Reviewerly/exHarmony huggingface dataset:
Development of Physics Learning Media Based on Self-Efficacy Use Mobile Augmented Reality for Senior High School

Abstract It was found that the value of self efficacy in physics learning in high school (SMA) is still low in one school. Self-efficacy was found at the level of 48%. And this is supported by OECD data (3) which also states that the performance of Indonesian science more than 55% of students score below level 2 out of 5 levels. The development of self-efficacy media in learning physics astronomy is an effort made to improve students’ self-efficacy. Media self-efficacy is designed using mobile augmented reality which is considered capable of giving students a better learning experience so as to be able to improve student learning abilities so that students’ self-efficacy also increases. To be able to improve self-efficacy better this media must be supported by teacher assistance as a motivator to students so that the effects given are even better.
Manufacture of custom‐made spectacles using three‐dimensional printing technology

Optical rehabilitation of aphakia after congenital cataract surgery is crucial.2010 Contact lenses are a good option in rehabilitation of paediatric aphakia, but insertion of contact lenses in micr...
Improving Sensor-Based Affect Detection with Multimodal Data Imputation

Utilizing sensors for affect detection in adaptive learning technologies has been the subject of growing interest in recent years. This extends to the collection of multiple concurrent sensor-based input channels to enable multimodal affective modeling. However, sensors pose significant challenges to affect detection, including sensor connectivity issues, background noise, inconsistent data logging, and loss of data due to hardware failure. In this paper, we introduce a framework for multimodal data imputation to improve automated detection of student affect in adaptive learning technologies. Through the use of an autoencoder neural network trained on Microsoft Kinect-based posture data and electrodermal activity data with synthetic noise injection, we approximate missing values within the original dataset while still preserving the inter-related context between features when reconstructing the dataset. The reconstructed dataset can be used in conjunction with multimodal data fusion techniques to further boost affect detector accuracy. Results indicate that this framework improves the effectiveness of multimodal affect detectors when compared to unimodal baseline models, as well as models using baseline data imputation techniques such as mean imputation. Further, it maintains cross-modality information that influences the multimodal affect detectors' performance, as the approach also outperforms previous work using the latent representation of the imputed dataset as training data instead of a complete reconstruction of the original dataset's dimensionality.
Clustering of the population benefiting from health insurance using K-means

Clustering techniques aim to discover groupings of a set of patterns or data and are widely used in any discipline that involves analysis of multivariate data. Their applications in different fields are multiple and diverse. They can also be used in health insurance, which is an important part of healthcare. The use of clustering techniques in this field will provide opportunities and solutions for decision makers in order to monitor the insurance coverage in general and health insurance in particular. The aim of this paper is to create clusters of the insured population by means of an unsupervised machine learning algorithm, which is a partitional clustering technique that has proved to be efficient and fast, which is K-means. We present and discuss the results obtained by using this clustering method.
The Effect of Bloom’s Taxonomy on Random Forest Classifier for cognitive level identification of E-content

With the advancement in internet, the efficiency of e-learning increased and currently e-learning is one of the primary method of learning for most learners after the regular academics studies. The knowledge delivery through e-learning web sites increased exponentially over the years because of the advancement in internet and e-learning technologies. The learner can find many website with lots of information on the relevant domain. However learners often found it difficult to Figure out the right leaning content from the humongous availability of e-content. In the proposed work an intelligent framework is developed to address this issue. The framework recommend the right learning content to a user from the e-learning web sites with the knowledge level of the user. The e-contents available in web sites were divided in to three cognitive levels such as beginner, intermediate and advanced level. The current work uses Blooms Taxonomy verbs and its synonyms to improve the accuracy of the classifier used in the framework.
A Robustness and Low Bit-Rate Image Compression Network for Underwater Acoustic Communication

Image compression algorithm is an important technology in the process of image transmission. Algorithm faces more difficult challenges in underwater acoustic communication. Images are required to be transmitted at a low bit-rate due to the limited underwater bandwidth and the noisy underwater acoustic environment will cause errors like random bit flip or packet loss. Therefore, the performance of common compression algorithms (JPEG, BPG, etc.) will be greatly reduced. Based on deep neural network (DNN), we propose an image compression algorithm that compresses the image texture and color separately for reducing the bit-rate. Moreover, we simulate the underwater acoustic environment and add different types of errors to compressed bit codes in our training process. Extensive experiments show that this training method improves the robustness of decoder and reconstruction performance. Besides, the algorithm is better than common compression algorithms and DNN based algorithms for underwater acoustic communication.
Extraction of Creation-Time for Recovered Files on Windows FAT32 File System

