (BO) is a successful methodology to tune hyper-parameters automatically. While traditional methods optimize each tuning task in isolation, there has been recent interest in speeding up BO by transferring knowledge across previous tasks. In this work, we introduce an automatic method to design the BO search space with the aid of tuning history from past tasks. This simple yet effective approach can be used to endow many existing BO methods with transfer learning capabilities. In addition, it enjoys the three advantages: universality, generality, and safeness. The extensive experiments show that our approach considerably boosts BO by designing a promising and compact search space instead of using the entire space, and outperforms the state-of-the-arts on a wide range of benchmarks, including machine learning and deep learning tuning tasks, and neural architecture search.
Sistem Pakar Untuk Mendiagnosa Penyakit Mata Menerapkan Metode Certainty Factor dan Forward Chaining

Disorders of the eye become a disease that is often experienced. There is a new symptom model found, causing confusion that occurs. Diseases of the eye often attack humans, especially in the current era of digital technology, because they spend more time staring at cell phone or computer screens. So the application of this expert system is made to provide solutions for the community to diagnose the symptoms that are often experienced. In making this application, the forward chaining method and certainty factor are used in the application built and comparison with the combined forward chaining and naive Bayes method. The test is carried out by testing 40 data samples based on existing symptoms, carried out by the system and also manually to see the accuracy of the values given. The results given are that there is a fairly high level of certainty in the diagnosis of hypermetropia with a certainty value of 88.43% and the smallest certainty value in the diagnosis of presbyopia with a value of 61.702% of the total testing obtained a probability level of certainty ranging from 51%-79%.
Traffic Refinery

Network management often relies on machine learning to make predictions about performance and security from network traffic. Often, the representation of the traffic is as important as the choice of the model. The features that the model relies on, and the representation of those features, ultimately determine model accuracy, as well as where and whether the model can be deployed in practice. Thus, the design and evaluation of these models ultimately requires understanding not only model accuracy but also the systems costs associated with deploying the model in an operational network. Towards this goal, this paper develops a new framework and system that enables a joint evaluation of both the conventional notions of machine learning performance (model accuracy) and the systems-level costs of different representations of network traffic. We highlight these two dimensions for two practical network management tasks, video streaming quality inference and malware detection, to demonstrate the importance of exploring different representations to find the appropriate operating point. We demonstrate the benefit of exploring a range of representations of network traffic and present Traffic Refinery, a proof-of-concept implementation that both monitors network traffic at 10~Gbps and transforms traffic in real time to produce a variety of feature representations for machine learning. Traffic Refinery both highlights this design space and makes it possible to explore different representations for learning, balancing systems costs related to feature extraction and model training against model accuracy.
Enhancing Location Privacy through P2P Network and Caching in Anonymizer

The fear that location privacy may be compromised greatly hinders the development of location-based service.Accordingly, some schemes based on the distributed architecture in peer-to-peer network for location privacy protection are proposed.Most of them assume that mobile terminals are mutually trusted, but this does not conform to realistic scenes, and they cannot make requirements for the level of location privacy protection.Therefore, this paper proposes a scheme for location attribute-based security authentication and private sharing data group, so that they trust each other in peer-to-peer network and the trusted but curious mobile terminal cannot access the initiator's query request.A new identifier is designed to allow mobile terminals to customize the protection strength.In addition, the caching mechanism is introduced considering the cache capacity, and a cache replacement policy based on deep reinforcement learning is proposed to reduce communications with location-based service server for achieving location privacy protection.Experiments show the effectiveness and efficiency of the proposed scheme.
“My nose is running.” “Are you also coughing?”: Building A Medical Diagnosis Agent with Interpretable Inquiry Logics

With the rise of telemedicine, the task of developing Dialogue Systems for Medical Diagnosis (DSMD) has received much attention in recent years. Different from early researches that needed to rely on extra human resources and expertise to build the system, recent researches focused on how to build DSMD in a data-driven manner. However, the previous data-driven DSMD methods largely overlooked the system interpretability, which is critical for a medical application, and they also suffered from the data sparsity issue at the same time. In this paper, we explore how to bring interpretability to data-driven DSMD. Specifically, we propose a more interpretable decision process to implement the dialogue manager of DSMD by reasonably mimicking real doctors' inquiry logics, and we devise a model with highly transparent components to conduct the inference. Moreover, we collect a new DSMD dataset, which has a much larger scale, more diverse patterns, and is of higher quality than the existing ones. The experiments show that our method obtains 7.7%, 10.0%, 3.0% absolute improvement in diagnosis accuracy respectively on three datasets, demonstrating the effectiveness of its rational decision process and model design. Our codes and the GMD-12 dataset are available at https://github.com/lwgkzl/BR-Agent.
Few-Shot Text Classification with Global–Local Feature Information

