Reviewerly/exHarmony huggingface dataset:
CaCo: Both Positive and Negative Samples are Directly Learnable via Cooperative-Adversarial Contrastive Learning

As a representative self-supervised method, contrastive learning has achieved great successes in unsupervised training of representations. It trains an encoder by distinguishing positive samples from negative ones given query anchors. These positive and negative samples play critical roles in defining the objective to learn the discriminative encoder, avoiding it from learning trivial features. While existing methods heuristically choose these samples, we present a principled method where both positive and negative samples are directly learnable end-to-end with the encoder. We show that the positive and negative samples can be cooperatively and adversarially learned by minimizing and maximizing the contrastive loss, respectively. This yields cooperative positives and adversarial negatives with respect to the encoder, which are updated to continuously track the learned representation of the query anchors over mini-batches. The proposed method achieves 71.3% and 75.3% in top-1 accuracy respectively over 200 and 800 epochs of pre-training ResNet-50 backbone on ImageNet1K without tricks such as multi-crop or stronger augmentations. With Multi-Crop, it can be further boosted into 75.7%. The source code and pre-trained model are released in https://github.com/maple-research-lab/caco.
Quantum Computing and IS - Harnessing the Opportunities of Emerging Technologies

Emerging technologies have a high potential for impact and are worthy of attention by the Information Systems (IS) community. To date, the IS discipline has not been able to be a leader in the research and teaching of emerging technologies in their early stages arguably because: (1) IS researchers often lack knowledge of the foundational principles of such emerging technologies, and (2) during the emerging phase, there is insufficient data on the adoption, use, and impact of these technologies. To overcome these challenges, the IS discipline must be willing to break its own disciplinary research boundaries to go beyond software applications and their related management issues and start studying emerging technologies before they are massively adopted by industry. In this paper, we use quantum computing as an exemplar emerging technology and outline a research and education agenda that will help IS harness the opportunities quantum computing offers. We propose that IS researchers rigorously investigate emergent technologies through collaboration with researchers from other disciplines. We also see a role for IS researchers in the scholarship of emerging technologies and in the process of introducing emerging technologies into IS curricula.
High reflection surface topography measurement based on fringe projection phase method and multi-exposure image fusion technology

Abstract：Grating projection three-dimensional measurement technology has become one of the most widely used three-dimensional measurement technologies owing to its noncontact，high resolution，and
Electric Wheelchair Hybrid Operating System Coordinated with Working Range of a Robotic Arm

Electric wheelchair-mounted robotic arms can help patients with disabilities to perform their activities in daily living (ADL). Joysticks or keypads are commonly used as the operating interface of Wheelchair-mounted robotic arms. Under different scenarios, some patients with upper limb disabilities such as finger contracture cannot operate such interfaces smoothly. Recently, manual interfaces for different symptoms to operate the wheelchair-mounted robotic arms are being developed. However, the stop the wheelchairs in an appropriate position for the robotic arm grasping task is still not easy. To reduce the individual’s burden in operating wheelchair in narrow spaces and to ensure that the chair always stops within the working range of a robotic arm, we propose here an operating system for an electric wheelchair that can automatically drive itself to within the working range of a robotic arm by capturing the position of an AR marker via a chair-mounted camera. Meanwhile, the system includes an error correction model to correct the wheelchair’s moving error. Finally, we demonstrate the effectiveness of the proposed system by running the wheelchair and simulating the robotic arm through several courses.
A Bare-Bones Particle Swarm Optimization With Crossed Memory for Global Optimization

The offspring selection strategy is the core of evolutionary algorithms, which directly affects the method's accuracy.Normally, to improve the search accuracy in local areas, the population converges quickly around the optimal individual.However, excessive aggregation can narrow the search range of the population, and thus the population may be trapped by local optima.To overcome this problem, a bare-bones particle swarm optimization with crossed memory (BPSO-CM) is proposed in this work.The BPSO-CM contains a multi-memory storage mechanism (MSM) and an elite offspring selection strategy (EOSS).The MSM enables an extra storage space to extend the search ability of the particle swarm and the EOSS enhances the local minimum escaping ability of the particle swarm.The population is endowed with the ability of enhanced global search through the cooperation of the MSM and the EOSS.To verify the performance of the BPSO-CM, the CEC2017 benchmark functions are used in experiments, five population-based methods are selected in the control group.Finally, experimental results proved that the BPSO-CM can present highly accurate results for global optimization problems.
An Ensemble Multi-Layered Sentiment Analysis Model (EMLSA) for Classifying the Complex Datasets

