Reviewerly/exHarmony huggingface dataset:
An Unsupervised Machine Learning Approach for the Detection and Characterization of Illicit Drug-Dealing Comments and Interactions on Instagram

Background: Growing use of social media has led to the emergence of virtual controlled substance and illicit drug marketplaces, prompting calls for action by government and law enforcement. Previous studies have analyzed Instagram drug selling via posts. However, comments made by users involving potential drug selling have not been analyzed. In this study, we use unsupervised machine learning to detect and classify prescription and illicit drug-related buying and selling interactions on Instagram. Methods: We used over 1,000 drug-related hashtags on Instagram to collect a total of 43,607 Instagram comments between February 1st, 2019 and May 31st, 2019 using data mining approaches in the Python programming language. We then used an unsupervised machine learning approach, the Biterm Topic Model (BTM), to thematically summarize Instagram comments into distinct topic groupings, which were then extracted and manually annotated to detect buying and selling comments. Results: We detected 5,589 comments from sellers, prospective buyers, and online pharmacies from 531 unique posts. The vast majority (99.7%) of comments originated from drug sellers and online pharmacies. Key themes from comments included providing contact information through encrypted third-party messaging platforms, drug availability, and price inquiry. Commonly offered drugs for sale included scheduled controlled substances such as Adderall and Xanax, as well as illicit hallucinogens and stimulants. Comments from prospective buyers of drugs most commonly included inquiries about price and availability. Conclusions: We detected prescription controlled substances and other illicit drug selling interactions via Instagram comments to posts. We observed that comments were primarily used by sellers offering drugs, and typically not by prospective buyers interacting with sellers. Further research is needed to characterize these “social” drug marketplace interactions on this and other popular social media platforms.
ForecastQA: A Question Answering Challenge for Event Forecasting with Temporal Text Data

Event forecasting is a challenging, yet important task, as humans seek to constantly plan for the future.Existing automated forecasting studies rely mostly on structured data, such as time-series or event-based knowledge graphs, to help predict future events.In this work, we aim to formulate a task, construct a dataset, and provide benchmarks for developing methods for event forecasting with large volumes of unstructured text data.To simulate the forecasting scenario on temporal news documents, we formulate the problem as a restricted-domain, multiple-choice, question-answering (QA) task.Unlike existing QA tasks, our task limits accessible information, and thus a model has to make a forecasting judgement.To showcase the usefulness of this task formulation, we introduce FORECASTQA, a question-answering dataset consisting of 10,392 event forecasting questions, which have been collected and verified via crowdsourcing efforts.We present our experiments on FORECASTQA using BERTbased models and find that our best model achieves 61.0% accuracy on the dataset, which still lags behind human performance by about 19%.We hope FORECASTQA will support future research efforts in bridging this gap. 1
Document-level relation extraction via graph transformer networks and temporal convolutional networks

• We propose a novel document-level relation extraction framework DocRE-HGNN. • Temporal convolutional networks is used to encode document with non-local dependency. • Heterogeneous GNN is adopted to obtain entity-entity representation automatically. • DocRE-HGNN can obtain both long inter-sentence paths and short intra-sentence paths. • Experiments on document-level biomedical datasets demonstrate model’s effectiveness. Relation Extraction (RE) aims at extracting meaningful relation facts between entities in texts. It is an important semantic processing task in the field of natural language processing (NLP) and has many applications. Traditional RE focuses on extracting entity relationships from a single input sentence. Recently, the research scope has been extended from sentence level to document level. However, compared with sentence-level RE, document-level RE, which needs to identify the inter-sentence relations from entities scattered in different sentences, is more complex and still lacks of solutions. To solve this problem, we propose a novel document-level RE method based on Heterogeneous Graph Neural Networks in this paper. Concretely, to obtain token embeddings containing long-distance dependency signals well, we encode the document with Temporal Convolutional Networks, whose dilated convolution and residual structure allow the effective and efficient preservation of historical information. To better describe the interaction between different elements, we construct the input documents as heterogeneous graphs with different node and edge types and utilize Graph Transformer Networks to generate semantic paths. Numerical experiments on two document-level biomedical datasets demonstrate the effectiveness of the proposed method.
Multi-image Reorganization Encryption Based on S-L-F Cascade Chaos and Bit Scrambling