Meta-learning frameworks have been proposed to generalize machine learning models for domain adaptation without sufficient label data in computer vision. However, text classification with meta-learning is less investigated. In this paper, we propose SumFS to find global top-ranked sentences by extractive summary and improve the local vocabulary category features. The SumFS consists of three modules: (1) an unsupervised text summarizer that removes redundant information; (2) a weighting generator that associates feature words with attention scores to weight the lexical representations of words; (3) a regular meta-learning framework that trains with limited labeled data using a ridge regression classifier. In addition, a marine news dataset was established with limited label data. The performance of the algorithm was tested on THUCnews, Fudan, and marine news datasets. Experiments show that the SumFS can maintain or even improve accuracy while reducing input features. Moreover, the training time of each epoch is reduced by more than 50%.
4D dual-mode staring hyperspectral-depth imager for simultaneous spectral sensing and surface shape measurement

A 4D dual-mode staring hyperspectral-depth imager (DSHI), which acquire reflectance spectra, fluorescence spectra, and 3D structural information by combining a staring hyperspectral scanner and a binocular line laser stereo vision system, is introduced. A 405 nm laser line generated by a focal laser line generation module is used for both fluorescence excitation and binocular stereo matching of the irradiated line region. Under the configuration, the two kinds of hyperspectral data collected by the hyperspectral scanner can be merged into the corresponding points in the 3D model, forming a dual-mode 4D model. The DSHI shows excellent performance with spectral resolution of 3 nm, depth accuracy of 26.2 µm. Sample experiments on a fluorescent figurine, real and plastic sunflowers and a clam are presented to demonstrate system’s with potential within a broad range of applications such as, e.g., digital documentation, plant phenotyping, and biological analysis.
EDITnet: A Lightweight Network for Unsupervised Domain Adaptation in Speaker Verification

Performance degradation caused by language mismatch is a common problem when applying a speaker verification system on speech data in different languages. This paper proposes a domain transfer network, named EDITnet, to alleviate the language-mismatch problem on speaker embeddings without requiring speaker labels. The network leverages a conditional variational auto-encoder to transfer embeddings from the target domain into the source domain. A self-supervised learning strategy is imposed on the transferred embeddings so as to increase the cosine distance between embeddings from different speakers. In the training process of the EDITnet, the embedding extraction model is fixed without fine-tuning, which renders the training efficient and low-cost. Experiments on Voxceleb and CN-Celeb show that the embeddings transferred by EDITnet outperform the un-transferred ones by around 30% with the ECAPA-TDNN512. Performance improvement can also be achieved with other embedding extraction models, e.g., TDNN, SE-ResNet34.
The opacity myth: A response to Swofford &amp; Champod (2022)

Swofford & Champod (2022) FSI Synergy article 100220 reports the results of semi-structured interviews that asked interviewees their views on probabilistic evaluation of forensic evidence in general, and probabilistic evaluation of forensic evidence performed using computational algorithms in particular. The interview protocol included a leading question based on the premise that machine-learning methods used in forensic inference are not understandable even to those who develop those methods. We contend that this is a false premise.
BDESF-ITS: Blockchain-Based Secure Data Exchange and Storage Framework for Intelligent Transportation System

The communication in Intelligent Transportation Systems (ITS) suffers from various security and privacy related issues as several attacks, like replay, man-in-the-middle (MiTM), impersonation, data leakage and unauthorised data update attacks can be mounted by an adversary. Therefore, we need a robust security mechanism to sort out such issues, which can be further enhanced through the use of mechanism of blockchain technology. In this paper, we propose a blockchain-based secure data exchange and storage framework for the ITS (in short, we call it as BDESF-ITS). We conduct the security analysis of the BDESF-ITS to prove its robustness against various possible attacks. The practical implementation of the BDESF-ITS is also carried out to observe its behaviour with the real world settings.
Regulatory and Methodological Support for the Mixed Reality Technology in Education

This article analyzes the possibilities of using the mixed reality technology in education. The authors review the literature on the augmented reality technologies that can provide benefits compared to other technical solutions used in education at the moment.
Overcoming Vocabulary-Related Anxieties in Students When Communicating in the Media Internationally

