Reviewerly/exHarmony huggingface dataset:
Student’s Response Toward Utilizing Discord Application as an Online Learning Media in Learning Speaking at Senior High School

Technology and education are different things, but in this modern era we all can see that these two things are inseparable and become necessities. This present study is aimed to know the students’ response toward utilizing Discord application as an online learning media in learning speaking at Senior High School. Therefore, This study investigates the effectiveness of the Discord application as an online learning medium, and also investigates the students' motivation to learn speaking skills while learning using the Discord application. This study uses a qualitative approach and uses an interpretative studies research design. Participants in this study involved 6 students' in 12th grade in Senior High School SMA Negeri 1 Cikarang Timur. Online observations, online interviews, and virtual documentation are used to obtain data as information or answers related to the research question. This research found that , the utilizing of the Discord application as an online learning media in speaking learning has several benefits and advantages. The discord application facilitates the convenience of students in the learning experience such as a very effective, practical, lighter application used in various smart phones because of its small size, has a Voice channel feature that allows interaction via voice in the discord room.Keywords : Discord Application, Student’s Response, Speaking.
Protecting DNNs from Theft using an Ensemble of Diverse Models

Several recent works have demonstrated highly effective model stealing (MS) attacks on Deep Neural Networks (DNNs) in black-box settings, even when the training data is unavailable. These attacks typically use some form of Out of Distribution (OOD) data to query the target model and use the predictions obtained to train a clone model. Such a clone model learns to approximate the decision boundary of the target model, achieving high accuracy on in-distribution examples. We propose Ensemble of Diverse Models (EDM) to defend against such MS attacks. EDM is made up of models that are trained to produce dissimilar predictions for OOD inputs. By using a different member of the ensemble to service different queries, our defense produces predictions that are highly discontinuous in the input space for the adversary's OOD queries. Such discontinuities cause the clone model trained on these predictions to have poor generalization on in-distribution examples. Our evaluations on several image classification tasks demonstrate that EDM defense can severely degrade the accuracy of clone models (up to 39.7%). Our defense has minimal impact on the target accuracy, negligible computational costs during inference, and is compatible with existing defenses for MS attacks.
Maturity Framework Analysis ISO 27001: 2013 on Indonesian Higher Education

Information Security Management System (ISMS) implementation in Institution is an effort to minimize information security risks and threats such as information leakage, application damage, data loss and declining IT network performance. The several incidents related to information security have occurred in the implementation of the Academic System application in Indonesian higher education. This research was conducted to determine the maturity level of information security practices in Academic Information Systems at universities in Indonesia. The number of universities used as research samples were 35 institutions. Compliance with the application of ISO 27001:2013 standard is used as a reference to determine the maturity level of information system security practices. Meanwhile, to measure and calculate the level of maturity using the SSE-CMM model. In this research, the Information System Security Index obtained from the analysis results can be used as a tool to measure the maturity of information security that has been applied. There are six key areas examined in this study, namely the role and importance of ICT, information security governance, information security risk management, information security management framework, information asset management, and information security technology. The results showed the level of information security maturity at 35 universities was at level 2 Managed Process and level 3 Established Process. The composition is that 40% of universities are at level 3, and 60% are out of level 3. The value of the gap between the value of the current maturity level and the expected level of maturity is varied for each clause (domain). The smallest gap (1 level) is in clause A5: Information Security Policy, clause A9: Access Control, and clause A11: Physical and environmental security. The biggest gap (4 levels) is in clause A14: System acquisition, development and maintenance and clause A18: compliance.Â Â Â
Managing Network Slicing Resources Using Blockchain in a Multi-Domain Software Defined Optical Network Scenario

This paper presents an experimental validation to use a peer-to-peer network composed of network slicing managers to manage and share optical network resources using Blockchain in a network scenario with multiple domains and without the need of a central authority that validates any information exchange.
Certified robustness against physically-realizable patch attack via randomized cropping

This paper studies a certifiable defense against adversarial patch attacks on image classification. Our approach classifies random crops from the original image independently and the original image is classified as the vote over these crops. This process minimizes changes to the training process, as only the crop classification model needs to be trained, and can be trained in a standard manner without explicit adversarial training. Leveraging the fact that a patch attack can only influence some pixels of the image, we derive certified robustness bounds on the resulting classification. Our method is particularly effective when realistic physical transformations are applied to the adversarial patch, such as affine transformations. Such transformations occur naturally when an adversarial patch is physically introduced to a scene. Our method improves upon the current state of the art in defending against patch attacks on CIFAR10 and ImageNet, both in terms of certified accuracy and inference time.
Advanced Data Mining of SSD Quality Based on FP-Growth Data Analysis