Aiming at the problems of small value range of a single chaotic parameter, low sequence chaos, and transient effects, a composite chaotic system of cascaded Sine-Sine mapping, Logistic chaos and generalized third-order Fibonacci is proposed (S-L-F). The new system is highly sensitive to initial values, the maximum spectral entropy of the generated sequence can reach 0.95, and the value range of the parameter x is expanded to [0,4] compared with the traditional Logistic, indicating that the new system is suitable for generating pseudo-random sequences for image encryption. For the problem that the traditional multi-image encryption scheme can only encrypt images of the same type and size, the practicability is poor, and a multi-image encryption scheme based on image reorganization and biting is proposed. The algorithm recombines any number, different sizes and different types of images into a three-dimensional matrix, converts it into a binary matrix, performs bit-level scrambling and surface cyclic scrambling, and then restores the scrambling matrix to decimal, and the chaotic sequence performs exclusive-or diffusion, and completes simultaneous encryption at one time, which greatly improves the encryption efficiency and scope of application. The NPCR of the ciphertext image is 0.9961, and the UACI is 0.3345, which proves that the ciphertext image can effectively resist the difference attack. The information entropy is greater than 7.999, which can effectively resist attacks. It has certain application value in image information security. Experimental analysis shows that the algorithm has high security and strong practicability.
Object Detection and Depth Estimation Approach Based on Deep Convolutional Neural Networks

In this paper, we present a real-time object detection and depth estimation approach based on deep convolutional neural networks (CNNs). We improve object detection through the incorporation of transfer connection blocks (TCBs), in particular, to detect small objects in real time. For depth estimation, we introduce binocular vision to the monocular-based disparity estimation network, and the epipolar constraint is used to improve prediction accuracy. Finally, we integrate the two-dimensional (2D) location of the detected object with the depth information to achieve real-time detection and depth estimation. The results demonstrate that the proposed approach achieves better results compared to conventional methods.
Turkish Medical Text Classification Using BERT

Medical text classification is mostly carried out on English data sets. The limited number of studies in Turkish is due to the compelling morphological structure of Turkish for natural language processing and the limited number of data sets in the medical domain. In addition, the use of domain specific words and abbreviations makes natural language processing studies more challenging. In this study, a classification model is implemented to assign article abstracts to appropriate disease categories using multilingual BERT and BERTurk models on a data set consisting of Turkish medical article abstracts. As a result of the experimental study, 0.82 and 0.93 F-score are obtained for multilingual BERT and BERTurk, respectively. The results show that the BERTurk is more successful than other compared models for Turkish medical text classification.
Attribute-based Neural Collaborative Filtering

The core task of recommendation systems is to capture user preferences for items. Dot product operations are usually used to mine user preferences for items. However, the dot product can only capture the low-order linear relationships between users and items. In addition, to alleviate the data sparsity problem, current methods mainly introduce auxiliary information, such as user/item attribute information. This attribute information is often treated equivalently. In fact, the importance of this information has different effects on the recommendation results. Therefore, in this paper, we propose a novel Attribute-based Neural Collaborative Filtering (ANCF) method to solve the above problems. Specifically, we use the attention mechanism to distinguish the importance of attribute information and integrate it into the corresponding user and item feature representations to obtain a complete feature representation of users and items. To further capture the high-order interactive relationship between users and items, we use a multi-layer perceptron in ANCF to fully learn the high-order nonlinear relationship between users and items. Extensive experiments on four publicly available datasets demonstrate the effectiveness of the proposed ANCF framework.
High-fidelity quantum teleportation toward cubic phase gates beyond the no-cloning limit

High-fidelity quantum teleportation is necessary to configure a cubic phase gate that enables the processing of the nonclassicality in an input quantum state. The noise variance of the antisqueezing component, which increases rapidly with a higher pump factor, contaminates the squeezing component due to the phase fluctuation and results in a reduction of the fidelity. Here, we balance the influence of the extraction loss and phase fluctuation on the fidelity and optimize the performance of quantum teleportation. As a result, a fidelity $F=0.905\ifmmode\pm\else\textpm\fi{}0.022$ is experimentally achieved. The ultrahigh fidelity is expected to configure a cubic phase gate involving four-time sequential teleportation to perform universal continuous-variable quantum information processing.
Abductive Knowledge Induction from Raw Data

