Reviewerly/exHarmony huggingface dataset:
Comparison of Non-AI and AI-Enabled M-Health Platforms for COVID-19 Self Screening in Indonesia

This study aimed to analyze and differentiate the role of AI and no AI-supported m-health platforms for COVID-19 self-screening in Indonesia. We utilized a mysterious shopping method to develop four standardized cases with various severity levels of COVID-19 tested in Indonesia’s most popular mHealth platforms. We selected seven apps from the top 200 free mHealth apps in the “Medical” category in the Google Play Store equipped with COVID-19 symptom checkers. A total of 36 teleconsultations were performed in four chatbot-based, two apps supported with AI combined with a human-based approach, and three apps with the human-based process. Teleconsultations were recorded, classified, and analyzed compared with the COVID-19 guideline by the MoH of Indonesia. The study indicated that most of the self-screening provided questions that had consistently led to the COVID-19 condition such as cough, fever, and shortness of breath and followed the guideline from the national health authority.
Video Captioning based on Image Captioning as Subsidiary Content

Video captioning is the more heuristic task of the combination of computer vision and Natural language processing while researchers are concentrated more in video related tasks. Dense video captioning is still considering the more challenging task as it needs to consider every event occurs in the video and provide optimal captions separately for all the events presents in the video with high diversity. Captioning process with less corpus leads to less performance. To avoid such issues, our proposed model constructed with the option of generating captions with high diversity. Image captions are taken as subsidiary content to enlarge the diversity for captioning the videos. Attention mechanism is utilized for the generation process. Generator and three different discriminators are utilized to contribute an appropriate caption which enriches the captioning process. ActivityNet caption dataset is used to demonstrate the proposed model. Microsoft coco image dataset is considered as subsidiary content for captioning. The benchmark metrics BLEU and METEOR are used to estimate the performance of the proposed model.
The Saudi Novel Corpus: Design and Compilation

Arabic has recently received significant attention from corpus compilers. This situation has led to the creation of many Arabic corpora that cover various genres, most notably the newswire genre. Yet, Arabic novels, and specifically those authored by Saudi writers, lack the sufficient digital datasets that would enhance corpus linguistic and stylistic studies of these works. Thus, Arabic lags behind English and other European languages in this context. In this paper, we present the Saudi Novels Corpus, built to be a valuable resource for linguistic and stylistic research communities. We specifically present the procedures we followed and the decisions we made in creating the corpus. We describe and clarify the design criteria, data collection methods, process of annotation, and encoding. In addition, we present preliminary results that emerged from the analysis of the corpus content. We consider the work described in this paper as initial steps to bridge the existing gap between corpus linguistics and Arabic literary texts. Further work is planned to improve the quality of the corpus by adding advanced features.
A cognitive and neural network approach for software defect prediction

Software defect prediction is used to assist developers in finding potential defects and allocating their testing efforts as the scale of software grows. Traditional software defect prediction methods primarily concentrate on creating static code metrics that are fed into machine learning classifiers to predict defects in the code. To achieve the desired classifier performance, appropriate design decisions are required for deep neural network (DNN) and convolutional neural network (CNN) models. This is especially important when predicting software module fault proneness. When correctly identified, this could help to reduce testing costs by concentrating efforts on the modules that have been identified as fault prone. This paper proposes a CONVSDP and DNNSDP (cognitive and neural network) approach for predicting software defects. Python Programming Language with Keras and TensorFlow was used as the framework. From three NASA system datasets (CM1, KC3, and PC1) selected from PROMISE repository, a comparative analysis with machine learning algorithms (such as Random Forest (RF), Decision Trees (DT), Nave Bayes (NF), and Support Vector Machine (SVM) in terms of F-Measure (known as F1-score), Recall, Precision, Accuracy, Receiver Operating Characteristics (ROC) and Area Under Curve (AUC) has been presented. We extract four dataset attributes from the original datasets and use them to estimate the development effort, development time, and number of errors. The number of operands, operators, branch count, and executable LOCs are among these attributes. Furthermore, a new parameter called cognitive weight (Wc) of Basic Control Structure (BCS) is proposed to make the proposed cognitive technique more effective, and a cognitive data set of 8 features for NASA system datasets (CM1, KC3, and PC1) selected from the PROMISE repository to predict software defects is created. The experimental results showed that the CONVSDP and DNNSDP models was comparable to existing classifiers in both original datasets and cognitive data sets, and that it outperformed them in most of the experiments.
Integrating Artificial Intelligence in Scientific Practice: Explicable AI as an Interface

