Reviewerly/exHarmony huggingface dataset:
EFEKTIVITAS PEMBELAJARAN DARING PROGRAM STUDI PENDIDIKAN EKONOMI

The Covid-19 virus has changed the learning process around the world, Efforts made during a pandemic are online learning. Where the online learning system can be done without having to meet face to face but through virtual classes through online media. The aim of this research is to get an overview of the implementation of online learning in the Economic Education Study Program of STKIP 'Aisyiyah Riau. The subjects in this study were all students of the Economic Education Study Program. The method of data collection is done by using the interview method via the zoom meeting app and by telephone. Data were analyzed in three stages, namely the process of data reduction, data presentation and drawing conclusions or verification. The results of this study indicate that: (1) Online learning was very effective in the Covid-19 period which could increase motivation and comfort in the learning process; (2) overall students already have the means to support online learning; (3) Quota fees that must be paid by students for online learning are quite expensive; (4) Online learning can reduce the spread of Covid-19 in STKIP 'Aisyiyah RiauKeywords : Effectiveness, Online learning, Covid-19
Parameter optimization in satellite-based measurement-device-independent quantum key distribution

Abstract The satellite-based measurement-device-independent quantum key distribution can promote the realization of quantum communication networks. Under the condition of the limited data set, it is necessary to optimize all parameters. For communication networks, real-time prediction and optimization are also indispensable. With the development of machine learning, cross-combination with machine learning has also become the mainstream of parameter optimization in various disciplines. This paper discusses the asymmetric MDI-QKD based on the satellite in the case of statistical fluctuations, and uses the local search algorithm to achieve full parameter optimization under the condition of considering the probability of sending the signal. Compared with fixed related parameters, the key rate is increased by an order of magnitude. On this basis, random forest is used to predict the high-precision optimal parameters, thereby eliminating the simulation and iteration required by the search method to meet the real-time optimization of the future QKD network.
An Intuitionistic Calculus to Complex Abnormal Event Recognition on Data Streams

Data mining in real-time data streams is associated with multiple types of uncertainty, which often leads the respective categorizers to make erroneous predictions related to the presence or absence of complex events. But recognizing complex abnormal events, even those that occur in extremely rare cases, offers significant support to decision-making systems. Therefore, there is a need for robust recognition mechanisms that will be able to predict or recognize when an abnormal event occurs or will occur on a data stream. Considering this need, this paper presents an Intuitionistic Tumbling Windows event calculus (ITWec) methodology. It is an innovative data analysis system that combines for the first time in the literature a set of multiple systems for Complex Abnormal Event Recognition (CAER). In the proposed system, the probabilities of the existence of a high-level complex abnormal event for each period are initially calculated nonparametrically, based on the probabilities of the low-level events associated with it. Because cumulative results are sought in consecutive, nonoverlapping sections of the data stream, the method uses the clearly defined rules of initialization and termination of the tumbling windows method, where there is an explicit determination of the time interval within which several blocks of a particular stream are investigated window. Finally, the number of maximum probable intervals in which an event is likely to occur based on a certain probability threshold is calculated, based on a parametric representation of intuitively fuzzy sets.
Quantum network recovery from multinode failure using network encoding with GHZ states on higher-order butterfly networks

We propose a protocol to transmit three quantum states crossly in a butterfly network with prior entanglement, in the form of GHZ states, between three senders. The proposed protocol requires only one qubit transmission or two classical bits transmission in each channel of the network. We generalize this protocol to higher number of qubits with multiqubit GHZ states towards quantum network operability using network coding with multiqubit GHZ states on higher-order butterfly networks.
FraudMove: Fraud Drivers Discovery Using Real-Time Trajectory Outlier Detection

Taxicabs and rideshare cars nowadays are equipped with GPS devices that enable capturing a large volume of traces. These GPS traces represent the moving behavior of the car drivers. Indeed, the real-time discovery of fraud drivers earlier is a demand for saving the passenger’s life and money. For this purpose, this paper proposes a novel time-based system, namely FraudMove, to discover fraud drivers in real-time by identifying outlier active trips. Mainly, the proposed FraudMove system computes the time of the most probable path of a trip. For trajectory outlier detection, a trajectory is considered an outlier trajectory if its time exceeds the time of this computed path by a specified threshold. FraudMove employs a tunable time window parameter to control the number of checks for detecting outlier trips. This parameter allows FraudMove to trade responsiveness with efficiency. Unlike other related works that wait until the end of a trip to indicate that it was an outlier, FraudMove discovers outlier trips instantly during the trip. Extensive experiments conducted on a real dataset confirm the efficiency and effectiveness of FraudMove in detecting outlier trajectories. The experimental results prove that FraudMove saves the response time of the outlier check process by up to 65% compared to the state-of-the-art systems.
Combining Curriculum Learning and Knowledge Distillation for Dialogue Generation

