Reviewerly/exHarmony huggingface dataset:
Application of Intelligent Sensor Algorithm in Student Management Information Fusion

In order to improve the effectiveness of college student management and promote the integration of college student management information, this paper applies intelligent sensor algorithms to student management. Moreover, this paper combines uncertainty theory with multisensor data fusion technology to establish a complete set of multisensor data processing tools for student information and provides a complete mathematical theoretical framework for the principles of student management information fusion. In addition, in view of the problem of comparing a large number of mixed data of information sources, it is necessary to transfer the information fragments obtained by each sensor to a common set so that the information fragments expressed in different sets can be integrated. Finally, this paper constructs an intelligent student management model and conducts research in combination with simulation experiments. Through simulation research, it can be known that the method proposed in this paper can effectively improve the effect of student management.
A New Conservative Hyperchaotic System-Based Image Symmetric Encryption Scheme with DNA Coding

In the current paper, a new conservative hyperchaotic system is proposed. We make a quantitative analysis of the complexity of the conservative hyperchaotic system from several different aspects, such as phase diagrams, bifurcation diagrams, Lyapunov exponents, and Kaplan–Yorke dimension. The complexity of chaotic time series is tested with various measurement tools, such as the scale index, the multiscale sample entropy and approximate entropy, TESTU01, and NIST test. In addition, a novel hyperchao-based image encryption scheme with dynamic DNA coding is proposed. The encryption algorithm consists of line-by-line scrambling and diffusion of DNA encoding characters. The dynamic DNA coding mechanism is introduced by using the chaotic sequence. The generation of the intermediate secret keys is related to the sum of the image DNA code, and the ciphertext feedback mechanism of the DNA encoding image is introduced in the diffusion procedure. Simulation experiments and various security analyses show that this algorithm has a good effect on encryption, high time efficiency, and can effectively resist brute force attacks, statistical attacks, chosen-plaintext attacks, and differential attacks.
A commentary of TikTok recommendation algorithms in MIT Technology Review 2021

TikTok is one of the world's most attractive and fastest-growing social media platforms. It has more than 2.6 billion downloads globally and over 100 million users in the US. The “secret weapon” is its unique methodology of discovering and delivering content. On the “For You” feed, TikTok mixes videos of both internet celebrities and newcomers, encourages high-quality creative content according to page views, and promotes new bloggers to share videos with users. The unique aspect is that anyone has the opportunity to spring into fame on the feed. Through TikTok's recommendation algorithm, videos are continuously recommended to users with similar interests or attributes as video bloggers, thus allowing high-quality creative content to be disseminated quickly. The TikTok recommendation algorithm does not take video blogger's fanbase or popularity into major consideration. In addition to the candidate video title, audio, and tags, the algorithm integrates the content of the user's uploaded videos, and the categories of videos liked by the user. TikTok's skills at enhancing user loyalty are impressive, which can not only accurately recommend videos of interest to users, but also assist them in expanding into new intersecting areas.①
Towards Design and Feasibility Analysis of DePaaS: AI Based Global Unified Software Defect Prediction Framework

Using artificial intelligence (AI) based software defect prediction (SDP) techniques in the software development process helps isolate defective software modules, count the number of software defects, and identify risky code changes. However, software development teams are unaware of SDP and do not have easy access to relevant models and techniques. The major reason for this problem seems to be the fragmentation of SDP research and SDP practice. To unify SDP research and practice this article introduces a cloud-based, global, unified AI framework for SDP called DePaaS—Defects Prediction as a Service. The article describes the usage context, use cases and detailed architecture of DePaaS and presents the first response of the industry practitioners to DePaaS. In a first of its kind survey, the article captures practitioner’s belief into SDP and ability of DePaaS to solve some of the known challenges of the field of software defect prediction. This article also provides a novel process for SDP, detailed description of the structure and behaviour of DePaaS architecture components, six best SDP models offered by DePaaS, a description of algorithms that recommend SDP models, feature sets and tunable parameters, and a rich set of challenges to build, use and sustain DePaaS. With the contributions of this article, SDP research and practice could be unified enabling building and using more pragmatic defect prediction models leading to increase in the efficiency of software testing.
Machine-Learning-Assisted Quantum Control in a Random Environment