In this article, we propose a creation order reconstruction method of deleted files for the FAT32 file system with Windows operating systems. Creation order of files is established using a correlation between storage locations of the files and their directory entry locations. This method can be utilized to derive the creation-time bound of files recovered without the creation-time information. In this article, we first examine the file allocation behavior of Windows FAT32 file system. Next, based on the examined behavior, we propose a novel method that finds the creation order of deleted files after being recovered without the creation-time information. Due to complex behaviors of Windows FAT32 file system, the method may find multiple creation orders although the actual creation order is unique. In experiments with a commercial device, we confirm that the actual creation order of each recovered file belongs to one of the creation orders found by the method.
Knowledge-Driven Intelligent Survey Systems Towards Open Science

Abstract In this paper, we propose Knowledge Graph (KG), an articulated underlying semantic structure, as a semantic bridge between humans, systems, and scientific knowledge. To illustrate our proposal, we focus on KG-based intelligent survey systems. In state-of-the-art systems, information is hard-coded or implicit, making it hard for researchers to reuse, customise, link, or transmit structured knowledge. Furthermore, such systems do not facilitate dynamic interaction based on semantic structure. We design and implement a knowledge-driven intelligent survey system which is based on knowledge graph, a widely used technology that facilitates sharing and querying hypotheses, survey content, results, and analyses. The approach is developed, implemented, and tested in the field of Linguistics. Syntacticians and morphologists develop theories of grammar of natural languages. To evaluate theories, they seek intuitive grammaticality (well-formedness) judgments from native speakers, which either support hypotheses or provide counter-evidence. Our preliminary experiments show that a knowledge graph-based linguistic survey can provide more nuanced results than the traditional document-based grammaticality judgment surveys by allowing for tagging and manipulation of specific linguistic variables.
Backpropagation neural network prediction for cryptocurrency bitcoin prices

Abstract The value of bitcoin currency is very volatile, hard to guess for every hour, so many of the bitcoin traders suffer losses because they are wrong in managing their bitcoin assets. Changes in the price of bitcoin itself are influenced by many things such as the closing of the bitcoin market in a country, the occurrence of hacker attacks on the bitcoin blockchain and the emergence of new coins that use technology similar to bitcoin. But when a stable market situation changes the price of bitcoin is purely influenced by market forces. By implementing an artificial neural network using backpropagation method, it will be able to predict the price of bitcoin by giving a form of predictive results that are strengthened with a fairly good value of accuracy. This research begins by determining prediction variables with target values that can be determined based on previous bitcoin prices. This artificial neural network process is able to conduct training and testing of data based on network patterns that have been formed, then the results of training and testing of the network will be analysed again, so that at the last stage the best network patterns will be used in the prediction process.
Detection and Mitigation of Cybersickness via EEG-Based Visual Comfort Improvement

Cybersickness is a notoriously common problem experienced with virtual reality (VR) systems. While the symptoms produced in the user of these systems are similar to the symptoms of motion sickness, the reaction to cybersickness is largely unrelated to physical movement, since physical movement, if any, is realized by the user in VR. Detection of cybersickness in a VR environment is a substantial step towards providing a comfortable VR experience. Because cybersickness has many symptoms, it has proved to be difficult to develop a precise method to detect it. Visual discomfort is believed to be the foremost contributing factor to cybersickness. Detection of visual discomfort is currently performed by questionnaires. However, this approach is not reliable due to the subjective nature of the collected feedback. With the availability of mobile EEG devices which are wearable, portable and low cost compared to medical level EEG devices, it is possible to use them with head mounted VR displays without interference. In this study, we first review the methodology used in EEG-based cybersickness detection and prevention. Then, we present our precursor study for EEG-based visual comfort improvement in VR and demonstrate the preliminary results of an EEG-based analysis using a mobile EEG device supported with the responses to the user questionnaire.
Cylindrical Shape Decomposition for 3D Segmentation of Tubular Objects