Curriculum learning, a machine training strategy that feeds training instances to the model from easy to hard, has been proven to facilitate the dialogue generation task.Meanwhile, knowledge distillation, a knowledge transformation methodology among teachers and students networks can yield significant performance boost for student models.Hence, in this paper, we introduce a combination of curriculum learning and knowledge distillation for efficient dialogue generation models, where curriculum learning can help knowledge distillation from data and model aspects.To start with, from the data aspect, we cluster the training cases according to their complexity, which is calculated by various types of features such as sentence length and coherence between dialog pairs.Furthermore, we employ an adversarial training strategy to identify the complexity of cases from model level.The intuition is that, if a discriminator can tell the generated response is from the teacher or the student, then the case is difficult that the student model has not adapted to yet.Finally, we use selfpaced learning, which is an extension to curriculum learning to assign weights for distillation.In conclusion, we arrange a hierarchical curriculum based on the above two aspects for the student model under the guidance from the teacher model.Experimental results demonstrate that our methods achieve improvements compared with competitive baselines.
Using Innovation to Develop Digital Tools for Public Health During the COVID-19 Pandemic

Introduction: Technology has played a key role in enabling public health to respond to the COVID-19 pandemic at a pace and scale never seen before. The Digital Health and Care Innovation Centre (DHI) assisted with development of two new digital services to enable testing and contact tracing at scale using innovative methods. Methods: The DHI employed a design innovation approach by bringing all relevant stakeholders together to co-design new technology services to identify the ‘preferred future’. Workshops were used to identify the preferred solutions. The innovative methods for development of digital health tools included adopting an iterative approach, addressing the situational requirements posed by COVID-19, and democratising technology for purposes of pandemic control. Results: A National Notification Service (NNS) for automation of delivery and feedback (if results messages were viewed) was developed and adopted by five of the 14 health boards in Scotland, processing over 7 million results since inception. The Simple Tracing Tools (STT) is an open-platform web-based app that is designed for data entry by contact tracing teams. STT was adopted by all local health protection teams and informed development of the national case management system. Discussion: The Cynefin framework can be used to understand the design innovation process when facing the challenges of designing digital tools during a pandemic. There are significant opportunities for public health to engage with digital health to transform the pandemic response and derive benefit for tackling future population health challenges.
Use of Telepathology to Facilitate COVID-19 Research and Education through an Online COVID-19 Autopsy Biorepository

The coronavirus disease 2019 (COVID-19) pandemic has increased the use of technology for communication including departmental conferences, working remotely, and distance teaching. Methods to enable these activities should be developed and promulgated.To repurpose a preexisting educational website to enable the development of a COVID-19 autopsy biorepository to support distance teaching and COVID-19 research.After consent was obtained, autopsies were performed on patients with a confirmed positive severe acute respiratory syndrome coronavirus-2 reverse-transcriptase-polymerase-chain reaction test. Autopsies were performed according to a COVID-19 protocol, and all patients underwent both gross and microscopic examination. The H and E histology slides were scanned using a Leica Biosystems Aperio CS ScanScope whole slide scanner and the digital slide files were converted to deep zoom images that could be uploaded to the University of Alabama at Birmingham (UAB) Pathology Educational Instructional Resource website where virtual microscopy of the slides is available.A total of 551 autopsy slides from 24 UAB COVID-19 cases, 1 influenza H1N1 case and 1 tuberculosis case were scanned and uploaded. Five separate COVID-19 research teams used the digital slides remotely with or without a pathologist on a Zoom call. The scanned slides were used to produce one published case report and one published research project. The digital COVID-19 autopsy biorepository was routinely used for educational conferences and research meetings locally, nationally and internationally.The repurposing of a pre-existing website enabled telepathology consultation for research and education purposes. Combined with other communication technology (Zoom) this achievement highlights what is possible using pre-existing technologies during a global pandemic.
Differential Privacy Protection of Face Images Based on Region Growing

