Reviewerly/exHarmony huggingface dataset:
Comparison of Hidden Markov Models and the FAST Algorithm for Feature-Aware Knowledge Tracing

In many Indian rural schools, individual students do not receive adequate attention due to the high student–teacher ratio. It is anonerous task for teachers to assess their students’ knowledge levels, and identify their deficient areas of learning. An Intelligent Tutoring System (ITS) enables the teacher to create a report on topics, which students need to study in more detail. Knowledge tracing approaches are a good option for the generation of such reports. For the current paper, we analyze first-grade students from 28 schools, who use Amrita Learning, an ITS developed by Amrita University. There were 211,275 responses obtained in a single academic year. The performance of three knowledge tracing approaches were compared using this dataset: standard Bayesian Knowledge Tracing, Feature-Aware Student Knowledge Tracing (FAST) and Hidden Markov Model (HMM). We find that the HMM approach marginally outperforms the other two methods.
Vote-from-Home? Evaluation Framework for Election Security on Remote Voting in Response to COVID-19

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Developing a Case-based Emergency Response System with Adaptive Case Management

Abstract The emergency responses required during large-scale crises or disasters are extremely knowledge-intensive processes and are usually characterized by a high degree of unpredictability and unrepeatability. An emergency response is mission- and time-critical, unstructured, very dynamic, and it is very difficult to predefine or even to anticipate all possible response scenarios. Therefore, designing and implementing a software system to support such a response system is highly complicated and challenging. This research aims to investigate and discuss how Adaptive Case Management (ACM) can be leveraged in the design and implementation of a case-based emergency response system. In particular, this research considers the best practices of the National Incident Management System (NIMS), which is an essential part of the National Response Framework (NRF) developed in the United States. As a proof-of-concept, a prototype demonstration has been carried out on a leading commercial ACM platform. In addition, a walkthrough scenario is discussed to elaborate how ACM can support emergency response activities in real settings using the Incident Command System (ICS) organizational structure. The key benefit of this research is to guide the development and implementation of cased-based emergency response systems with a flexible and agile approach.
An Accuracy Improvement of the Neuromorphic Functional Models by Using the Parallel ANN Architecture

Enhancement of the up-to-date computing systems in performance and memory capacity stimulates development of new mathematical models and methods for numerical simulation. Machine learning methods are widely used nowadays in the electronic design automation. New mathematical entities are focused onto increasing the design quality and reducing a time cost. A method of constructing the neuromorphic functional models (NFM) for analog components and functional blocks is proposed. An approach to improvement of the NFM accuracy by partitioning the domain of definition for output characteristics according to the threshold coefficient and using the parallel artificial neural network (ANN) architecture is offered. The automated synthesis route of the NFM is represented. The results of experimental study for semiconductor diode and the voltage rectifier circuit are demonstrated. The accuracy increasing of the synthesized NFM and circuit simulation results shown high efficiency of the proposed method.
Institutionalized Cloud Clients

The paradigm of of cloud computing have brought with it the challenge of vendor lock-in, preventing customers from choose from alternative vendors and induces substantial costs for switching provider. Since only a limited number of in-depth studies who investigate the complexity of cloud lock-in problem within enterprise organizations exist, this thesis tries to describe the measures taken by companies to avoid the issue of vendor lock-in from a strategic perspective. The factors of measures taken were seen as Informed decision-making, Contract review and negotiation, and Risk mitigation. The study found that vendor lock-in is of secondary concern for deciding the actions of business enterprises, but that standard operating procedures and by prioritizing functionality and usability vendor lock-in risks can be lowered. (Less)
Efektivitas Media Video Pembelajaran Pembuatan Chiffon Cake Terhadap Hasil Belajar Mahasiswa D3 Tata Boga Pada Mata Kuliah Kue Kontinental

The purpose of this research is to study the effectiveness of video learning media for making chiffon cakes for learning outcomes from D3 Food Catering students in continental pastry class, Universitas Negeri Jakarta. The population in management of continental pastry class is students who are taking continental cake class, and the respondent of this research is 44 student. The method used in the this research is quasi experiment. The results of this reserach are that the experimental class group learning outcomes show an average value of 67.65 for pre-test 80.24 for post-test scores increasing 12.58 or 15.69% and for the control class of 67.82 for pre-test 74.29 for post-test scores increasing 6.46 or 8.71% and for the group skills in experimental class, the average value was 86.13 and for the control group was 79.13. Thus it can be conluded that there is an influence from learning video about chiffon cake making towards the learning outcomes of food and nutrition student in continental pastry class.
Autonomous Extraction of Gleason Patterns for Grading Prostate Cancer using Multi-Gigapixel Whole Slide Images.