Disorder in condensed matter and atomic physics is responsible for a great variety of fascinating quantum phenomena, which are still challenging for understanding, not to mention the relevant dynamical control. Here we introduce proof of the concept and analyze a neural-network-based machine-learning algorithm for achieving feasible high-fidelity quantum control of a particle in random environment. To explicitly demonstrate its capabilities, we show that convolutional neural networks are able to solve this problem as they can recognize the disorder and, by supervised learning, further produce the policy for the efficient low-energy cost control of a quantum particle in a time-dependent random potential. We show that the accuracy of the proposed algorithm is enhanced by a higher-dimensional mapping of the disorder pattern and using two neural networks, each properly trained for the given task. The designed method, being computationally more efficient than the gradient-descent optimization, can be applicable to identify and control various noisy quantum systems on a heuristic basis.
Impact of Virtual Imaging Technology on Film and Television Production Education of College Students Based on Deep Learning and Internet of Things

More and more schools begin to design simulation technology based on virtual imaging technology (VIT) and virtual reality (VR) in their course contents. In particular, among these technical courses, there is a need to first strengthen the Film and Television Production (FTP) education in higher institutions. This article aims to study the impact of VRT, VR, and Internet of things (IoT) technology on FTP courses and audience psychology in higher institutions under the era of intelligent multimedia. How to use emerging VR technology to promote the psychological wellbeing of students or patients has become a new research direction, the exploration of which has a far-reaching significance for the applications of the related technologies. First, the principle and applications of VR and IoT technology are described. Thereon, the deep learning (DL)-based training model is used to analyze the postproduction (PP) of VR-based Sand Table game, and the function and effect of the designed game model are discussed. Subsequently, VR-based Sand Play Therapy (SPT) is applied to mentally ill patients to obtain its therapeutic effect. The results show that the designed VR-based Sand Table game model can be used to treat mentally ill patients and alleviate their negative psychological states. Meanwhile, the Test Anxiety Scale (TAS) scores prove the significant therapeutic effect of the designed game model on the mental problems of patients. Therefore, VR-based psychological SPT can be applied in the stress relief of students and the treatment of mentally ill patients, as well as alleviate their mental health problems. This research provides a new direction and some theoretical support for the application field of VR technology.
The potential of precision psychiatry: what is in reach?

Summary Progress in developing personalised care for mental disorders is supported by numerous proof-of-concept machine learning studies in the area of risk assessment, diagnostics and precision prescribing. Most of these studies primarily use clinical data, but models might benefit from additional neuroimaging, blood and genetic data to improve accuracy. Combined, multimodal models might offer potential for stratification of patients for treatment. Clinical implementation of machine learning is impeded by a lack of wider generalisability, with efforts primarily focused on psychosis and dementia. Studies across all diagnostic groups should work to test the robustness of machine learning models, which is an essential first step to clinical implementation, and then move to prospective clinical validation. Models need to exceed clinicians’ heuristics to be useful, and safe, in routine decision-making. Engagement of clinicians, researchers and patients in digitalisation and ‘big data’ approaches are vital to allow the generation and accessibility of large, longitudinal, prospective data needed for precision psychiatry to be applied into real-world psychiatric care.
Blind Remote Sensing Image Deblurring Using Local Binary Pattern Prior

In this paper, an algorithm based on local binary pattern (LBP) is proposed to obtain clear remote sensing images under the premise of unknown causes of blurring. We find that LBP can completely record the texture features of the images, which will not change widely due to the generation of blur. Therefore, LBP prior is proposed, which can filter out the pixels containing important textures in the blurry image through the mapping relationship. The corresponding processing methods are adopted for different types of pixels to cope with the challenges brought by the rich texture and details of remote sensing images and prevent over-sharpening. However, the existence of LBP prior increases the difficulty of solving the model. To solve the model, we construct the projected alternating minimization (PAM) algorithm that involves the construction of the mapping matrix, the fast iterative shrinkage-thresholding algorithm (FISTA) and the half-quadratic splitting method. Experiments with the AID dataset show that the proposed method can achieve highly competitive processing results for remote sensing images.
Synthesized Landing Strategy for Quadcopter to Land Precisely on a Vertically Moving Apron

Quadcopter unmanned aerial vehicles have become increasingly popular for various real-world applications, and a significant body of literature exists regarding the improvement of their flight capabilities to render them fully autonomous. The precise landing onto moving platforms, such as ship decks, is one of the remaining challenges that is largely unresolved. The reason why this operation poses a considerable challenge is because landing performance is considerably degraded by the ground effect or external disturbances. In this paper, we propose a synthesized landing algorithm that allows a quadcopter to land precisely on a vertically moving pad. Firstly, we introduce a disturbance observer-based altitude controller that allows the vehicle to perform robust altitude flight in the presence of external disturbances and the ground effect, strictly proving the system’s stability using Lyapunov’s theory. Secondly, we derive an apron state estimator to provide information on the landing target’s relative position. Additionally, we propose a landing planner to ensure that the landing task is completed in a safe and reliable manner. Finally, the proposed algorithms are implemented in an actual quadcopter, and we demonstrate the effectiveness and applicability of our method through real flight experiments.
Classification of Breast Cancer Images by Implementing Improved DCNN with Artificial Fish School Model