We develop a cylindrical shape decomposition (CSD) algorithm to decompose an object, a union of several tubular structures, into its semantic components.We decompose the object using its curve skeleton and restricted translational sweeps.For that, CSD partitions the curve skeleton into maximallength sub-skeletons over an orientation cost, each sub-skeleton corresponds to a semantic component.To find the intersection of the tubular components, CSD translationally sweeps the object in decomposition intervals to identify critical points at which the shape of the object changes substantially.CSD cuts the object at critical points and assigns the same label to parts along the same sub-skeleton, thereby constructing a semantic component.The proposed method further reconstructs the acquired semantic components at the intersection of object parts using generalized cylinders.We apply CSD for segmenting axons in large 3D electron microscopy images and decomposing vascular networks and synthetic objects.We show that our proposal is robust to severe surface noise and outperforms state-of-the-art decomposition techniques in its applications.
Style Transfer for Detecting Vehicles with Thermal Camera

In this work we focus on nighttime vehicle detection for intelligent traffic monitoring from the thermal camera. To train a Convolutional Neural Network (CNN) detector we create a stylized version of COCO (Common Objects in Context) dataset using Style Transfer technique that imitates images obtained from thermal cameras. This new dataset is further used for fine-tuning of the model and as a result detection accuracy on images from thermal cameras has significantly improved. As a side effect, we noticed that Style Transfer can be also used to improve detection accuracy from standard RGB camera, which has potential for various applications.
A Systematic Literature Review on DevOps Capabilities and Areas

Businesses today need to respond to customer needs at an unprecedented speed. Driven by this need for speed, many companies are rushing to the DevOps movement. DevOps, the combination of Development and Operations, is a new way of thinking in the software engineering domain that recently received much attention. Since DevOps has recently been introduced as a new term and novel concept, no common understanding of what it means has yet been achieved. Therefore, the definitions of DevOps often are only partly relevant to the concept. This research presents a systematic literature review to identify the determining factors contributing to the implementation of DevOps, including the main capabilities and areas with which it evolves.
Quantum teleportation mediated by surface plasmon polariton

Surface plasmon polaritons (SPPs) are collective excitations of free electrons propagating along a metal-dielectric interface. Although some basic quantum properties of SPPs, such as the preservation of entanglement, the wave-particle duality of a single plasmon, the quantum interference of two plasmons, and the verification of entanglement generation, have been shown, more advanced quantum information protocols have yet to be demonstrated with SPPs. Here, we experimentally realize quantum state teleportation between single photons and SPPs. To achieve this, we use polarization-entangled photon pairs, coherent photon-plasmon-photon conversion on a metallic subwavelength hole array, complete Bell-state measurements and an active feed-forward technique. The results of both quantum state and quantum process tomography confirm the quantum nature of the SPP mediated teleportation. An average state fidelity of 0.889$\pm$0.004 and a process fidelity of 0.820$\pm$0.005, which are well above the classical limit, are achieved. Our work shows that SPPs may be useful for realizing complex quantum protocols in a photonic-plasmonic hybrid quantum network.
Operating models of application protocols

These last years the internet of things (IoT) is a buzzword in tech circles. It covers a very wide range of technologies, from embedded real-time to storage in the cloud. It's about dialogue and automating the behavior of everyday objects to provide services or send information about their state or environment to extract value. One way to interact with these connected objects is to use the application protocols. In this paper, we will present the operating models of the application protocols which are specifically designed for equipment with limited memory capacity, in computing power and in energy with a small distance. These protocols are: CoAP, MQTT, XMPP and AMQP. For these models, we used the activity diagram which is a behavioral diagram of UML, it allows to graphically represent the behavior of a method or the flow of a use case. We will adopt these models later the test of the operation of these protocols and the realization of the models of good practice of these protocols.
Semi-black-box Attacks Against Speech Recognition Systems Using Adversarial Samples

As automatic speech recognition (ASR) systems have been integrated into a diverse set of devices around us in recent years, security vulnerabilities of them have become an increasing concern for the public. Existing studies have demonstrated that deep neural networks (DNNs), acting as the computation core of ASR systems, is vulnerable to deliberately designed adversarial attacks. Based on the gradient descent algorithm, existing studies have successfully generated adversarial samples which can disturb ASR systems and produce adversary-expected transcript texts designed by adversaries. Most of these research simulated white-box attacks which require knowledge of all the components in the targeted ASR systems. In this work, we propose the first semi-black-box attack against the ASR system - Kaldi. Requiring only partial information from Kaldi and none from DNN, we can embed malicious commands into a single audio chip based on the gradient-independent genetic algorithm. The crafted audio clip could be recognized as the embedded malicious commands by Kaldi and unnoticeable to humans in the meanwhile. Experiments show that our attack can achieve high attack success rate with unnoticeable perturbations to three types of audio clips (pop music, pure music, and human command) without the need of the underlying DNN model parameters and architecture.
Performance analysis of physical-layer security in ISK quantum-noise randomized cipher based on wiretap channel

