Reviewerly/exHarmony huggingface dataset:
Toward a standard formal semantic representation of the model card report

Model card reports aim to provide informative and transparent description of machine learning models to stakeholders. This report document is of interest to the National Institutes of Health's Bridge2AI initiative to address the FAIR challenges with artificial intelligence-based machine learning models for biomedical research. We present our early undertaking in developing an ontology for capturing the conceptual-level information embedded in model card reports.Sourcing from existing ontologies and developing the core framework, we generated the Model Card Report Ontology. Our development efforts yielded an OWL2-based artifact that represents and formalizes model card report information. The current release of this ontology utilizes standard concepts and properties from OBO Foundry ontologies. Also, the software reasoner indicated no logical inconsistencies with the ontology. With sample model cards of machine learning models for bioinformatics research (HIV social networks and adverse outcome prediction for stent implantation), we showed the coverage and usefulness of our model in transforming static model card reports to a computable format for machine-based processing.The benefit of our work is that it utilizes expansive and standard terminologies and scientific rigor promoted by biomedical ontologists, as well as, generating an avenue to make model cards machine-readable using semantic web technology. Our future goal is to assess the veracity of our model and later expand the model to include additional concepts to address terminological gaps. We discuss tools and software that will utilize our ontology for potential application services.
Automatic macaque brain segmentation based on 7T MRI

In monkey neuroimaging, particularly magnetic resonance imaging (MRI) studies, quick and accurate automatic macaque brain segmentation is essential. However, there are few processing and analysis tools dedicated to automatic brain tissue segmentation and labeling of the macaque brain on a subject-specific basis. As a result, currently most adopted methods are through direct implementation of existing tools that have been designed for human brain. However, the operation steps combining different functional modules of a variety of processing and analysis tool software are inevitably complicated, cumbersome, time-consuming and labor-intensive.In this study, we proposed a novel quick and accurate automatic macaque brain segmentation method based on multi-atlas registration and majority-vote algorithm. First, the single-atlas method based on S-HAMMER is used to register each template image of the reference atlas set (including brain tissue labeled images and brain anatomical structure labeled images) to the preprocessed image to be segmented. Thus, we obtain the corresponding deformation field and spatially transform the labeled image, and then get multiple segmentation results by local weighted voting method, which perform label fusion to obtain the final labeled images of brain structures segmentation result.By collecting high SNR and high spatial resolution images of macaque brain images from our 7T human MRI scanner, we have constructed two brain templates for each individual macaque subject, and macaque brain tissues and brain anatomical structure by one-atlas method. However, segmentation result of single-atlas method is not much accurate in some brain tissue area. It takes about 2 h and need more manual correction for segmentation. Automatic segmentation of macaque brain structure based on multi-atlas method was reasonably successful, the accuracy of segmentation was greatly improved without manual correction. Also, the proposed method provided good tissue fitting to V1 with smooth and continuous boundary. The Dice similarity of multi-atlas method showing 3.24%, 4.24%, 2.55%, 2.85%, 3.05%, and 0.35% improvement in image slices of 63, 66, 70, 71, 99 and 100, respectively. The entire processing time for the construction of a single template map took ~40 min.This study proposed a novel automatic segmentation method of individual macaque brain structure based on multi-atlas registration method, which is concise and reliable. It may offer a valuable tool to applications in the field of brain and neuroscience research using the macaque as an experimental animal model.
TERQA: Question answering over knowledge graph considering precise dependencies of temporal information on vectors

Time questions involve explicit and implicit constraints as well as complex time interval interactions, making them critical criteria for measuring the effect of knowledge base question answering. Although attention to temporal questions has spurred the development of temporal knowledge graphs, existing studies have focused on the simple splicing and fusion of temporal information with question or knowledge base embeddings, losing sight of the hidden interaction features between temporal information and embedded vectors. In this paper, we proposed TERQA, a temporal knowledge base question-answering approach to explore precise spatial dependencies between temporal information and embedded vectors. The exploration of the deep dependency between time and embedded vectors was divided into two stages. In the first stage, the Transformer model of depth extraction was employed to extract richer features from questions and the representation was enhanced with temporal information; in the second stage, high-level capsules were adopted to extract the low-level vector features for detailed pose determination, allowing a more precise deep dependency of temporal facts on embedded vectors. We conducted an experiment using two temporal question answering datasets, TempQuestions and CronQuestions, and the results showed that accuracy for TERQA improved 11.3% from baseline on the dataset TempQuestions with higher annotated information. Additionally, the adapted TERQA also showed varying degrees of improvements over the baseline in the larger but simply annotated dataset CronQuestions.
Fast Image Encryption Algorithm Based on Multiple Chaotic Maps