Breast cancer is an important factor affecting human health. This issue has various diagnosis process which were evolved such as mammography, fine needle aspirate, and surgical biopsy. These techniques use pathological breast cancer images for diagnosis. Breast cancer surgery allows the forensic doctor to histologist to access the microscopic level of breast tissues. The conventional method uses an optimized radial basis neural network using a cuckoo search algorithm. Existing radial basis neural network techniques utilized feature extraction and reduction parts separately. It is proposed that it overcomes the CNN approach for all the feature extraction and classification process to reduce time complexity. In this proposed method, a convolutional neural network is proposed based on an artificial fish school algorithm. The breast cancer image dataset is taken from cancer imaging archives. In the preprocessing step of classification, the breast cancer image is filtered with the support of a wiener filter for classification. The convolutional neural network has set the intense data of an image and is used to remove the features. After executing the extraction procedure, the reduction process is performed to speed up the train and test data processing. Here, the artificial fish school optimization algorithm is utilized to give the direct training data to the deep convolutional neural network. The extraction, reduction, and classification of features are utilized in the single deep convolutional neural network process. In this process, the optimization technique helps to decrease the error rate and increases the performance efficiency by finding the number of epochs and training images to the Deep CNN. In this system, the normal, benign, and malignant tissues are predicted. By comparing the existing RBF technique with the cuckoo search algorithm, the presented model attains the outcome in the way of sensitivity, accuracy, specificity, F1 score, and recall.
ACE-HGNN: Adaptive Curvature Exploration Hyperbolic Graph Neural Network

Graph Neural Networks (GNNs) have been widely studied in various graph data mining tasks. Most existing GNNs embed graph data into Euclidean space and thus are less effective to capture the ubiquitous hierarchical structures in real-world networks. Hyperbolic Graph Neural Networks (HGNNs) extend GNNs to hyperbolic space and thus are more effective to capture the hierarchical structures of graphs in node representation learning. In hyperbolic geometry, the graph hierarchical structure can be reflected by the curvatures of the hyperbolic space, and different curvatures can model different hierarchical structures of a graph. However, most existing HGNNs manually set the curvature to a fixed value for simplicity, which achieves a suboptimal performance of graph learning due to the complex and diverse hierarchical structures of the graphs. To resolve this problem, we propose an Adaptive Curvature Exploration Hyperbolic Graph Neural Network named ACE-HGNN to adaptively learn the optimal curvature according to the input graph and downstream tasks. Specifically, ACE-HGNN exploits a multi-agent reinforcement learning framework and contains two agents, ACE-Agent and HGNN-Agent for learning the curvature and node representations, respectively. The two agents are updated by a Nash Q-leaning algorithm collaboratively, seeking the optimal hyperbolic space indexed by the curvature. Extensive experiments on multiple real-world graph datasets demonstrate a significant and consistent performance improvement in model quality with competitive performance and good generalization ability.
Deep Learning Techniques for Automatic Short Answer Grading: Predicting Scores for English and German Answers

We investigate and compare state-of-the-art deep learning techniques for Automatic Short Answer Grading. Our experiments demonstrate that systems based on the Bidirectional Encoder Representations from Transformers (BERT) [1] performed best for English and German. Our system achieves a Pearson correlation coefficient of 0.73 and a Mean Absolute Error of 0.4 points on the Short Answer Grading data set of the University of North Texas [2]. On our German data set we report a Pearson correlation coefficient of 0.78 and a Mean Absolute Error of 1.2 points. Our approach has the potential to greatly simplify the life of proofreaders and to be used for learning systems that prepare students for exams: 31% of the student answers are correctly graded and in 40% the system deviates on average by only 1 point out of 6, 8 and 10 points.
Optical asymmetric single-channel cryptosystem based on QZ synthesis for color images

In this paper, we propose an optical asymmetric single-channel cryptosystem based on QZ synthesis (QZS) for color images. In our cryptosystem, a QZS method for synthesizing color image into the grayscale image is proposed for the first time, thus realizing single-channel encryption. Then the synthesized image is modulated with the random phase masks, and transformed in the Gyrator domain to obtain the cipher image. With the QZS, our cryptosystem can achieve asymmetric and single-channel encryption. Compared with the existing phase truncated-based cryptosystems, our cryptosystem can improve security against the special attack. Numerical simulation results demonstrate the feasibility and robustness of our cryptosystem for encrypting one or even multiple color images.
Netscout Threat Intelligence Report 2H 2020