For many reasoning-heavy tasks with raw inputs, it is challenging to design an appropriate end-to-end pipeline to formulate the problem-solving process. Some modern AI systems, e.g., Neuro-Symbolic Learning, divide the pipeline into sub-symbolic perception and symbolic reasoning, trying to utilise data-driven machine learning and knowledge-driven problem-solving simultaneously. However, these systems suffer from the exponential computational complexity caused by the interface between the two components, where the sub-symbolic learning model lacks direct supervision, and the symbolic model lacks accurate input facts. Hence, they usually focus on learning the sub-symbolic model with a complete symbolic knowledge base while avoiding a crucial problem: where does the knowledge come from? In this paper, we present Abductive Meta-Interpretive Learning (MetaAbd) that unites abduction and induction to learn neural networks and logic theories jointly from raw data. Experimental results demonstrate that MetaAbd not only outperforms the compared systems in predictive accuracy and data efficiency but also induces logic programs that can be re-used as background knowledge in subsequent learning tasks. To the best of our knowledge, MetaAbd is the first system that can jointly learn neural networks from scratch and induce recursive first-order logic theories with predicate invention.
Comparing different algorithms for the course of Alzheimer’s disease using machine learning

Background: Alzheimer's disease (AD) is one of the most influential nervous system diseases in the world.It is accompanied by symptoms such as loss of memory, thinking, and language ability.This paper discusses the characteristic indexes of brain magnetic resonance imaging (MRI) in mild cognitive impairment (MCI) and AD.It applies the MRI characteristic indexes in machine learning to classify and predict the course of AD to select the best model for classification and prediction auxiliary diagnosis of AD.Methods: In this study, 560 eligible subjects numbered 0-15,000 in the AD Neuroimaging Initiative (ADNI) database were randomly selected.According to the ADNI diagnostic criteria, the subjects were divided into four groups: the cognitive normal (CN) group (n=140), 230 cases in the early MCI (EMCI) group, 110 cases in the late MCI (LMCI) group, and 80 patients in the AD group.Random forest (RF), decision tree (DT), support vector machine (SVM) algorithms were used to classify and predict the different disease progress of AD.Next, different MRI indexes were input into the three machine learning algorithms to predict CN-EMCI-LMCI-AD.We compared the prediction accuracy, sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC).Results: This study found that CN-AD had the highest classification accuracy, followed by EMCI-AD, CN-LMCI, LMCI-AD, EMCI-LMCI, and CN-EMCI.In the prediction of CN-AD, the AUC of 0.92 of the RF classifier was higher than the AUCs of the SVM and DT classifiers.Of the three machine learning algorithms, RF was better than the SVM and DT at predicting different MRI features.The accuracy of RF, SVM, and DT was 73.8%, 60.7%, and 59.5%, respectively. Conclusions:The RF classifier had the best prediction effect on different disease processes of AD.Five MRI indexes (used as classification features) had the best prediction effects.CN-AD had the best classification effect.Overall, the classification accuracy of the RF classifier for CN-EMCI-LMCI-AD was higher than those of the other models.The RF classifier can be used to classify different stages of AD in the early stages of the disease to assist in diagnosing AD.
Spatial-temporal Evolution of Injection Induced Earthquakes in Weiyuan Area by Machine-Learning Phase Picker and Waveform Cross-correlation

We detected and located earthquakes from
MEC Resource Offloading for QoE-Aware HAS Video Streaming

With the popularity of mobile edge computing (MEC), video streaming's peer-offloading strategy significantly affects the Quality of Experience (QoE) performance of video streaming for mobile users (MUs). Improving the service quality regarding the MUs' demand has become a vital challenge, reflected by QoE. This paper proposes a QoE-aware MEC-based peer-offloading method for HAS-based video streaming, called QOMECS. The proposed method considers dynamic MUs' demands and corresponding QoE requirements. We categorize the QoE KPIs into perceptual and systemic sectors. We formulate the corresponding transmission, computation, and offloading for MEC-based HAS into a QoE maximization problem. We propose a reverse-fuzzied particle swarm optimization (R-FPSO), to solve the highly nonlinear and perceptual-oriented optimization formulation. Unlike conventional fuzzy logic, R-FPSO reverses the fuzzification process by fuzzifying PSO's output (i.e. translated QoE KPIs into satisfactory levels) and further updates the particle values and velocities in the PSO process. Simulation results show that the proposed QOMECS dramatically improves the edge computing efficiency, with optimized QoE performance.
Ethics of artificial intelligence 1

Artificial intelligence (AI) is a digital technology that will be of major importance for the development of humanity in the near future. This chapter outlines the ethical issues of human use of AI and robotics systems that can be more or less autonomous. The ethical issues of AI in surveillance go beyond the mere accumulation of data and direction of attention: They include the use of information for problematic purposes. Opacity and bias are central issues in what is now sometimes called ‘data ethics’ or ‘big data ethics’. Automated AI decision support systems and ‘predictive analytics’ operate on data and produce a decision as ‘output’. Human-robot interaction (HRI) is an academic field in its own right that now pays significant attention to ethical matters, the dynamics of perception from both sides, and both the different interests present in and the intricacy of the social context, including co-working.
Movie Title Extraction and Script Separation Using Shallow Convolution Neural Network