This paper presents an efficient encryption algorithm based on 3 chaotic systems. Initially, the Bogdanov chaotic system is used to scramble the original image, in order to complicate the cryptanalysis of the cipher, followed by a number of iterations of encryption with tent map. The parameters of each iteration of the tent map encryption are generated by the chaotic logistic map transformation after going through the transient regime (entering stationarity). The key of the algorithm is composed of the individual keys of each chaotic transformation.
Performance Evaluation of a new one-time password (OTP) scheme using stochastic petri net (SPN)

Static password is no more considered as a secure alternative to attain the authentication of the valid user in the internet world. This already opens the door for the attackers to compromise the password because of several vulnerable security attacks. Thus, it needs a secure mechanism which introduces the concept of OTP where password is frequently changed and valid for a certain period. The concept makes the OTP a strong alternative of static password that is widely used now a day. However, now smart phone is convenient and commonly used media to receive OTP generated by the system which is threatened by the man-in-the-middle attack if the channel for transferring the OTP to the user end is not protected. Bearing this shortcoming of existing OTP system, we develop a method where the user will perform a certain intermediate mathematical calculation before using the OTP received by the smart phone and thus, incorporates an extra layer of security in the existing OTP system. This extra layer of security protects the method from usual security attacks whether the channel used for transferring OTP is compromised or not. This new method of OTP has noticeable improvements in performance over the existing system and puts no extra burden in the server-side verification process.
FLITC: A Novel Federated Learning-Based Method for IoT Traffic Classification

Internet of Things (IoT) systems are rightly receiving considerable interest for many real-world applications, from in-body networks to satellite networks. Such a massive-scale system generates a considerable amount of traffic data, making IoT systems a distributed data source generator. For many reasons, such as the functionality of IoT applications and Quality of Service (QoS) provisioning, classifying these traffic data is of high importance. In the last few years, widespread interest has been expressed in applying Machine Learning (ML)-based techniques for Network Traffic Classification (NTC) tasks. However, the traditional centralized learning-based traffic classifiers pose serious challenges, especially in IoT networks. The centralized ML techniques call for collecting a large amount of data from various IoT devices, which in turn introduces data governance and privacy challenges. Furthermore, in the centralized ML, training data need to be transferred to the Cloud, which increases communication cost and latency. To address these problems, we propose Federated Learning (FL) Internet of Things (IoT) Traffic Classifier (FLITC)-a Federated Learning (FL)-based IoT traffic classification method which is based on the Multi-Layer Perception (MLP) neural network and holds the local data unimpaired on IoT devices by sending only the learned parameters to the aggregation server. Our experimental results show that the FLITC beats centralized learning in preserving the privacy of sensitive data and offers a better degree of accuracy at the cost of a longer training time.
Detecting Childcare Activities Using an Off-the-shelf Smartwatch

The childcare environment has significantly changed, owing to accelerating women's social advancement and the increasing number of nuclear families. Improving and supporting childcare have become major challenges in current society. Automatically recording and subsequently observing childcare activities can be used for various purposes to support childcare. However, methods to detect childcare activities using off-the-shelf devices have not yet been proposed. This study develops a method to detect childcare activities that parents perform for their babies using an off-the-shelf wearable device. We define nine childcare activities and develop corresponding detection models based on motion-sensor data from a smartwatch. Our evaluation in a laboratory setting resulted in classification performances of 71% (F1: 0.66).
Scaling up Deep Reinforcement Learning for Intelligent Video Game Agents

We introduce the concept of Deep Reinforcement Learning (DRL) and describe the current state-of-the-art in subareas relevant to the author's research. We present previous and ongoing work done by the author in context of game engines, video game development and Machine Learning performance. We discuss our measurements showing the performance discrepancy between training DRL agents on game engines and end-to-end GPU-based physics simulators. We propose the use of external GPU-based physics simulators and transfer learning to accelerate the training of DRL models for game engines. As future work, we discuss the use of model decomposition in complex environments to further accelerate learning efficiency of DRL in addition to increased hardware utilization.
Online Handwriting Recognition Based on Microphone and IMU