&lt;p style="text-align:justify"&gt;The purpose of the study was to identify how the designed instructional model moderated the vocabulary-related anxieties in students when they communicated in the media internationally. The methodological approach used in the study was aimed at controlling and manipulating variables which were as follows: The level of vocabulary anxiety related to communication in the media, cultural intelligence, and English vocabulary level. The study relied on quantitative and qualitative methods to gather the experimental data and answer the research questions. Insufficient vocabulary was found to be one of the key factors of anxieties in students when they communicated in the media internationally. The major sub-factors of the vocabulary-related anxieties in the students when they communicated in the media internationally (as perceived by the respondents) were spontaneous interaction with the native speakers, guessing the meaning of the words from the context, and paraphrasing ideas using synonyms. The instructional-purpose use of the predesigned vocabulary lists, learning them using the Telegram bot, talking to the foreign speakers in the media, self-analysis, and analysis by peers were proven to be capable to moderate the vocabulary-related anxieties in students when they communicate in the media with the foreign speakers.&lt;/p&gt;
Sequential Intention-aware Recommender based on User Interaction Graph

The next-item recommendation problem has received more and more attention from researchers in recent years. Ignoring the implicit item semantic information, existing algorithms focus more on the user-item binary relationship and suffer from high data sparsity. Inspired by the fact that user's decision-making process is often influenced by both intention and preference, this paper presents a SequentiAl inTentiOn-aware Recommender based on a user Interaction graph (Satori). In Satori, we first use a novel user interaction graph to construct relationships between users, items, and categories. Then, we leverage a graph attention network to extract auxiliary features on the graph and generate the three embeddings. Next, we adopt self-attention mechanism to model user intention and preference respectively which are later combined to form a hybrid user representation. Finally, the hybrid user representation and previously obtained item representation are both sent to the prediction modul to calculate the predicted item score. Testing on real-world datasets, the results prove that our approach outperforms state-of-the-art methods.
FMFCC-V: An Asian Large-Scale Challenging Dataset for DeepFake Detection

The abuse of DeepFake technique has raised enormous public concerns in recent years. Currently, the existing DeepFake datasets suffer some weaknesses of obvious visual artifacts, minimal Asian proportion, backward synthesis methods and short video length. To make up these weaknesses, we have constructed an Asian large-scale challenging DeepFake dataset to enable the training of DeepFake detection models and organized the accompanying video track of the first Fake Media Forensics Challenge of China Society of Image and Graphics (FMFCC-V). The FMFCC-V dataset is by far the first and the largest public available Asian dataset for DeepFake detection, which contains 38102 DeepFake videos and 44290 pristine videos, corresponding more than 23 million frames. The source videos in the FMFCC-V dataset are carefully collected from 83 paid individuals and all of them are Asians. The DeepFake videos are generated by four of the most popular face swapping methods. Extensive perturbations are applied to obtain a more challenging benchmark of higher diversity. The FMFCC-V dataset can lend powerful support to the development of more effective DeepFake detection methods. We contribute a comprehensive evaluation of six representative DeepFake detection methods to demonstrate the level of challenge posed by FMFCC-V dataset. Meanwhile, we provide a detailed analysis of the top submissions from the FMFCC-V competition.
CHEF: A Pilot Chinese Dataset for Evidence-Based Fact-Checking

The explosion of misinformation spreading in the media ecosystem urges for automated factchecking.While misinformation spans both geographic and linguistic boundaries, most work in the field has focused on English.Datasets and tools available in other languages, such as Chinese, are limited.In order to bridge this gap, we construct CHEF, the first CHinese Evidence-based Fact-checking dataset of 10K real-world claims.The dataset covers multiple domains, ranging from politics to public health, and provides annotated evidence retrieved from the Internet.Further, we develop established baselines and a novel approach that is able to model the evidence retrieval as a latent variable, allowing jointly training with the veracity prediction model in an end-to-end fashion.Extensive experiments show that CHEF will provide a challenging testbed for the development of fact-checking systems designed to retrieve and reason over non-English claims.Source code and data are available 1 .
Sentiment Analysis of Image with Text Caption using Deep Learning Techniques