Abstract A recent article by Herzog provides a much-needed integration of ethical and epistemological arguments in favor of explicable AI (XAI) in medicine. In this short piece, I suggest a way in which its epistemological intuition of XAI as “explanatory interface” can be further developed to delineate the relation between AI tools and scientific research.
An Improved BP Neural Network Algorithm for the Evaluation System of Innovation and Entrepreneurship Education in Colleges and Universities

The ability of college students has important index to evaluate the training quality of universities. Domestic scholars begin to cultivate spirit and ability of students. With the rapid development of tourism industry and the continuous emergence of “tourism +” new business forms, there is an urgent demand for professionals with outstanding innovation and entrepreneurship ability. China’s education field urgently needs a system that can scientifically evaluate the teaching quality. The purpose of this topic is to enrich the theoretical methods of universities. Taking S University as the research sample, the relevant evaluation index system is set up, and, on this basis, the evaluation model of network is established, providing relevant basis for evaluation and cultivation of universities. According to certain evaluation indicators, this paper constructs the main framework of teaching quality evaluation in colleges and universities. 7 representative universities in China are randomly selected, 6 of which are samples and 1 university is the research target, MATLAB is used to calculate the scores of each index, the current situation of quality in a university is analyzed, and corresponding improvements opinion is proposed. Based on the analysis of the current education in a university, it is found that, in the current education, innovation and entrepreneurship knowledge and professional knowledge are taken into account, and the academic achievements are remarkable, forming a preliminary education system, but it is also found that there are some problems of low educational practicality, and corresponding suggestions for this problem are put forward. If the evaluation system is put into practical application, it will improve the education level of cultivating innovative entrepreneurial talents in tourism major in universities.
Lightweight Target-Aware Attention Learning Network-Based Target Tracking Method

Siamese network trackers based on pre-trained depth features have achieved good performance in recent years. However, the pre-trained depth features are trained in advance on large-scale datasets, which contain feature information of a large number of objects. There may be a pair of interference and redundant information for a single tracking target. To learn a more accurate target feature information, this paper proposes a lightweight target-aware attention learning network to learn the most effective channel features of the target online. The lightweight network uses a designed attention learning loss function to learn a series of channel features with weights online with no complex parameters. Compared with the pre-trained features, the channel features with weights can represent the target more accurately. Finally, the lightweight target-aware attention learning network is unified into a Siamese tracking network framework to implement target tracking effectively. Experiments on several datasets demonstrate that the tracker proposed in this paper has good performance.
Human–robot interface design – the ‘Robot with a Tablet’ or ‘Robot only’, which one is better?

ABSTRACTABSTRACTThe new era of integrating robots with artificial intelligence and the Internet of Things has provided huge potential for service robots to play important roles in our daily lives. Therefore, it is interesting to investigate whether a robot should have a separate device to incorporate digital content or use its face to toggle between facial expressions and digital content. From the human–robot interface (HRI) design perspective, a robot with a tablet obviously is closer to a real human teacher using a tablet while teaching students in a classroom. However, limited research has been done to explore such HRI design and its impacts on human behaviours. A robot-facilitated storytelling learning system was developed in this study to evaluate the impact of HRI design on college students' learning behaviours in two different conditions: 'Robot with Tablet' or 'Robot only'. The results revealed that both learning conditions of HRI design benefited college students from knowledge acquisition of vocabulary and comprehension of story content. Furthermore, 'Robot only' which is more cost-effective was found to be as good as that of 'Robot with Tablet' for the students in terms of knowledge acquisition, cognitive load and learning fatigue.KEYWORDS: Cognitive loadhuman–robot interfaceknowledge acquisitionlearning fatiguerobot-facilitated learning Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis research was supported in part by the Ministry of Science and Technology, Taiwan under project numbers MOST 109-2511-H-224-001-MY2, MOST-109-2511-H-036-001-MY2, MOST-109-2511-H-003-053-MY3 and MOST-108-2511-H-003-061-MY3. This work was financially supported by the 'Institute for Research Excellence in Learning Sciences' of National Taiwan Normal University (NTNU) from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project and Tatung University (TTU) from the project number B111-N04-028 by the Ministry of Education (MOE) in Taiwan.
Learning to Overcome Noise in Weak Caption Supervision for Object Detection

We propose the first mechanism to train object detection models from weak supervision in the form of captions at the image level. Language-based supervision for detection is appealing and inexpensive: many blogs with images and descriptive text written by human users exist. However, there is significant noise in this supervision: captions do not mention all objects that are shown, and may mention extraneous concepts. We first propose a technique to determine which image-caption pairs provide suitable signal for supervision. We further propose several complementary mechanisms to extract image-level pseudo labels for training from the caption. Finally, we train an iterative weakly-supervised object detection model from these image-level pseudo labels. We use captions from four datasets (COCO, Flickr30K, MIRFlickr1M, and Conceptual Captions) whose level of noise varies. We evaluate our approach on two object detection datasets. Weighting the labels extracted from different captions provides a boost over treating all captions equally. Further, our primary proposed technique for inferring pseudo labels for training at the image level, outperforms alternative techniques under a wide variety of settings. Both techniques generalize to datasets beyond the one they were trained on.
Automatic and Fast Extraction of 3D Hand Measurements using a Deep Neural Network

