Reviewerly/exHarmony huggingface dataset:
Meningkatkan Motivasi Belajar Peserta Didik Dengan Menggunakan Media Modul Elektronik Di Era Revolusi Industri 4.0

There are problems related to learning motivation that occurred in SMP Negeri 22 according to the observation conducted: passive learning environment and lack of students’ participation, readiness and engagement in learning that lead to the students’ poor academic achievement. In order to overcome the problems, the researchers in this study used electronic module due to its practicality, efficiency and interesting features that it has: audio, animation and video. Electronic module has a potential to bring abstract content into more concrete for the students, so that they might feel more motivated to learn. The purpose of this study was to determine the effect of Electronic Module Media on the stduents’ learning motivation in Class VIII E SMP Negeri 22 Jambi city. The research method applied is classroom action research. The results show that the use of electronic module media has increased the students’ motivation to learn, this can be seen from the results of the questionnaire as follows: 0% agree that the media is lacking, 16.7% agree that it is sufficiently good, 56.7% agree that it is good and 26.7% agree that it is categorized as very good and able to give a positive impact on the students’ learning outcomes on cognitive, affective and psychomotor aspect. This can be seen from the cognitive learning outcomes of the students who have completed learning that reached 86.67% with an average score of 75.3. The students successfully achieved good categories on affective and psychomotor aspects.&#x0D; Abstrak. Kulit singkong kurang dimanfaatkan oleh masyarakat sehingga menjadi limbah. Kulit singkong mengandung pati yang berpotensi sebagai edible coating. Tomat mudah mengalami kerusakan pasca panen sehingga kadar total fenol dan vitamin C menurun, maka perlu adanya alternatif untuk mempertahankan kadar total fenol dan vitamin C tomat dengan cara aplikasi edible coating pati kulit singkong. Tujuan dari penelitian ini adalah untuk mengetahui pengaruh adanya aplikasi edible coating pati kulit singkong konsentrasi 1%, 2% dan 4% terhadap kadar total fenol dan kadar vitamin C tomat hari ke 3, 6 dan 12 serta mengetahui potensi hasil penelitian sebagai sumber belajar IPA SMP kelas VIII materi zat aditif. Kadar total fenol diuji menggunakan metode spektrofotometri dan kadar vitamin C menggunakan metode titrasi iodium. Hasil penelitian menunjukan kadar total fenol dan kadar vitamin C tertinggi terjadi pada hari ke 12 perlakuan edible coating 4% berturut-turut sebesar 0.0525% dan 33.2210mg/100g, maka edible coating pati kulit singkong dapat mempertahankan kadar total fenol dan vitamin C tomat dibandingkan kontrol. Hasil penelitian dapat digunakan sebagai sumber belajar IPA SMP kelas VIII materi zat aditif.
Interactive Classification by Asking Informative Questions

We study the potential for interaction in natural language classification.We add a limited form of interaction for intent classification, where users provide an initial query using natural language, and the system asks for additional information using binary or multichoice questions.At each turn, our system decides between asking the most informative question or making the final classification prediction.The simplicity of the model allows for bootstrapping of the system without interaction data, instead relying on simple crowdsourcing tasks.We evaluate our approach on two domains, showing the benefit of interaction and the advantage of learning to balance between asking additional questions and making the final prediction.
Understanding Unintended Memorization in Federated Learning

Recent works have shown that generative sequence models (e.g., language models) have a tendency to memorize rare or unique sequences in the training data. Since useful models are often trained on sensitive data, to ensure the privacy of the training data it is critical to identify and mitigate such unintended memorization. Federated Learning (FL) has emerged as a novel framework for large-scale distributed learning tasks. However, it differs in many aspects from the well-studied central learning setting where all the data is stored at the central server. In this paper, we initiate a formal study to understand the effect of different components of canonical FL on unintended memorization in trained models, comparing with the central learning setting. Our results show that several differing components of FL play an important role in reducing unintended memorization. Specifically, we observe that the clustering of data according to users---which happens by design in FL---has a significant effect in reducing such memorization, and using the method of Federated Averaging for training causes a further reduction. We also show that training with a strong user-level differential privacy guarantee results in models that exhibit the least amount of unintended memorization.
“None of the Above”: Measure Uncertainty in Dialog Response Retrieval

This paper discusses the importance of uncovering uncertainty in end-to-end dialog tasks and presents our experimental results on uncertainty classification on the processed Ubuntu Dialog Corpus 1 .We show that instead of retraining models for this specific purpose, we can capture the original retrieval model's underlying confidence concerning the best prediction using trivial additional computation.
Metric Learning in Optimal Transport for Domain Adaptation