Storage devices in the computer industry have gradually transformed from the hard disk drive (HDD) to the solid-state drive (SSD), of which the key component is error correction in not-and (NAND) flash memory. While NAND flash memory is under development, it is still limited by the “program and erase” cycle (PE cycle). Therefore, the improvement of quality and the formulation of customer service strategy are topics worthy of discussion at this stage. This study is based on computer company A as the research object and collects more than 8000 items of SSD error data of its customers, which are then calculated with data mining and frequent pattern growth (FP-Growth) of the association rule algorithm to identify the association rule of errors by setting the minimum support degree of 90 and the minimum trust degree of 10 as the threshold. According to the rules, three improvement strategies of production control are suggested: (1) use of the association rule to speed up the judgment of the SSD error condition by customer service personnel, (2) a quality strategy, and (3) a customer service strategy.
Real-Time Human Violent Activity Recognition Using Complex Action Decomposition

There is a scarcity of intelligent security systems in previous researches focusing on violence detection for public spaces. Previous efforts focus only on system accuracy but not their speed or operability. This defeats the purpose of the safety-critical aspect of security systems. With this, we developed a violence recognition framework for surveillance systems using a two-stream neural network architecture for complex action decomposition. As there is no publicly available dataset for static surveillance cameras for violence recognition considering them as complex actions, the proponents organized a multi-view dataset for determining simple violent actions. The main pipeline of the system utilizes state-of-the-art architectures on object detection for extracting spatial features and optical flow estimation for temporal features that are then fused using a boost fusion algorithm to determine simple actions. The simple actions are then further classified as either violent or non-violent actions by logistic regression which completes the complex action recognition. The experimental results show that our proposed framework has an accuracy of 81.2% and a recall of 81.7%. Previous implementations that included their operating speed reaches 25 FPS on shallow neural networks while this research's operating speed reaches 21 FPS on a very deep network. This shows that the performance of the research has competent accuracy and can operate in real-time safety-critical operations.
On the general conditions of existence for linear MMSE filters: Wiener and Kalman

State estimation is a fundamental task in a plethora of applications. Two important classes of linear minimum mean square error (MMSE) estimators are Wiener filters (WFs) and Kalman filters (KFs), the latter being the recursive form of the former for linear discrete state-space (LDSS) dynamic models. The minimum set of uncorrelation conditions regarding states and measurements which allows to formulate a general KF form has been recently introduced in the literature, but for both WFs and KFs a standard key assumption is that the measurement noise covariance is invertible. In this contribution we provide i) an alternative derivation of the WF in the case of a non-invertible measurement covariance matrix, and ii) the proof that under the minimum set of uncorrelation conditions lately released, the KF can always be formulated irrespective of the measurement covariance matrix rank. Notice that these results also apply for Kalman predictors and smoothers, linearly constrained WFs and minimum variance distortionless response (MVDR) filters, therefore being a remarkable result of broad interest.
Serverless architecture for edge computing

In this chapter, present a review over two emerging technologies, namely edge computing and serverless computing. Both technologies have great potential to be extended and adopted in next few years for development of smart applications. In addition, there are many cloud providers working toward enabling users to get benefit from these services. We strongly believe that combining these two technologies provides a number of benefits for developers to be able to use the edge -cloud computing systems with less complexity and effort. This paper presented a conceptual framework along with a case study to show how the serverless edge computing architecture can help to develop smart applications. There are several technical challenges that should be investigated in this area, which we have presented as the open questions for future work.
Analysis and Evaluation of Keystroke Dynamics as a Feature of Contextual Authentication

The following topics are dealt with: authorisation; data privacy; mobile computing; security of data; cryptography; Internet of Things; message authentication; invasive software; Android (operating system); vectors.
Emotional dialog generation via multiple classifiers based on a generative adversarial network