People are actively expressing their views and opinions via the use of visual pictures and text captions on social media platforms, rather than just publishing them in plain text as a consequence of technical improvements in this field. With the advent of visual media such as images, videos, and GIFs, research on the subject of sentiment analysis has expanded to encompass the study of social interaction and opinion prediction via the use of visuals. Researchers have focused their efforts on understanding social interaction and opinion prediction via the use of images, such as photographs, films, and animated GIFs (graphics interchange formats). The results of various individual studies have resulted in important advancements being achieved in the disciplines of text sentiment analysis and image sentiment analysis. It is recommended that future studies investigate the combination of picture sentiment analysis and text captions in more depth, and further research is necessary for this field. An intermodal analysis technique known as deep learning-based intermodal (DLBI) analysis is discussed in this suggested study, which may be used to show the link between words and pictures in a variety of scenarios. It is feasible to gather opinion information in numerical vector form by using the VGG network. Afterward, the information is transformed into a mapping procedure. It is necessary to predict future views based on the information vectors that have been obtained thus far, and this is accomplished through the use of active deep learning. A series of simulation tests are being conducted to put the proposed mode of operation to the test. When we look at the findings of this research, it is possible to infer that the model outperforms and delivers a better solution with more accuracy and precision, as well as reduced latency and an error rate, when compared to the alternative model (the choice).
An Organizational Model of Online Learning in the Pandemic Period: Comparison with Traditional Face-to-Face Learning

The COVID-19 pandemic has led to a paradigm shift in educational systems. During the lockdown, higher education became digital. This caused a change in communication within the educational ecosystem. Relational coordination (RC) is a communication and relationship model associated with the improvement of organizational results. Therefore, the objective of this research is to build an organizational model of online learning applied during the pandemic period and compare it with the previous face-to-face learning. A sample of 2774 students from two Ecuadorian universities was selected. A two-stage methodology was applied: First, an organizational model of online learning was built using multivariate methods. The RC model was linked to student satisfaction using generalized linear models (GLM). In the second stage, the organizational differences between the 2018 face-to-face and the 2020 online learning were identified. Finally, the online learning model was validated with external data. The components associated with a higher level of RC were institutional cooperation, institutional problem-solving, and administrative communication. Administrative communication lost importance in the online model. Significant differences between the satisfaction of the two models were not found. Nevertheless, since online learning was not associated with an improvement in satisfaction, the creation of a third educational model that combines the best practices of online and face-to-face learning in a hybrid system could be an alternative that improves the satisfaction of students.
ACDSE: A Design Space Exploration Method for CNN Accelerator based on Adaptive Compression Mechanism

Customized accelerators for Convolutional Neural Network (CNN) can achieve better energy efficiency than general computing platforms. However, the design of a high-performance accelerator should take into account a variety of parameters and physical constraints. The increasing parameters and tighter constraints gradually complicate the design space, which poses new challenges to the capacity and efficiency of design space exploration methods. In this paper, we provide a novel design space exploration method named ACDSE for optimizing the design process of CNN accelerators. ACDSE implements the adaptive compression mechanism to dynamically adjust the search range and prune low-value design points according to the exploration states. As a result, it can focus on valuable subspace while also improving exploration capacity and efficiency. Additionally, we implement ACDSE to address the problem of CNN accelerator latency optimization. The experiment indicates that, compared to former DSE methods, ACDSE can reduce latency and increase efficiency by 1.39x-5.07x and 2.07x-43.87x, respectively, under the most stringent constraint conditions, demonstrating its superior adaptability to the complicated design space.
Validation of a TAM Technology Acceptance Model in Dominican University Students

In the context of the virtualization of university education due to the Covid-19 pandemic, we conducted a study to establish the determinants of the intention to use the virtual classroom, following a theoretical model based on the Technology Acceptance Model modified by Park (2009) and including the factors Attitude, Perceived Usefulness, Perceived Ease, Virtual Self-Efficacy, Subjective Norm and Accessibility of the system. The sample consisted of 1260 self-selected students from 13 Dominican universities. To check the validity and reliability of the measurement instrument, we performed a Confirmatory Factor Analysis, which determined that the System Accessibility factor had to be eliminated because it was based on a single item that did not have discriminatory validity. One item was eliminated from the Subjective Norm factor to bring it to an acceptable reliability. In general, the validity of the instrument was sustained, but its fit indices with the theoretical model could be improved. With the Multiple Mediation Analysis, we were able to verify that the Subjective Norm, the social factor, was the factor with the most significant statistical influence on the Intention to Use the virtual classroom, both directly and indirectly. Indirectly, the Subjective Norm was mediated by Perceived Usefulness and Attitude. The other factor that had a significant influence, both directly and indirectly, on Intention to Use was Virtual Self-efficacy. Indirectly, this Self-efficacy was mediated by Perceived Ease, Perceived Usefulness and Attitude.