Sentiment analysis is one domain that analyzes the feelings and emotions of the users based on their text messages.Sentiment analysis of short messages, reviews in online social media (OSM), and social networking sites (SNS) messages gives the analysis of given text data.Processing short text and SNS messages is a very tedious task because of the restricted detailed information generally contained.Solving this issue requires advanced techniques that are combined to give accurate results.This paper developed an Ensemble Multi-Layered Sentiment Analysis Model (EMLSA) that exploits the trust-based sentiment analysis on various real-time datasets.EMLA is the combined approach with VADER (Valence Aware Dictionary and sEntiment Reasoned) and Recurrent Neural Networks (RNNs).VADER is the lexicon and rule-based sentiment analysis model that predicts the sentiments extracted from input datasets and it is used for training.The feature extraction technique is termfrequency and inverse document frequency.Word-Level Embeddings (WLE) and Character-Level Embeddings (CLE) are the two models that increase the short text and single-word analysis.The proposed model was applied to four real-time datasets: Amazon, eBay, Trip-advisor, and IMDB Movie Reviews.The performance is analyzed using various parameters such as sensitivity, specificity, precision, accuracy, and F1-score.
Higher Educational Information Resource Sharing Model Based on Blockchain

Through the sharing of high-quality educational resources in colleges and universities, various colleges and universities have carried out active and effective exchanges in teaching resources, avoiding the duplication of larger educational resources, and improving the academic and career development of teachers and students. The construction of a blockchain-based regional higher education information resource sharing model can solve the problems of scattered teaching resources and duplicate construction of teaching resources, difficulties to ensure the security of digital education resources, high operating costs of platforms, and urgent protection of intellectual property rights of resources. Most of the existing solutions rely on third-party certificate issuing centers or use a single key to encrypt the data flow of education information resources, which has hidden dangers of leakage of privacy information such as intellectual property rights, copyrights, confidential information of resources, other key information, and operation records. Therefore, this paper studies the regional higher education information resource sharing model based on blockchain and designs the data protection protocol of higher education information resources and its resource transaction relationship protection scheme. It introduces the blockchain-based regional higher education information resource sharing model mainly from three aspects: blinding the identity of authorized resource recipients, resource initiators publishing resource transactions, and authorized resource recipients publishing resource transactions. Experimental results verify the effectiveness of the proposed model.
Prioritizing Critical Success Factors of Requirements Engineering using Analytical Hierarchy Process

Requirements engineering is not as straightforward as asking stakeholders what they want the information systems to do. In most cases, their vision tends to be limited by the status quo. Eliciting a complete set of requirements that fulfil every gap and withstand scrutiny during validation is challenging. Hence, it is important to consider various factors influencing the success of requirements engineering. This paper identifies and prioritizes multiple critical success factors of requirements engineering using the Analytical Hierarchy Process. The initial model was developed from literature review and validated using evidence from an empirical study. Quantitative data was collected through a questionnaire and then analyzed to rank the success criteria and critical success factors. The results show that the user satisfaction is the most important success criterion, while the clear definition of project scopes and goals is the most critical factor for the success of requirements engineering.
Computer Literacy with Skills of Seeking for Information Electronically among University Students

Computer literacy is an urgent necessity for university students, given the rapid development in the means of communication in which we live in this era, and the flow of abundant information. Mainly on the computer in all administrative and academic transactions, where first of all the registration for the semester is done through the computer. Computer culture has many characteristics and advantages that distinguish it from other sciences, including the concept of computer culture that cannot be defined absolutely, and it is difficult to define its levels, because the specifications of the computer-educated individual differ from one individual to another, and from time to time also, you find it a luxury in a country What, and you find it necessary in another country. In order to measure and know the level of computer culture among university students, a computerized scale of (40) items with five multiple-choice alternatives were built. In order to know that they have the skills of searching for information electronically, a computerized scale of (21) items were prepared, and a five-point Likert scale was adopted. Results showed that they have computer literacy and therefore they have skills of searching for information electronically at all.
ChatGPT, the Cognitive Mediation Networks Theory and the Emergence of Sophotechnic Thinking: How Natural Language AIs Will Bring a New Step in Collective Cognitive Evolution

This paper examines ChatGPT, a natural language processing artificial intelligence (AI) chatbot that has become popular for its human-like text generation and high-level performance on various sophisticated tasks, and its potential impacts on society and individuals. First, there is an overview of ChatGPT, describing what it is, how it works, and its capabilities, development and rapid dissemination, as well as its influence in work, education and life in general. Then, the main tenets and concepts of the Cognitive Mediation Networks Theory (CMNT) are presented, particularly regarding the cognitive ramifications of the Digital Revolution and the emergence of the Hyperculture. Using the lens of the CMNT, it is suggested that the introduction of this radically new technology will likely create a new stage of collective cognitive functioning, called “Sophotechnic Mediation”, with characteristics that can be extrapolated from the way these new tools work and the dynamics of the sociocultural structures being created around them. Finally, an instrument is suggested to assess the extent of an individual’s internalization of Sophotechnic thinking and acting so as to empirically test the predictions from the CMNT, along with indications for future research.
Google Assistant Assisted Language Learning (GAALL): ESL Learners’ Perception and Problem towards AI-powered Google Assistant-Assisted English Language Learning