Human-machine dialog generation is an essential topic of research in the field of natural language processing. Generating high-quality, diverse, fluent, and emotional conversation is a challenging task. Based on continuing advancements in artificial intelligence and deep learning, new methods have come to the forefront in recent times. In particular, the end-to-end neural network model provides an extensible conversation generation framework that has the potential to enable machines to understand semantics and automatically generate responses. However, neural network models come with their own set of questions and challenges. The basic conversational model framework tends to produce universal, meaningless, and relatively "safe" answers. Based on generative adversarial networks (GANs), a new emotional dialog generation framework called EMC-GAN is proposed in this study to address the task of emotional dialog generation. The proposed model comprises a generative and three discriminative models. The generator is based on the basic sequence-to-sequence (Seq2Seq) dialog generation model, and the aggregate discriminative model for the overall framework consists of a basic discriminative model, an emotion discriminative model, and a fluency discriminative model. The basic discriminative model distinguishes generated fake sentences from real sentences in the training corpus. The emotion discriminative model evaluates whether the emotion conveyed via the generated dialog agrees with a pre-specified emotion, and directs the generative model to generate dialogs that correspond to the category of the pre-specified emotion. Finally, the fluency discriminative model assigns a score to the fluency of the generated dialog and guides the generator to produce more fluent sentences. Based on the experimental results, this study confirms the superiority of the proposed model over similar existing models with respect to emotional accuracy, fluency, and consistency. The proposed EMC-GAN model is capable of generating consistent, smooth, and fluent dialog that conveys pre-specified emotions, and exhibits better performance with respect to emotional accuracy, consistency, and fluency compared to its competitors.
Towards Automatic Evaluation of Dialog Systems: A Model-Free Off-Policy Evaluation Approach

Reliable automatic evaluation of dialogue systems under an interactive environment has long been overdue.An ideal environment for evaluating dialog systems, also known as the Turing test, needs to involve human interaction, which is usually not affordable for large scale experiments.Though researchers have attempted to use metrics for language generation tasks (e.g., perplexity, BLEU) or some model-based reinforcement learning methods (e.g., self-play evaluation) for automatic evaluation, these methods only show very weak correlation with the actual human evaluation in practice.To bridge such a gap, we propose a new framework named ENIGMA for estimating human evaluation scores based on recent advances of off-policy evaluation in reinforcement learning.ENIGMA only requires a handful of pre-collected experience data, and therefore does not involve human interaction with the target policy during the evaluation, making automatic evaluations feasible.More importantly, ENIGMA is model-free and agnostic to the behavior policies for collecting the experience data (see details in Section 2), which significantly alleviates the technical difficulties of modeling complex dialogue environments and human behaviors.Our experiments show that ENIGMA significantly outperforms existing methods in terms of correlation with human evaluation scores.
Certified Robustness to Programmable Transformations in LSTMs

Deep neural networks for natural language processing are fragile in the face of adversarial examples-small input perturbations, like synonym substitution or word duplication, which cause a neural network to change its prediction.We present an approach to certifying the robustness of LSTMs (and extensions of LSTMs) and training models that can be efficiently certified.Our approach can certify robustness to intractably large perturbation spaces defined programmatically in a language of string transformations.Our evaluation shows that (1) our approach can train models that are more robust to combinations of string transformations than those produced using existing techniques; (2) our approach can show high certification accuracy of the resulting models.
Mutual stain conversion between Giemsa and Papanicolaou in cytological images using cycle generative adversarial network

Papanicolaou and Giemsa stains used in cytology have different characteristics and complementary roles. In this study, we focused on cycle-consistent generative adversarial network (CycleGAN), which is an image translation technique using deep learning, and we conducted mutual stain conversion between Giemsa and Papanicolaou in cytological images using CycleGAN.A total of 191 Giemsa-stained images and 209 Papanicolaou-stained images were collected from 63 patients with lung cancer. From those images, 67 images from nine cases were used for testing and the remaining images were used for training. For data augmentation, the number of training images was increased by rotation and inversion, and the images were pipelined to CycleGAN to train the mutual conversion process involving Giemsa- and Papanicolaou-stained images. Three pathologists and three cytotechnologists performed visual evaluations of the authenticity of cell nuclei, cytoplasm, and cell layouts of the test images translated using CycleGAN.As a result of converting Giemsa-stained images into Papanicolaou-stained images, the background red blood cell patterns present in Giemsa-stained images disappeared, and cell patterns that reproduced the shape and staining of the cell nuclei and cytoplasm peculiar to Papanicolaou staining were obtained. Regarding the reverse-translated results, nuclei became larger, and red blood cells that were not evident in Papanicolaou-stained images appeared. After visual evaluation, although actual images exhibited better results than converted images, the results were promising for various applications.The stain translation technique investigated in this paper can complement specimens under conditions where only single stained specimens are available; it also has potential applications in the massive training of artificial intelligence systems for cell classification, and can also be used for training cytotechnologist and pathologists.
Dynamic Gesture Recognition System with Gesture Spotting Based on Self-Organizing Maps