This paper presents a method for handwritten character recognition based on the Inertial Measurement Unit (IMU) and the microphone set on the pen. It realized online handwriting recognition by integrating acceleration data and audio data of handwritten action. In this method, the audio during writing is firstly recognized and located, and the effective acceleration sensing data during writing are filtered by the audio location results. The filtered data sets of strokes and characters are used to train the model for handwriting recognition with neural network learning method. Through the training and testing of 1500 groups (2 * 15 * 50 = 1500) of 15 types of characters, this work realizes the results that the overall recognition accuracy is improved by 8% and the recognition accuracy of several typical characters is improved by 18% compared with the original model, which shows that the algorithm effectively improves the recognition accuracy of some typical characters and also enhances the overall accuracy. The method not only improves the accuracy of handwriting recognition data, but also reduces the amount of data transmitted and processed. It is suitable for the implementation of efficient handwriting recognition in embedded system.
Rating the Severity of Toxic Comments Using BERT-Based Deep Learning Method

With the integration of Internet and smartphones into people’s lives, toxic comments become ubiquitous on various online social media. These comments hinder seriously the construction of a safe and healthy network environment, leading to a great demand for automated methods which can effectively identify such harmful information and deal with it in a timely manner. To address this challenge, we propose a BERT-based deep learning method in this paper to rate the severity of toxic comments. On the basis of the text dataset provided by Jigsaw, BERT-based backbones (RoBERTa and DeBERTa) are trained to extract contextualized embeddings from sentences. After that, corresponding severity scores of comments are calculated by the subsequent head layers, where the head is chosen from the multilayer perceptron, convolutional neural network, and attention structure. After applying the K-Fold cross validation and an average ensemble of different models, our method achieves a rank 28/2301 (top 1.2%) in the leaderboard of Jigsaw Rate Severity of Toxic Comments Kaggle competition. This result can get a silver medal in this competition, and proves that our model can be an effective approach to rate precisely the severity of a toxic comment. This work can remarkably reduce the workload of manual review of Internet content and help build a more harmonious online community environment.
Distribution Transformer Loss-of- Life Assessment in the Presence of Cryptocurrency Mining Loads

By privatization and deregulation, distribution transformers have gradually been pushed further into their operating limits. Under these circumstances, in countries with cheap or subsidized electricity price, the introduction of profitable cryptocurrencies application is deeply penetrated. It has attracted many low-voltage customers to mine these digital currencies individually or in mining pools. The employed mining devices with their own unique constant load profile have escalated the coincident factor of the loads supplied by a transformer. This can highly overload the transformers engaged with loads of this type and, in the long term will cause destructive impacts not only on the transformer also on the distribution facilities. This paper studies the adverse effect of cryptocurrency mining loads on the distribution transformer aging. At first, the customer's profit analysis through mining operation has been introduced, realizing different electric energy pricing strategies. Finally, 105 real-world distribution substations have been monitored over one year, and their actual load profiles data are recorded for analysis. A relation between the penetration rate of the mining devices and the transformer's expected lifetime is determined in this work. It is shown that the distribution transformers' expected lifetime will decrease by 25% in the presence of only a 5% penetration rate of cryptocurrency miners. This is an alarming statistic for distribution networks operating under the presence of cryptocurrency mining loads. This should be strictly addressed in the future planning of these distribution networks.
Attention-based Deep Learning Methods for Document Layout Analysis

research-article Share on Attention-based Deep Learning Methods for Document Layout Analysis Authors: Hao-Yue Sun Xiamen University of Technology, China Xiamen University of Technology, ChinaSearch about this author , Ying Zhong Xiamen University of Technology, China Xiamen University of Technology, ChinaSearch about this author , Da-Han Wang Xiamen University of Technology, China Xiamen University of Technology, ChinaSearch about this author Authors Info & Claims ICCAI '22: Proceedings of the 8th International Conference on Computing and Artificial IntelligenceMarch 2022 Pages 32–37https://doi.org/10.1145/3532213.3532219Online:13 July 2022Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
LR-SGD: Layer-based Random SGD For Distributed Deep Learning

Distributed training is an effective way to accelerate the training of a large-scale deep neural network(DNN) model, while the data communication between computational workers is always a bottleneck because of the frequent parameter communication at each iteration. To alleviate this problem, many communication-efficient distributed training algorithms based on synchronized stochastic gradient descent(SSGD) are proposed to reduce the amount of the exchanged data by compressing or quantizing the gradients such as the top-k SGD, TernGrad. However, these methods introduce extra intensive computation because of the frequent operation on tensor and memory accessing. Especially when the faster communication backends such as nccl(i.e, NVIDIA Collective Communications Library) or faster communication hardware is proffered in the near future, they no longer have a time advantage. In this paper, we propose an efficient sparsification method, layer-based random SGD (LR-SGD), that randomly select a certain number of layers of the DNN model to be exchanged instead of some elements of each tensor, which reduces communication while keep the performance close to the SSGD. Particularly, we use a hyper parameter k (i.e., indicates the number of layers to be selected) to adjust the compressing ratio while two probabilistic models are utilized to select the layers being exchanged. To validate the proposed method, we conduct several experiments on different datasets with two different scale DNN models on a stimulative cluster. The results demonstrate that the layer-based random sparsification method effectively reduce the communication overhead while maintain the high accuracy. Our code is available at https://github.com/prototype-zzy/LR-SGD.
An Approach Based on Linked Open Data and Augmented Reality for Cultural Heritage Content-Based Information Retrieval