Face images, as an information carrier, are rich in sensitive information. Direct publication of these images would cause privacy leak, due to their natural weak privacy. Most of the existing privacy protection methods for face images adopt data publication under a non-interactive framework. However, the E-effect under this framework covers the entire image, such that the noise influence is uniform across the image. To solve the problem, this paper proposes region growing publication (RGP), an algorithm for the interactive publication of face images under differential privacy. This innovative algorithm combines the region growing technique with differential privacy technique. The privacy budget E is dynamically allocated, and the Laplace noise is added, according to the similarity between adjacent sub-images. To measure this similarity more effectively, the fusion similarity measurement mechanism (FSMM) was designed, which better adapts to the intrinsic attributes of images. Different from traditional region growing rules, the FSMM fully considers various attributes of images, including brightness, contrast, structure, color, texture, and spatial distribution. To further enhance algorithm feasibility, RGP was extended to atypical region growing publication (ARGP). While RGP limits the region growing direction between adjacent sub-images, ARGP searches for the qualified sub-images across the image, with the aid of the exponential mechanism, thereby expanding the region merging scope of the seed point. The results show that our algorithm can satisfy E-differential privacy, and the denoised image still have a high availability.
Developing a Conversational Agent’s Capability to Identify Structural Wrongness in Arguments Based on Toulmin’s Model of Arguments

This article discusses the usefulness of Toulmin's model of arguments as structuring an assessment of different types of wrongness in an argument. We discuss the usability of the model within a conversational agent that aims to support users to develop a good argument. Within the article, we present a study and the development of classifiers that identify the existence of structural components in a good argument, namely a claim, a warrant (underlying understanding), and evidence. Based on a dataset (three sub-datasets with 100, 1,026, 211 responses in each) in which users argue about the intelligence or non-intelligence of entities, we have developed classifiers for these components: The existence and direction (positive/negative) of claims can be detected a weighted average F1 score over all classes (positive/negative/unknown) of 0.91. The existence of a warrant (with warrant/without warrant) can be detected with a weighted F1 score over all classes of 0.88. The existence of evidence (with evidence/without evidence) can be detected with a weighted average F1 score of 0.80. We argue that these scores are high enough to be of use within a conditional dialogue structure based on Bloom's taxonomy of learning; and show by argument an example conditional dialogue structure that allows us to conduct coherent learning conversations. While in our described experiments, we show how Toulmin's model of arguments can be used to identify structural problems with argumentation, we also discuss how Toulmin's model of arguments could be used in conjunction with content-wise assessment of the correctness especially of the evidence component to identify more complex types of wrongness in arguments, where argument components are not well aligned. Owing to having progress in argument mining and conversational agents, the next challenges could be the developing agents that support learning argumentation. These agents could identify more complex type of wrongness in arguments that result from wrong connections between argumentation components.
DEPENDENCIES BETWEEN MODEL INDICATORS OF THE BASIC AND THE SPECIALIZED SPEED IN HOCKEY PLAYERS AGED 13-14

PURPOSE The aim of our research is to establish the degrees and directions of dependence between the main forms of manifestation of the basic and specialized speed. The subject was 20 boys born in 2005 and 2006. METHODS Through tests and variance analysis (VA) we determine the level of the main forms of the basic and specialized speed, and through correlation analysis (CA) and intercriteria analysis (ICrA) we establish the existence of possible connections and degrees of dependence between them. RESULTS: By using VA we were able to characterize the average level, the level of dispersion and the distribution of the values of the studied indicators. Correlations and dependencies have been established by using CA, traditionally applied by sports science. We have also applied the innovative ICrA in order to confirm or reject the established correlations, directions and degrees of dependence. CONCLUSIONS: The conducted CA and ICrA have shown that the means for the development of the basic linear speed (BLS) and specialized linear speed (SLS) do not have a significant impact on the development of the special complex speed. ICrA also indicates a lack of correlations and dependencies between BLS and SLS, which, however, is not confirmed by CA.
NFAD: fixing anomaly detection using normalizing flows

Anomaly detection is a challenging task that frequently arises in practically all areas of industry and science, from fraud detection and data quality monitoring to finding rare cases of diseases and searching for new physics. Most of the conventional approaches to anomaly detection, such as one-class SVM and Robust Auto-Encoder, are one-class classification methods, i.e., focus on separating normal data from the rest of the space. Such methods are based on the assumption of separability of normal and anomalous classes, and subsequently do not take into account any available samples of anomalies. Nonetheless, in practical settings, some anomalous samples are often available; however, usually in amounts far lower than required for a balanced classification task, and the separability assumption might not always hold. This leads to an important task-incorporating known anomalous samples into training procedures of anomaly detection models. In this work, we propose a novel model-agnostic training procedure to address this task. We reformulate one-class classification as a binary classification problem with normal data being distinguished from pseudo-anomalous samples. The pseudo-anomalous samples are drawn from low-density regions of a normalizing flow model by feeding tails of the latent distribution into the model. Such an approach allows to easily include known anomalies into the training process of an arbitrary classifier. We demonstrate that our approach shows comparable performance on one-class problems, and, most importantly, achieves comparable or superior results on tasks with variable amounts of known anomalies.
Using Ultrasound-Based Multilayer Perceptron to Differentiate Early Breast Mucinous Cancer and its Subtypes From Fibroadenoma