Graphical texts in natural images play an important role in portraying information in multitudinous fields such as communication, education, and entertainment to name a few. Recognizing text in scene images is challenging due to the inherent complexity of the images. Text recognition in natural images involves script identification, which requires text localization. This is not trivial for natural scene images due to the presence of disparate foreground/background components. For scene images like movie posters, the challenge is more dominant. The challenges aggravate due to the presence of composite characteristics of posters like complex graphics background and the presence of different texts like a movie title, names of actors, producers, directors, and tagline. These texts have miscellaneous fonts, variations in colors, size, orientation, and textures. In this work, an M-EAST (modified EAST) model is proposed, which is based on the EAST (efficient and accurate scene text detector) model for text localization. A novel movie title extraction is thereafter used for separating the title from the extracted text pool. Finally, the title script was identified using a shallow convolutional neural network (SCNN)-based to ensure functionality in low-resource environments. Experiments were performed on a dataset of movie-poster images of Tollywood, Bollywood, and Hollywood industries, and a highest accuracy of 99.82% was obtained. The system performed better than the reported techniques.
Temperature Measurement—Inquiry-Based Learning Activities for Third Graders

The article presents our Inquiry-Based Learning (IBL) activities in the project-based Science education of third graders (8–9-year-old pupils) in the Slovak Republic. Individual tasks of project assignment titled “Temperature Measurement” were conceived for “guided inquiry”. We also supported the IBL of pupils by interactive education strategy–Integrated e-Learning (INTe-L). The strategy was built on the role of interactive simulations and experimenting at the knowledge acquisition. The following INTe-L components were used: (a) on-site and remote experiments, (b) interactive simulations and (c) electronic study materials. The research was based the hypothesis that the project-based teaching of the topic “Temperature Measurement” using the IBL activities is significantly more efficient than traditional (instructivist) way of teaching. To verify the hypothesis, we carried out a pedagogical experiment on a sample of 60 respondents. The third graders were divided into two parallel groups: an experimental group (EG, 30 pupils) and control group (CG, 30 pupils). To get a relevant feedback, pre-test and post-test were developed and applied. The results attained in the EG were statistically processed, evaluated, and subsequently compared with those in the CG. The analysis of the results attained by the EG showed that their knowledge acquisition has been increased by 24% compared to CG. We have created a methodology for making simple scientific project assignments with the implementation of all components of the new integrated e-learning education strategy (real and real remote experiment, e-simulation, e-learning material). On a specific example of the topic “Temperature Measurement” in primary education, we have presented the possibility of using a “guided inquiry” in the implementation of individual experimental tasks. We have introduced a form of teamwork that allows to harmonize the teaching requirements with individual learning approach and helps to prepare pupils for planned work and independent knowledge acquisition and problem solving. The study has shown that complex IBL activities can be successfully applied at primary school 3rd grade level already. Our form of education with the wider use of modern information and communication technologies (ICT) was attractive for the learners and helped them to develop interdisciplinary relationships. The integrated e-learning has helped third grades to develop a deeper conceptual understanding of temperature and allowed them to prepare presentations to demonstrate their conceptual knowledge. The approach allowed pupils to gain key competencies in Science (discussion and presentation of results). It has also increased the pupils’ interest in Science in general and helped them to understand links between different subjects.
Didactics of smart pedagogy: Smart pedagogy for technology enhanced learning

"Didactics of smart pedagogy: Smart pedagogy for technology enhanced learning." Innovations in Education and Teaching International, 58(5), pp. 606–607
Interpretability and accuracy trade-off in the modeling of belief rule-based systems

As a widely used rule-based system, one of the strongest advantages of belief rule-based (BRB) systems is good interpretability. Currently, most studies on BRB modeling focus on improving accuracy only but overlook interpretability. In this study, the definition of interpretability in a BRB system is first discussed, and the evaluation indicators are designed for measurement. On this basis, a single-objective optimization framework for the interpretability and accuracy trade-off in the modeling of BRB systems is proposed. The differential evolution (DE) algorithm is utilized as the optimization tool, and a new encoding method of chromosomes is proposed to realize the coding of rules with different structures. Two case studies are tested to validate the efficiency and feasibility of the proposed optimization framework for BRB modeling in both prediction and classification. The results show that the proposed optimization framework can ensure the interpretability and accuracy of BRB systems simultaneously, which is conducive to promoting the application of BRB systems in fields that require high interpretability and accuracy.