Recent advancements in 3D scanning technologies enable us to acquire the hand geometry represented as a three-dimensional point cloud. Providing accurate 3D hand scanning and accurately extracting its biometrics are of crucial importance for a number of applications in medical sciences, fashion industry, augmented and virtual reality (AR/VR). Traditional methods for hand measurement extraction require manual intervention using a measuring tape, which is time-consuming and highly dependent on the operator’s expertise. In this paper, we propose, to the best of our knowledge, the first deep neural network for automatic hand measurement extraction from a single 3D scan (H-Net). The proposed network follows an encoder-decoder architecture design, taking a point cloud of the hand as input and outputting the reconstructed hand mesh as well as the corresponding measurement values. In order to train the proposed deep model, a novel synthetic dataset of hands in various shapes and poses and their corresponding measurements is proposed. Experimental results on both synthetic data and real scans captured by Occipital Mark I structure sensor demonstrate that the proposed method outperforms the state-of-the-art methods in terms of accuracy and speed.
A Large-Scale Bitcoin Abuse Measurement and Clustering Analysis Utilizing Public Reports

Cryptocurrency abuse has become a critical problem. Due to the anonymous nature of cryptocurrency, criminals commonly adopt cryptocurrency for trading drugs and deceiving people without revealing their identities. Despite its significance and severity, only few works have studied how cryptocurrency has been abused in the real world, and they only provide some limited measurement results. Thus, to provide a more in-depth understanding on the cryptocurrency abuse cases, we present a large-scale analysis on various Bitcoin abuse types using 200,507 real-world reports collected by victims from 214 countries. We scrutinize observable abuse trends, which are closely related to real-world incidents, to understand the causality of the abuses. Furthermore, we investigate the semantics of various cryptocurrency abuse types to show that several abuse types overlap in meaning and to provide valuable insight into the public dataset. In addition, we delve into abuse channels to identify which widely-known platforms can be maliciously deployed by abusers following the COVID-19 pandemic outbreak. Consequently, we demonstrate the polarization property of Bitcoin addresses practically utilized on transactions, and confirm the possible usage of public report data for providing clues to track cyber threats. We expect that this research on Bitcoin abuse can empirically reach victims more effectively than cybercrime, which is subject to professional investigation.
Online Education Satisfaction Assessment Based on Machine Learning Model in Wireless Network Environment

With the development of wireless network technology, the transformation of educational concepts, the upgrading of users' educational needs, and the transformation of lifestyles, online education has made great strides forward. However, due to the rapid growth of online education in my country, many regulatory systems have not kept pace with the development of online education, resulting in low user experience and satisfaction with online education. The establishment of a user satisfaction model is beneficial for attracting attention and thinking about research in the field of online education service quality, assisting enterprises in recognizing the specific impact of various factors in services, accelerating service quality improvement, and assisting in the formulation of industry norms and improving enterprise competitiveness, all of which help students acquire knowledge more easily. In the era of big data, traditional satisfaction evaluation methods have many drawbacks, so more and more machine learning methods are applied to satisfaction evaluation models. This paper takes the research of machine learning algorithm as the core to carry out the research work, uses the cost-sensitive idea to improve the decision tree, considers the cost of different types of classification errors, and uses the random forest principle to integrate the generated decision tree, thereby improving the accuracy of the model. The model has better stability, and the validity of the model is verified by experiments. For a follow-up in-depth investigation of online education satisfaction rating technology, the linked work of this paper has certain reference and reference value.
Designing a Choreographic Interface During COVID-19

In 2019, metaLAB (at) Harvard began work on Curatorial A(i)gents, a digital exhibition that was slated to premiere at the Harvard Art Museums’ Lightbox Gallery in 2020. Half of the projects would be interactive, using mouse and keyboard conventions. With the advent of Covid-19 and the postponement of the show, the authors set out to develop an interface solution that would enable visitors to interact with the works without having to touch any public devices like a tablet. Toward this end, we prototyped a “choreographic interface” that uses machine vision and machine learning to interpret a full-torso gestural vocabulary, which is then translated into interactions. To make the choreographic interface, we relied on open-source solutions, which have all come with equal limitations and opportunities. In 2022, Curatorial A(i)gents was presented in the Lightbox Gallery, where we had the opportunity to test and demonstrate the interface. This paper discusses our design journey in making a choreographic interface using open-source technologies during Covid-19.
Contact Discovery in Mobile Messengers: Low-cost Attacks, Quantitative Analyses, and Efficient Mitigations