Prostate cancer (PCa) is the second deadliest form of cancer in males. The severity of PCa can be clinically graded through the Gleason scores obtained by examining the structural representation of Gleason cellular patterns. This paper presents an asymmetric encoder-decoder model that integrates a novel hierarchical decomposition block to exploit the feature representations pooled across various scales and then fuses them together to generate the Gleason cellular patterns using the whole slide images. Furthermore, the proposed network is penalized through a novel three-tiered hybrid loss function which ensures that the proposed model accurately recognizes the cluttered regions of the cancerous tissues despite having similar contextual and textural characteristics. We have rigorously tested the proposed network on 10,516 whole slide scans (containing around 71.7M patches), where the proposed model achieved 3.59\% improvement over state-of-the-art scene parsing, encoder-decoder, and fully convolutional networks in terms of intersection-over-union.
Identification of Reptile Species Using Convolutional Neural Networks (CNN)

Reptiles are one of the most common fauna in the territory of Indonesia. quite a lot of people who have an interest in knowing more about this fauna in order to increase knowledge. Based on previous research, Deep Learning is needed in particular the CNN method for computer programs to identify reptile species through images. This reseacrh aims to determine the right model in producing high accuracy in the identification of reptile species. Thousands of images are generated through data augmentation processes for manually captured images. Using the Python programming language and Dropout technique, an accuracy of 93% was obtained by this research in identifying 14 different types of reptiles.&#x0D;
Effect of climate change on morphological characteristics of the soils of Vaigai River Basin, Alluvial Tract, Madurai District, Tamil Nadu

The pedological characterization of alluvial tract soils of Vaigai river basin, Madurai District, Tamil Nadu was taken during 2019-20.Initially a Reconnaissance Soil Survey was carried out with Toposheet as a base map.Totally nine representative pedons in Northern and Southern bank of the Vaigai river alluvial basin were selected and studied for their morphological characteristics.Along the course of Vaigai river basin, deposition of alluvial soil was evidenced by the poorly developed AC profiles in both side.The depth of the soil varied from d4 to d5 in both banks.The color of the pedons varies with hue ranging from 10YR to 7.5YR, value 2 to 6and chroma 2-8 and in Northern bank ; from 10YR to 7.5YR,values varies by 2.5 to 6 and chroma by 1 to 6 in the Southern bank.The texture of soil varied from weak to structure less.Some yellowish brown mottles with jarosite were foundin the Northern bank and micaceous coatings found in the Southern banks.The calcium carbonate nodules were found in most of the pedons and the permeability of the soil was very rapid.Coconut, Banana and paddy are the major crops grown in this alluvial tract.Climate change, urbanization, is short of monsoon rain and industrialization had created the major impact on the shrinkage of land under cultivation and smash up the soil health.The present study on characteristics of alluvial tract of Vaigai River basin both in Northern and southern banks understood about the change due to anthropogenic activities and climate change scenario.
CogQN: A Queueing Model that Captures Human Learning of the User Interfaces of Session-Based Systems

A session-based system provides various services to its end users through user interfaces. A novice user of a service’s user interface takes more think time—the time to comprehend the content, and the layout of graphical elements, on the interface—in comparison to expert users. The think time gradually decreases, as she repeatedly comprehends the same interface, over time. This decrease in think time is the user learning phenomenon. Owing to this learning behavior, the proportion of users—at various learning levels for different services—changes dynamically leading to a difference in the workload. Traditionally though, workload specifications (required for system performance evaluation) never accounted for user learning behavior. They generally assumed a global mean think time, instead. In this work, we propose a novel queueing network (QN) model called CogQN that accounts for user learning. It is a multi-class QN model where each service and its learning level constitute a class of users for the service. The model predicts overall mean response times across different learning modes within 10% error in comparison to empirical data.
GPUs Utilization of Residual Network Training for Colon Histopathological Images Classification