Cyber criminals are nothing if not entrepreneurial. They have developed business models that sometimes borrow from mainstream enterprises – such as customer support and special offers – and which occasionally show flashes of originality.
Factors influencing the acceptance of smart homes in Jordan

Smart home services are one of the recent internet of things applications that aims to improve residents' quality of life. This study aims to investigate the factors that influence residents' acceptance and usage of smart home services in Jordan. Theoretically, this study extends the technology acceptance model. By following a quantitative method, responses were collected and statistically analysed from 258 household using the SEM-PLS approach. Results show that user awareness, trust, perceived enjoyment, personalisation, perceived usefulness and perceived ease of use significantly influence attitude towards smart home services which, with social influence, impact the intention to use.
Deep learning on time series laboratory test results from electronic health records for early detection of pancreatic cancer

The multi-modal and unstructured nature of observational data in Electronic Health Records (EHR) is currently a significant obstacle for the application of machine learning towards risk stratification. In this study, we develop a deep learning framework for incorporating longitudinal clinical data from EHR to infer risk for pancreatic cancer (PC). This framework includes a novel training protocol, which enforces an emphasis on early detection by applying an independent Poisson-random mask on proximal-time measurements for each variable. Data fusion for irregular multivariate time-series features is enabled by a “grouped” neural network (GrpNN) architecture, which uses representation learning to generate a dimensionally reduced vector for each measurement set before making a final prediction. These models were evaluated using EHR data from Columbia University Irving Medical Center-New York Presbyterian Hospital. Our framework demonstrated better performance on early detection (AUROC 0.671, CI 95% 0.667 – 0.675, p < 0.001) at 12 months prior to diagnosis compared to a logistic regression, xgboost, and a feedforward neural network baseline. We demonstrate that our masking strategy results greater improvements at distal times prior to diagnosis, and that our GrpNN model improves generalizability by reducing overfitting relative to the feedforward baseline. The results were consistent across reported race. Our proposed algorithm is potentially generalizable to other diseases including but not limited to cancer where early detection can improve survival.
Detection of fake news and hate speech for Ethiopian languages: a systematic review of the approaches

With the proliferation of social media platforms that provide anonymity, easy access, online community development, and online debate, detecting and tracking hate speech has become a major concern for society, individuals, policymakers, and researchers. Combating hate speech and fake news are the most pressing societal issues. It is difficult to expose false claims before they cause significant harm. Automatic fact or claim verification has recently piqued the interest of various research communities. Despite efforts to use automatic approaches for detection and monitoring, their results are still unsatisfactory, and that requires more research work in the area. Fake news and hate speech messages are any messages on social media platforms that spread negativity in society about sex, caste, religion, politics, race, disability, sexual orientation, and so on. Thus, the type of massage is extremely difficult to detect and combat. This work aims to analyze the optimal approaches for this kind of problem, as well as the relationship between the approaches, dataset type, size, and accuracy. Finally, based on the analysis results of the implemented approaches, deep learning (DL) approaches have been recommended for other Ethiopian languages to increase the performance of all evaluation metrics from different social media platforms. Additionally, as the review results indicate, the combination of DL and machine learning (ML) approaches with a balanced dataset can improve the detection and combating performance of the system.
Well-Logging-Based Lithology Classification Using Machine Learning Methods for High-Quality Reservoir Identification: A Case Study of Baikouquan Formation in Mahu Area of Junggar Basin, NW China

The identification of underground formation lithology is fundamental in reservoir characterization during petroleum exploration. With the increasing availability and diversity of well-logging data, automated interpretation of well-logging data is in great demand for more efficient and reliable decision making for geologists and geophysicists. This study benchmarked the performances of an array of machine learning models, from linear and nonlinear individual classifiers to ensemble methods, on the task of lithology identification. Cross-validation and Bayesian optimization were utilized to optimize the hyperparameters of different models and performances were evaluated based on the metrics of accuracy—the area under the receiver operating characteristic curve (AUC), precision, recall, and F1-score. The dataset of the study consists of well-logging data acquired from the Baikouquan formation in the Mahu Sag of the Junggar Basin, China, including 4156 labeled data points with 9 well-logging variables. Results exhibit that ensemble methods (XGBoost and RF) outperform the other two categories of machine learning methods by a material margin. Within the ensemble methods, XGBoost has the best performance, achieving an overall accuracy of 0.882 and AUC of 0.947 in classifying mudstone, sandstone, and sandy conglomerate. Among the three lithology classes, sandy conglomerate, as in the potential reservoirs in the study area, can be best distinguished with accuracy of 97%, precision of 0.888, and recall of 0.969, suggesting the XGBoost model as a strong candidate machine learning model for more efficient and accurate lithology identification and reservoir quantification for geologists.