Based on the conventional analytical method on wiretap channel model, the physical-layer security of intensity shift keying (ISK) quantum-noise randomized cipher (QNRC) systems is analyzed quantitatively. The wiretap channel models for the key and the data in ISK-QNRC systems are established respectively. The secrecy rates are employed to evaluate the physical-layer security level of both the key and data. Further, the maximal achievable secrecy rate of ISK-QNRC system is proposed, which guarantees the secrecy of the key and data. In order to further optimize the security of ISK-QNRC systems, the impacts of various system parameters and cases on secrecy rates are assessed and compared in detail. The simulation results indicate ISK-QNRC system combined with proper channel codes can realized high-speed and long-haul anti-interception communications. The maximal achievable secrecy rate of ISK-QNRC system is constrained mainly by the security of the running key in general cases. It is helpful to choose the proper power of mesoscopic quantum state, gain of optical amplifier at the transmitter and number of bases for improving the security performance of ISK-QNRC system.
Managing conflict originated by feminism

Abstract The aim of this study is to analyze how conflict begins, unfolds and ends in a WhatsApp interaction within a Spanish family on 2018’s International Women’s Day. The analytical framework proposed by Bou-Franch and Garcés-Conejos Blitvich (2014) is applied to a conversation that began at 10:37 am on March 8 and ended at 1:47 am on March 9. Multimodal analysis is used to consider not only participants’ linguistic strategies for expressing their opinions about feminism, but also the function of multimedia elements and emojis. The results show that conflict is constructed across multiple turns when some participants’ pro-feminist views are seen negatively by others. Thus, feminism emerges as a source of conflict among the family members. Another important issue drawn from the findings is the key role played by multimodal elements which have different functions related to how conflict is created and unfolds, such as being the origin of conflict itself, showing disapproval with others’ opinions, intensifying opinions to show emotion or trying to reduce face-threat.
Bitcoin Spot and Futures Market Microstructure

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Homogeneous Vs. Heterogeneous Distributed Data Clustering: A Taxonomy

Recent advances in computer architecture and networking allow for the opportunity to parallelize the data clustering process. By dividing the problem into smaller partitions, tackling each one in parallel, and then combining the partial solutions, the parallel algorithms can cluster large amounts of data much more efficiently. In specific scenarios, the dataset is inherently distributed over multiple nodes, making it impossible and even infeasible to apply centralized clustering, which has created a need for performing clustering in distributed environments. Distributed clustering solves two problems: infeasibility of collecting data at a central node, due to either technical and/or privacy limitations, and intractability of traditional clustering algorithms on large datasets. In this paper, we provide a novel taxonomy of distributed data clustering algorithms and provide insight into their distributed modeling strategies. The taxonomy classifies the distributed clustering processes as either a homogeneous or heterogeneous process. Various distributed performance and quality measures are also addressed.
SRRL: Select Reliable Friends for Social Recommendation with Reinforcement Learning

Along with the boom of social media, researchers consider that incorporating social relationships into traditional recommender systems can help alleviate the problems of data sparsity and cold start. However, recent reports show that social recommendation can hardly get the expected effect. The unsatisfying result can be attributed to the fact that users may share different preferences with their friends because of the randomness in the process of building social networks. In consequence, direct use of social relationships may lead to the degradation of recommendation performance, which makes identifying reliable friends for each user critical. In this paper, we propose an end-to-end social recommendation framework based on reinforcement learning to identify reliable social relationships for users. Specifically, our model consists of two parts: (a) an agent which samples users’ reliable social relationships and delivers them to the environment. (b) an environment which takes charge of generating recommendations with sampled social relations and returning rewards to the agent to optimize the sampling procedure. With the interactions between the agent and the environment, our model can dynamically identify reliable friends whose preferences are really similar to the current user. Experimental analysis on two real-world datasets demonstrates that our approach outperforms the state-of-the-art social recommendation algorithms.