In this paper, a real-time dynamic hand gesture recognition system with gesture spotting function is proposed. In the proposed system, input video frames are converted to feature vectors, and they are used to form a posture sequence vector that represents the input gesture. Then, gesture identification and gesture spotting are carried out in the self-organizing map (SOM)-Hebb classifier. The gesture spotting function detects the end of the gesture by using the vector distance between the posture sequence vector and the winner neuron’s weight vector. The proposed gesture recognition method was tested by simulation and real-time gesture recognition experiment. Results revealed that the system could recognize nine types of gesture with an accuracy of 96.6%, and it successfully outputted the recognition result at the end of gesture using the spotting result.
Normalized Convolution Upsampling for Refined Optical Flow Estimation

Optical flow is a regression task where convolutional neural networks (CNNs) have led to major breakthroughs. However, this comes at major computational demands due to the use of cost-volumes and pyramidal representations. This was mitigated by producing flow predictions at quarter the resolution, which are upsampled using bilinear interpolation during test time. Consequently, fine details are usually lost and post-processing is needed to restore them. We propose the Normalized Convolution UPsampler (NCUP), an efficient joint upsampling approach to produce the full-resolution flow during the training of optical flow CNNs. Our proposed approach formulates the upsampling task as a sparse problem and employs the normalized convolutional neural networks to solve it. We evaluate our upsampler against existing joint upsampling approaches when trained end-to-end with a a coarse-to-fine optical flow CNN (PWCNet) and we show that it outperforms all other approaches on the FlyingChairs dataset while having at least one order fewer parameters. Moreover, we test our upsampler with a recurrent optical flow CNN (RAFT) and we achieve state-of-the-art results on Sintel benchmark with ~6% error reduction, and on-par on the KITTI dataset, while having 7.5% fewer parameters (see Figure 1). Finally, our upsampler shows better generalization capabilities than RAFT when trained and evaluated on different datasets.
Platfospheres and sociocultural explosion of Web 2.0: The commercial centre of the digital semiosphere

This paper explores digital culture with the tools of cultural semiotics in general, and then employing the semiosphere model in particular. Web 2.0 platforms are taken as the major cultural dispositive of our time, as the most representative way in which the internet shapes digital culture. Most of the global population is currently immersed in digital culture. In the first part of the paper the striking similarities between Web 2.0 platforms and the semiosphere are explored and equivalences between the elements of the classic (Lotman’s) semiotic model and these platforms, or platfospheres, are identified. The second part explores the fundamental difference between the “genetic code” at the centre of the semiosphere (as conceived by Lotman), and the computer code and commercial algorithms at the core of the platfospheres that are responsible for their cultural operation. Then the parallels are examined that arise between the past cultural reality, in which the intellectual elite and academics were the driving force of culture, and the contemporary proactive (or even aggressive) core of the platfospheres, in which secret and patent-protected algorithms shape a cultural reality exclusively motivated by the logic of commercial success.
Participatory Methods in Food Behaviour Research: A Framework Showing Advantages and Disadvantages of Various Methods

A trend is visible in the food literature showing an increasing number of publications on studies that incorporate some form of participant engagement, such as citizen science and community-based participatory research. This “participation trend” will inevitably affect the scientific field of food behaviour research. This new trend is however not only associated with advantages, and a critical reflection on both the advantages and disadvantages is needed. The current article is a position paper that contributes to the literature in two main ways. First, participation is still in the developmental stage. Many different forms, methods and definitions are used. By providing a structured overview of a variety of participatory methods derived from a focused search of the literature on food behaviour, we aim to clarify the relationships between the various forms of participation methods. Second, the involvement of citizens in research is increasingly calling for novel research methods (e.g., voluntary recruitment and active involvement), which may be accompanied by both advantages and disadvantages. We add to the literature by developing a framework that indicates the advantages and disadvantages of participatory methods in food behaviour research. Our study highlights the relevance of differentiating the goal of the researcher (efficiency versus engagement) and the role of citizens (collecting versus creating), thus implying a trade-off between cost-effectiveness and involvement, as well as between data richness and data quality. Our work is a first effort to create structure and guidance within a new area. Our efforts could be used in future research aimed at developing more extensive protocols and tools for the application of participation in research, thereby offering a controlled manner to ensure that research stays abreast of our changing society.