Cancer is still one of the diseases with a high mortality rate in the world. Histopathological image is one image that can be used to analyze the presence of Cancer in the human body. The deep learning approach as state of the art was conducted by researchers to investigate the image of Cancer. One of the deep learning architectures is Residual Network (ResNet). This architecture has the characteristics of additional input on the layer, when the training process which has an impact on the memory and processor extension during the training process. In this work, we propose the parallelization of the ResNet model by using three GTX-1080 of Graphics Processing Units (GPUs) to carry out the training process. The performance of all three GPUs can be seen from the utilization of the GPU processor and memory and speed up during the training. The advantage of using parallelization with multiple GPUs is to overcome the out of memory in larger batch-size usage that cannot be handled by the use of a single GPU. This study uses various batch-sizes ranging from 8,16,24 and 32 as research scenarios. The results showed that the utilization of processor and memory is more efficient for larger batch-size. As a result, the average utilization of processors for GPU 1, 2, and 3 is 66%, 61.5%, and 81.5%, respectively. Meanwhile, memory GPU utilization is 44%, 40%, and 48.2%.
On using an iterative learning approach

Investigated are possibilities and features of using the iterative approach, when teaching at university. The article reviews and analyzes data of scientific publications and generalized practical experience. It is shown, that the iterative approach naturally follows from modern teaching concept: “self-study — solution — check — correction”. The importance of closing feedback loop and multiple attempts to obtain correct solution in an iterative approach is noted. Considered is change in the role of teacher in the context of the iterative approach; features of self-study by students of educational material, organization of checking correctness of the task, problems of creating necessary motivation for learning and possibility of transition to distance learning. It is shown, that the iterative approach contributes to individualization of teaching, improving degree of understanding of educational material and development of students’ thinking. In addition, the iterative approach facilitates formation of skills and abilities to rationally organize educational activities and self-master knowledge. The conclusion is made about effectiveness of using the iterative approach in university educational process.
The verification of a requirement of entanglement measures

The quantification of quantum entanglement is a difficult and fundamental question in quantum information theory. We devote to the refinement of axiomatic approach of quantifying entanglement. Recently, Gao et al. (Phys Rev Lett 112:180501, 2014) pointed out that the maximum of entanglement measure of the permutational invariant part $$\rho ^{\mathrm {PI}}$$ of a state $$\rho $$ ought to be a lower bound on entanglement measure of the original state $$\rho $$ . They further argued to add this result as requirement on any (multipartite) entanglement measure. Whether any individual proposed entanglement measure satisfies the new requirement still has to be proved. In this paper, we show that most existing entanglement measures of bipartite quantum systems satisfy the new criterion, including all convex-roof entanglement measures, the relative entropy of entanglement, the negativity, the logarithmic negativity and the logarithmic convex-roof extended negativity. Our approach gives a refinement in quantifying entanglement and provides new insights on better understanding of entanglement properties of composite quantum systems.
Handwritten Numeric Image Classification with Quantum Neural Network using Quantum Computer Circuit Simulator

Quantum Computer is a computer machine using principles of quantum mechanics in doing its computation.The Quantum Computer Machine itself is still in the development stage and has not been deployed yet, however TensorFlow provides a library for hybrid quantum-classical machine learning called TensorFlow Quantum (TFQ).One of the quantum computing models is the Quantum Neural Network (QNN).QNN is adapted from classical neural networks capable of processing qubit data and passing quantum circuits.QNN is a machine learning model that allows quantum computers to classify image data.The image data used is classical data, but classical data cannot reach a superposition state.So in order to carry out this protocol, the data must be readable into a quantum device that provides superposition.QNN uses a supervised learning method to predict image data.Quantum Neural Network (QNN) with a supervised learning method for classifying handwritten numeric image data is implemented using a quantum computer circuit simulation using the Python 3.6 programming language.The quantum computer circuit simulation is designed using library Cirq and TFQ.The classification process is carried out on Google Colab.The results of training on the QNN model obtained a value of 0.337 for the loss value and 0.3427 for the validation loss value.Meanwhile, the hinge accuracy value from the training results is 0.8603 for the hinge accuracy value with training data and 0.8669 for the hinge accuracy validation value.Model testing is done by providing 100 handwritten number images that are tested, with 53 image data of number three and 47 image data of number six.The results obtained for the percentage of testing accuracy are 100% for the number three image and 100% for the number six image.Thus, the total percentage of testing is 100%.
Converging evidence

Abstract New languages emerge through interactions among people, yet the role of social network structure in language emergence is not clear, despite research from experimental semiotics, observational fieldwork, and computational modeling. To better understand the effects of social network structure on the formation of conventional referring expressions, we use a silent gesture paradigm that combines the methodological control of experimental semiotics and computational simulations with the naturalistic affordances of the human body, physical environment, and interpersonal communication. We elicited gestural referring expressions from hearing participants randomly assigned to either a richly- or sparsely-connected communicative network. Results demonstrate greater conventionalization among participants in the richly-connected condition, although this effect disappears after accounting for between-condition differences in overall number of communicative interactions. These results provide the first experimental demonstration that communicative network structure causally impacts the conventionalization of referring expressions in human participants, using a communicative modality in which human language naturally arises.
Fast generation of microwave photon Fock states in a superconducting nanocircuit