The New-fangled strategy called Artificial Intelligence (AI) Assisted Language Learning (AI-ALL) incorporates Google Assistant (GA) to support the learning activities of learners. At present, it is considered Google Assistant Assisted Language Learning (GAALL). For many years, academicians have been exploring ways to use AI for tasks that are related to education. The major objectives of this study are to emphasize 1) The ESL learners' perceptions of using AI-powered Google Assistant for learning the English language; 2). The ESL learners’ problems are concerned with utilizing AI-powered Google Assistant to support language learning, especially in English. A survey instrument was employed to the data, including primary research objects, from (n = 141) engineering stream undergraduates. The data was gathered using a questionnaire with 5 points Likert scale. According to the survey, the vast majority of students, especially individuals who were learning English, had a positive opinion of AI-driven GA. The major problem is the lack of quality in GA on smartphones. However, it is envisaged that AI-powered GA in language learning, also known as GAALL, would be deployed as one of the instructional media that might help learners to learn English efficiently as a Second Language. This study suggests that further research studies in this field could be conducted to test the efficacy of AI in ESL contexts.
InspectorNet: Transformer network for violence detection in animated cartoon

InspectorNet is a convolutional neural network based on transformer deep learning techniques, which is designed to address some of the limitations of current state-of-the-art artificial neural networks (ANN) models. The paper compares the performance of InspectorNet against a commonly used neural network in image classification, ResNet [1], on the Danbooru2020 dataset of animated cartoon images with a variable number of classes. The comparison shows that while both networks require significant computing resources for training, InspectorNet demonstrates better classification performance in certain test situations. The paper also highlights that with the increasing access to the internet, it is important to control the dissemination of sensitive content such as violence, but current neural networks may not be as effective in filtering cartoon movies aimed at children as the filters for these movies are different from those for adult movies. InspectorNet also has a compact architecture than many modern networks, such as ResNet, which results in better performance on low-resource devices
Braille Identification System Using Artificial Neural Networks

The Braille system is a widely used method by the blind to read and write. Information technology revolution is changing the way Braille reading and writing, making it easier to use. All kinds of materials can be put into Braille representation, such as bank statements, bus ticket, maps, and music note.&#x0D; In this paper, an artificial neural networks are designed to identify the number's image from (0-9) in Braille representation system. Networks will be trained and tested to be used for identify the scanned English number in Braille representation system. Some of the numbers are noised with some type of noise to simulate somehow the real world environment.&#x0D; According to the experiment the result of the identification of number that written in Braille representation using Artificial Neural Networks the training accuracy was 97.1% and testing accuracy was 85%.
Noise dynamics of quantum annealers: estimating the effective noise using idle qubits

Quantum annealing is a type of analog computation that aims to use quantum mechanical fluctuations in search of optimal solutions of QUBO (quadratic unconstrained binary optimization) or, equivalently, Ising problems. Since NP-hard problems can in general be mapped to Ising and QUBO formulations, the quantum annealing paradigm has the potential to help solve various NP-hard problems. Current quantum annealers, such as those manufactured by D-Wave Systems, Inc., have various practical limitations including the size (number of qubits) of the problem that can be solved, the qubit connectivity, and error due to the environment or system calibration, which can reduce the quality of the solutions. Typically, for an arbitrary problem instance, the corresponding QUBO (or Ising) structure will not natively embed onto the available qubit architecture on the quantum chip. Thus, in these cases, a minor embedding of the problem structure onto the device is necessary. However, minor embeddings on these devices do not always make use of the full sparse chip hardware graph, and a large portion of the available qubits stay unused during quantum annealing. In this work, we embed a disjoint random QUBO on the unused parts of the chip alongside the QUBO to be solved, which acts as an indicator of the solution quality of the device over time. Using experiments on three different D-Wave quantum annealers, we demonstrate that (i) long term trends in solution quality exist on the D-Wave device, and (ii) the unused qubits can be used to measure the current level of noise of the quantum system.
ICD2Vec: Mathematical representation of diseases