Contact discovery allows users of mobile messengers to conveniently connect with people in their address book. In this work, we demonstrate that severe privacy issues exist in currently deployed contact discovery methods and propose suitable mitigations. Our study of three popular messengers (WhatsApp, Signal, and Telegram) shows that large-scale crawling attacks are (still) possible. Using an accurate database of mobile phone number prefixes and very few resources, we queried 10 % of US mobile phone numbers for WhatsApp and 100 % for Signal. For Telegram, we find that its API exposes a wide range of sensitive information, even about numbers not registered with the service. We present interesting (cross-messenger) usage statistics, which also reveal that very few users change the default privacy settings. Furthermore, we demonstrate that currently deployed hashing-based contact discovery protocols are severely broken by comparing three methods for efficient hash reversal. Most notably, we show that with the password cracking tool “JTR,” we can iterate through the entire worldwide mobile phone number space in &lt; 150 s on a consumer-grade GPU. We also propose a significantly improved rainbow table construction for non-uniformly distributed input domains that is of independent interest. Regarding mitigations, we most notably propose two novel rate-limiting schemes: our incremental contact discovery for services without server-side contact storage strictly improves over Signal’s current approach while being compatible with private set intersection, whereas our differential scheme allows even stricter rate limits at the overhead for service providers to store a small constant-size state that does not reveal any contact information.
Intelligent Test Algorithm for English Writing Using English Semantic and Neural Networks

English writing is considered by English learners to be the portion of English learning with the greatest application, the most thorough understanding, and the most challenging instruction. It automatically detects and corrects (DAC) grammatical faults in English writing, which is critical in the English learning and teaching processes. The goal of this research is to investigate the sequence annotation model and the Seq2Seq NN model based on cyclic NN, and to use these two models to detect grammatical faults in English (EGE). This paper provides an EGE DAC approach based on sequence annotation with the aid of the sequence annotation model developed in this paper. Simultaneously, this work presents an EGE DAC approach based on Seq2Seq that integrates the sequence annotation model. The model is no longer trained on a single form of grammatical error, but rather on all types of errors combined, allowing it to respond to any EGE. This work considers the DAC of grammatical errors with fixed confusion sets, such as prepositions and articles. This model’s F1 value for article error correction is 38.05 percent, which is 33.40 percent higher than the F1 value for UIUC article error correction. The F1 value for preposition error correction is 28.89 percent, which is 7.22 percent higher than the F1 value for UIUC preposition error correction.
Path Planning Method Considering Blind Spots Based on ROS Navigation Stack and Dynamic Window Approach for Wheeled Mobile Robot

This paper proposes a path planning method considering blind spots based on ros navigation stack and dynamic window approach (DWA) for a wheeled mobile robot. The proposed method uses a laser range finder (LRF) and RGB-D cameras to recognize environmental information in real-time. Blind spots occur when there are corners or obstacles in environments. If a human or object moves towards a robot from blind spots, a collision may occur. In order to avoid this collision, this paper describes a local path planning method considering blind spots. Blind spots are estimated from environmental information measured by RGB-D cameras. In the proposed method, the path planning method considering blind spots is achieved by the cost map “Blind Spots Layer (BSL)” and DWA which is local path planning with improved the cost function. The effectiveness of the proposed method is further demonstrated through simulations.
Dual-channel LIDAR searching, positioning, tracking and landing system for rotorcraft from ships at sea

Abstract To address the shortcomings of existing methods for rotorcraft searching, positioning, tracking and landing on a ship at sea, a dual-channel LIDAR searching, positioning, tracking and landing system (DCLSPTLS) is proposed in this paper, which utilises the multi-pulse laser echoes accumulation method and the physical phenomenon that the laser reflectivity of the ship deck in the near-infrared band is four orders of magnitude higher than that of the sea surface. The DCLSPTLS searching and positioning model, tracking model and landing model are established, respectively. The searching and positioning model can provide estimates of the azimuth angle, the distance of the ship relative to the rotorcraft and the ship's course. With the above parameters as inputs, the total tracking time and the direction of the rotorcraft tracking speed can be obtained by using the tracking model. The landing model can calculate the pitch and the roll angles of the ship's deck relative to the rotorcraft by using the least squares method and the laser irradiation coordinates. The simulation shows that the DCLSPTLS can realise the functions of rotorcraft searching, positioning, tracking and landing by using the above parameters. To verify the effectiveness of the DCLSPTLS, a functional test is performed using a rotorcraft and a model ship on a lake. The test results are consistent with the results of the simulation.