Optimal generation of photon Fock states is of significance for quantum information processing and state engineering. Here an efficient scheme is proposed for fast generation of photon Fock states in a superconducting nanocircuit via counterdiabatic driving. A Cooper-pair box circuit, behaving as an effective three-level artificial atom, is driven by a quantized cavity mode and two classical fields. By the technique of shortcuts to adiabaticity with counterdiabatic driving, we first generate a single-photon Fock state by performing population transfer within a composite system, consisting of the atom qutrit and microwave photons. Assisted by an auxiliary driving to initialize atom state, rapid generation of multi-photon Fock states can be addressed sequentially. Compared with an adiabatic counterpart, the shortcut-based operation in a shorter time contributes to enhance fidelity. The strategy could offer a promising avenue towards optimized creation of microwave photon Fock states with superconducting quantum circuits.
Multi-Task Siamese Neural Network for Improving Replay Attack Detection

Automatic speaker verification systems are vulnerable to audio replay attacks which bypass security by replaying recordings of authorized speakers. Replay attack detection (RA) detection systems built upon Residual Neural Networks (ResNet)s have yielded astonishing results on the public benchmark ASVspoof 2019 Physical Access challenge. With most teams using fine-tuned feature extraction pipelines and model architectures, the generalizability of such systems remains questionable though. In this work, we analyse the effect of discriminative feature learning in a multi-task learning (MTL) setting can have on the generalizability and discriminability of RA detection systems. We use a popular ResNet architecture optimized by the cross-entropy criterion as our baseline and compare it to the same architecture optimized by MTL using Siamese Neural Networks (SNN). It can be shown that SNN outperform the baseline by relative 26.8 % Equal Error Rate (EER). We further enhance the model's architecture and demonstrate that SNN with additional reconstruction loss yield another significant improvement of relative 13.8 % EER.
RETRACTED: Exploration and Research of Information Technology in College English Teaching

Abstract With social progress, economic and cultural development, the world has moved towards economic integration and cultural diversity. English is of great practical significance in promoting students’ employment and social harmony. College English teaching should not only focus on language teaching itself, but also include many western cultures. The traditional college English education in our country focuses on the test-oriented education, and the students only pay for passing the test.So, dumb English is very serious. With the development of Internet technology, college English education needs to transform and reform in the multicultural perspective. English education must adapt to the communication mode of the Internet. At the same time, college English education should pay attention to the influence on multi-culture in the Internet era. This paper analysis the influence of multi-culture English education based on the Internet + era. And then it analysis the problems in multi-culture English education. Then, it explores the application of information technology in multicultural English teaching. Finally, the article puts forward some suggestions on how to better combine multicultural English teaching and computer technology.
Face processing in free viewing visual search: An investigation using concurrent EEG and eye movement recordings

The neural underpinnings of face processing have largely been investigated under fixed-gaze. The electrophysiological hallmark of face processing is the N170 component, which emerges in the occipitotemporal cortex around 170 ms after stimulus onset and is characterized by a larger amplitude in response to pictures of faces, compared to other object categories. Several properties modulating this component have been extensively studied, including its sensitivity to face inversion and low-level manipulations (see, e.g. Rossion, 2015). However, little is known about face processing in natural viewing. A recent study (Kamienkowski, Varatharajah, Sigman & Ison, 2018) reported strong fixation-related potentials (fERPs) to faces in a free-viewing visual search task and a significant difference between target and distractor faces around 170 ms after fixation. In this study, we co-registered EEG and eye tracking to investigate fERPs to pictures of faces and objects during visual search and free exploration. Participants were asked to search for one target face or object stimulus during visual search blocks (VS) and to explore an array of faces and objects embedded in random noise during free exploration (EXP). We hypothesized that a larger N170 would be elicited by fixations to faces in comparison to objects. Importantly, based on a recently proposed framework (Kamienkowski et al., 2018), we also hypothesized that the early target detection that has previously been reported largely reflects saccade inhibition, and would therefore be activated differently for easy distractors (different categories) than hard distractors (same category). Preliminary analyses of the fERPs show robust early potentials, but no differential activation for different categories. Possible interpretations in terms of differences in low-level features, reduced signal to noise ratio and superposition of brain potentials are discussed.