Mucinous breast cancer (MBC), particularly pure MBC (pMBC), often tend to be confused with fibroadenoma (FA) due to their similar images and firm masses, so some MBC cases are misdiagnosed to be FA, which may cause poor prognosis. We analyzed the ultrasonic features and aimed to identify the ability of multilayer perceptron (MLP) to classify early MBC and its subtypes and FA.The study consisted of 193 patients diagnosed with pMBC, mMBC, or FA. The area under curve (AUC) was calculated to assess the effectiveness of age and 10 ultrasound features in differentiating MBC from FA. We used the pairwise comparison to examine the differences among MBC subtypes (pure and mixed types) and FA. We utilized the MLP to differentiate MBC and its subtypes from FA.The nine features with AUCs over 0.5 were as follows: age, echo pattern, shape, orientation, margin, echo rim, vascularity distribution, vascularity grade, and tumor size. In subtype analysis, the significant differences were obtained in 10 variables (p-value range, 0.000-0.037) among pMBC, mMBC, and FA, except posterior feature. Through MLP, the AUCs of predicting MBC and FA were both 0.919; the AUCs of predicting pMBC, mMBC, and FA were 0.875, 0.767, and 0.927, respectively.Our study found that the MLP models based on ultrasonic characteristics and age can well distinguish MBC and its subtypes from FA. It may provide a critical insight into MBC preoperative clinical management.
A Secure and Efficient Image Transmission Scheme Based on Two Chaotic Maps

The application of multimedia sensors is widespread, and people need to transmit images more securely and efficiently. In this paper, an image transmission scheme based on two chaotic maps is proposed. The proposed scheme consists of two parts, secure image transmission between sensor nodes and sink nodes (SIT-SS) and secure image transmission between sensor nodes and receivers (SIT-SR). For resource-constrained environments, SIT-SS utilizes Tent-Logistic Map (TLM) to generate chaotic sequences and adopts TLM-Driven permutation and transformation to confuse image pixels. Then the cipher image is obtained through TLM-Driven two-dimensional compressed sensing. Compared with existing schemes, the secret key design of SIT-SS is more reasonable and requires fewer hardware resources. When sampling ratio is greater than 0.6, its image reconstruction quality has obvious advantages. For environments with huge security threats, SIT-SR adopts dynamic permutation and confusion based on discrete logarithms to confuse the image and exploits dynamic diffusion based on discrete logarithms to generate final cipher image. Similarly, compared with some existing schemes, the design of SIT-SR is more practical, and the statistical characteristics of the cipher image are better. Finally, extensive simulation tests confirm the superiority of the proposed scheme.
A novel PID-like particle swarm optimizer: on terminal convergence analysis

Abstract In this paper, a novel proportion-integral-derivative-like particle swarm optimization (PIDLPSO) algorithm is presented with improved terminal convergence of the particle dynamics. A derivative control term is introduced into the traditional particle swarm optimization (PSO) algorithm so as to alleviate the overshoot problem during the stage of the terminal convergence. The velocity of the particle is updated according to the past momentum, the present positions (including the personal best position and the global best position), and the future trend of the positions, thereby accelerating the terminal convergence and adjusting the search direction to jump out of the area around the local optima. By using a combination of the Routh stability criterion and the final value theorem of the Z -transformation, the convergence conditions are obtained for the developed PIDLPSO algorithm. Finally, the experiment results reveal the superiority of the designed PIDLPSO algorithm over several other state-of-the-art PSO variants in terms of the population diversity, searching ability and convergence rate.
Toward a quantum computing algorithm to quantify classical and quantum correlation of system states

Optimal measurement is required to obtain the quantum and classical correlations of a quantum state, and the crucial difficulty is how to acquire the maximal information about one system by measuring the other part; in other words, getting the maximum information corresponds to preparing the best measurement operators. Within a general setup, we designed a variational hybrid quantum-classical (VHQC) algorithm to achieve classical and quantum correlations for system states under the Noisy-Intermediate Scale Quantum (NISQ) technology. To employ, first, we map the density matrix to the vector representation, which displays it in a doubled Hilbert space, and it's converted to a pure state. Then we apply the measurement operators to a part of the subsystem and use variational principle and a classical optimization for the determination of the amount of correlation. We numerically test the performance of our algorithm at finding a correlation of some density matrices, and the output of our algorithm is compatible with the exact calculation.