Nowadays, many technologies are changing our way of life, including those related to extended reality. One of the most interesting is Augmented Reality (AR). Today, even if this technology seems to be discovered yet, it is widely applied in several contexts, including the fruition and conservation of cultural heritage. Such spread is mainly offered by the new and more powerful mobile devices, allowing museums and art exhibitions to use AR to offer new experiences to visitors. In this paper, we present an augmented reality mobile system based on content-based image analysis techniques and Linked Open Data to improve the user knowledge about cultural heritage. We use different image analysis techniques, and we present several experimental results to show the performance of our system.
Voice Gender Recognition Using Acoustic Features, MFCCs and SVM

This paper presents a voice gender recognition system. Acoustic features and Mel-Frequency Cepstral Coefficients (MFCCs) are extracted to define the speaker's gender. The most used features in these kinds of studies are acoustic features, but in this work, we combined them with MFCCs to test if we will get more satisfactory results. To examine the performance of the proposed system we tried four different databases: the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), the Saarbruecken Voice Database (SVD), the CMU_ARCTIC database and the Amazigh speech database (Self-Created). At the pre-processing stage, we removed the silence from the signals by using Zero-Crossing Rate (ZCR), but we kept the noises. Support Vector Machine (SVM) is used as the classification model. The combination of acoustic features and MFCCs achieves an average accuracy of 90.61% with the RAVDESS database, 92.73% with the SVD database, 99.87% with the CMU_ARCTIC database and 99.95% with the Amazigh speech database.
Video Transcription and Summarization using NLP

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Deepfake Detection Using Machine Learning Algorithms

Deepfake, a new video manipulation technique, has drawn much attention recently. Among the unlawful or nefarious applications, Deepfake has been used for spreading misinformation, fomenting political discord, smearing opponents, or even blackmailing. As the technology becomes more sophisticated and the apps for creating them ever more available, detecting Deepfake has become a challenging task, and accordingly researchers have proposed various deep learning (DL) methods for detection. Though the DL-based approach can achieve good solutions, this paper presents the results of our study indicating that traditional machine learning (ML) techniques alone can obtain superior performance in detecting Deepfake. The ML-based approach is based on the standard methods of feature development and feature selection, followed by training, tuning, and testing an ML classifier. The advantage of the ML approach is that it allows better understandability and interpretability of the model with reduced computational cost. We present results on several Deepfake datasets that are obtained relatively fast with comparable or superior performance to the state-of-the-art DL-based methods: 99.84&#x0025; accuracy on FaceForecics++, 99.38&#x0025; accuracy on DFDC, 99.66&#x0025; accuracy on VDFD, and 99.43&#x0025; on Celeb-DF datasets. Our results suggest that an effective system for detecting Deepfakes can be built using traditional ML methods.
Risk Management of Silent Cyber Risks in Consideration of Emerging Risks

In recent years, new cyber attacks such as targeted attacks have caused extensive damage. With the continuing development of the IoT society, various devices are now connected to the network and are being used for various purposes. The Internet of Things has the potential to link cyber risks to actual property damage, as cyberspace risks are connected to physical space. With this increase in unknown cyber risks, the demand for cyber insurance is increasing. One of the most serious emerging risks is the silent cyber risk, and it is likely to increase in the future. However, at present, security measures against silent cyber risks are insufficient. In this study, we conducted a risk management of silent cyber risk for organizations with the objective of contributing to the development of risk management methods for new cyber risks that are expected to increase in the future. Specifically, we modeled silent cyber risk by focusing on state transitions to different risks. We newly defined two types of silent cyber risk, namely, Alteration risk and Combination risk, and conducted risk assessment. Our assessment identified 23 risk factors, and after analyzing them, we found that all of them were classified as Risk Transference. We clarified that the most effective risk countermeasure for Alteration risk was insurance and for Combination risk was measures to reduce the impact of the risk factors themselves. Our evaluation showed that the silent cyber risk could be reduced by about 50&#x0025;, thus demonstrating the effectiveness of the proposed countermeasures.