The International Classification of Diseases (ICD) codes represent the global standard for reporting disease conditions. The current ICD codes connote direct human-defined relationships among diseases in a hierarchical tree structure. Representing the ICD codes as mathematical vectors helps to capture nonlinear relationships in medical ontologies across diseases. We propose a universally applicable framework called “ICD2Vec” designed to provide mathematical representations of diseases by encoding corresponding information. First, we present the arithmetical and semantic relationships between diseases by mapping composite vectors for symptoms or diseases to the most similar ICD codes. Second, we investigated the validity of ICD2Vec by comparing the biological relationships and cosine similarities among the vectorized ICD codes. Third, we propose a new risk score called IRIS, derived from ICD2Vec, and demonstrate its clinical utility with large cohorts from the UK and South Korea. Semantic compositionality was qualitatively confirmed between descriptions of symptoms and ICD2Vec. For example, the diseases most similar to COVID-19 were found to be the common cold (ICD-10: J00), unspecified viral hemorrhagic fever (ICD-10: A99), and smallpox (ICD-10: B03). We show the significant associations between the cosine similarities derived from ICD2Vec and the biological relationships using disease-to-disease pairs. Furthermore, we observed significant adjusted hazard ratios (HR) and area under the receiver operating characteristics (AUROC) between IRIS and risks for eight diseases. For instance, the higher IRIS for coronary artery disease (CAD) can be the higher probability for the incidence of CAD (HR: 2.15 [95% CI 2.02–2.28] and AUROC: 0.587 [95% CI 0.583–0.591]). We identified individuals at substantially increased risk of CAD using IRIS and 10-year atherosclerotic cardiovascular disease risk (adjusted HR: 4.26 [95% CI 3.59–5.05]). ICD2Vec, a proposed universal framework for converting qualitatively measured ICD codes into quantitative vectors containing semantic relationships between diseases, exhibited a significant correlation with actual biological significance. In addition, the IRIS was a significant predictor of major diseases in a prospective study using two large-scale datasets. Based on this clinical validity and utility evidence, we suggest that publicly available ICD2Vec can be used in diverse research and clinical practices and has important clinical implications.
Teachers’ online preparedness in times of crises: trends from Norway and US

Abstract The closing of schools world-wide in March 2020 due to the COVID-19 pandemic resulted in a rapid and unexpected shift from predominantly in-person teaching to online teaching practices. As teacher educators in the field of educational technology, we wondered about the preparedness of teachers for making the transition to fully online environments. Through an internationally distributed survey consisting of predominantly open-ended questions, we captured teachers’ perceptions of this transition. We aimed to inform our practice and that of other teacher educators about the strengths and weaknesses of professional development designed to develop teachers’ digital competence. In this paper, we present data from Norwegian (n = 574) and US (n = 239) teachers related to their elaborations on readiness. We qualitatively examined data for evidence of extent of preparedness and alignment to the pedagogical, ethical, attitudinal, and technical dimensions of digital competence. Findings indicated themes related to extent of preparedness, trends in preparation, focus on digital tools, teacher agency without autonomy, collaboration/networks, and challenges for work and learning lives. Findings informed implications and recommendations for the professional development of teachers’ digital competence at the teacher education, K-12 schools, and school policy/leadership levels.
Improved U-net-based leukocyte segmentation method

SignificanceLeukocytes are mainly composed of neutrophils, basophils, eosinophils, monocytes, and lymphocytes. The number and proportion of different types of leukocytes correspond to different diseases, so an accurate segmentation of each type of leukocyte is important for the diagnosis of disease. However, the acquisition of blood cell images can be affected by external environmental factors, which can lead to variable light and darkness, complex backgrounds, and poorly characterized leukocytes.AimTo address the problem of complex blood cell images collected under different environments and the lack of obvious leukocyte features, a leukocyte segmentation method based on improved U-net is proposed.ApproachFirst, adaptive histogram equalization-retinex correction was introduced for data enhancement to make the leukocyte features in the blood cell images clearer. Then, to address the problem of similarity between different types of leukocytes, convolutional block attention module is added to the four skip connections of U-net to focus the features from spatial and channel aspects, so that the network can quickly locate the high-value information of features in different channels and spaces. It avoids the problem of large amount of repeated computation of low-value information, prevents overfitting, and improves the training efficiency and generalization ability of the network. Finally, to solve the problem of class imbalance in blood cell images and to better segment the cytoplasm of leukocytes, a loss function combining focal loss and Dice loss is proposed.ResultsWe use the BCISC public dataset to verify the effectiveness of the proposed method. The segmentation of multiple leukocytes using the method of this paper can achieve 99.53% accuracy and 91.89% mIoU.ConclusionsThe experimental results show that the method achieves good segmentation results for lymphocytes, basophils, neutrophils, eosinophils, and monocytes.