Domain Adaptation aims at benefiting from a labeled dataset drawn from a source distribution to learn a model from examples generated from a different but related target distribution. Creating a domain-invariant representation between the two source and target domains is the most widely technique used. A simple and robust way to perform this task consists in (i) representing the two domains by subspaces described by their respective eigenvectors and (ii) seeking a mapping function which aligns them. In this paper, we propose to use Optimal Transport (OT) and its associated Wassertein distance to perform this alignment. While the idea of using OT in domain adaptation is not new, the original contribution of this paper is two-fold: (i) we derive a generalization bound on the target error involving several Wassertein distances. This prompts us to optimize the ground metric of OT to reduce the target risk; (ii) from this theoretical analysis, we design an algorithm (MLOT) which optimizes a Mahalanobis distance leading to a transportation plan that adapts better. Extensive experiments demonstrate the effectiveness of this original approach.
Improving the security of “measurement-device-independent quantum communication without encryption”

Recently in 2018, Niu et al. proposed a measurement-device-independent quantum secure direct communication protocol using Einstein-Podolsky-Rosen pairs and generalized it to a quantum dialogue protocol (Niu et al., Science bulletin 63.20, 2018). By analyzing these protocols we find some security issues in both these protocols. In this work, we show that both the protocols are not secure against information leakage, and a third party can get half of the secret information without any active attack. We also propose suitable modifications of these protocols to improve the security.
VISUALISING SYSTEMS: MAPPING SYSTEM FEATURES AND INTERACTIVE INFORMATION VISUALISATIONS IN DESIGN

Abstract Interactive computer-supported information visualisations are being increasingly used in design. However, while there are frameworks that discuss how traditional representations, such as sketches, CAD models and static diagrams support design tasks, no such mapping exists for interactive visualisations of product-related information. As novel contributions, this paper reviews the design literature for the use of information visualisations. Moreover, using systems theory and Gestalt principles, insights on the applicability of such information visualisations for various design tasks are given.
Marthe: Scheduling the Learning Rate Via Online Hypergradients

We study the problem of fitting task-specific learning rate schedules from the perspective of hyperparameter optimization, aiming at good generalization. We describe the structure of the gradient of a validation error w.r.t. the learning rate schedule -- the hypergradient. Based on this, we introduce MARTHE, a novel online algorithm guided by cheap approximations of the hypergradient that uses past information from the optimization trajectory to simulate future behaviour. It interpolates between two recent techniques, RTHO (Franceschi et al., 2017) and HD (Baydin et al. 2018), and is able to produce learning rate schedules that are more stable leading to models that generalize better.
MULTIPOLAR: Multi-Source Policy Aggregation for Transfer Reinforcement Learning between Diverse Environmental Dynamics

Transfer reinforcement learning (RL) aims at improving the learning efficiency of an agent by exploiting knowledge from other source agents trained on relevant tasks. However, it remains challenging to transfer knowledge between different environmental dynamics without having access to the source environments. In this work, we explore a new challenge in transfer RL, where only a set of source policies collected under diverse unknown dynamics is available for learning a target task efficiently. To address this problem, the proposed approach, MULTI-source POLicy AggRegation (MULTIPOLAR), comprises two key techniques. We learn to aggregate the actions provided by the source policies adaptively to maximize the target task performance. Meanwhile, we learn an auxiliary network that predicts residuals around the aggregated actions, which ensures the target policy's expressiveness even when some of the source policies perform poorly. We demonstrated the effectiveness of MULTIPOLAR through an extensive experimental evaluation across six simulated environments ranging from classic control problems to challenging robotics simulations, under both continuous and discrete action spaces. The demo videos and code are available on the project webpage: https://omron-sinicx.github.io/multipolar/.
Adaptive model selection in photonic reservoir computing by reinforcement learning

Abstract Photonic reservoir computing is an emergent technology toward beyond-Neumann computing. Although photonic reservoir computing provides superior performance in environments whose characteristics are coincident with the training datasets for the reservoir, the performance is significantly degraded if these characteristics deviate from the original knowledge used in the training phase. Here, we propose a scheme of adaptive model selection in photonic reservoir computing using reinforcement learning. In this scheme, a temporal waveform is generated by different dynamic source models that change over time. The system autonomously identifies the best source model for the task of time series prediction using photonic reservoir computing and reinforcement learning. We prepare two types of output weights for the source models, and the system adaptively selected the correct model using reinforcement learning, where the prediction errors are associated with rewards. We succeed in adaptive model selection when the source signal is temporally mixed, having originally been generated by two different dynamic system models, as well as when the signal is a mixture from the same model but with different parameter values. This study paves the way for autonomous behavior in photonic artificial intelligence and could lead to new applications in load forecasting and multi-objective control, where frequent environment changes are expected.
Max-and-Smooth: A Two-Step Approach for Approximate Bayesian Inference in Latent Gaussian Models

With modern high-dimensional data, complex statistical models are necessary, requiring computationally feasible inference schemes. We introduce Max-and-Smooth, an approximate Bayesian inference scheme for a flexible class of latent Gaussian models (LGMs) where one or more of the likelihood parameters are modeled by latent additive Gaussian processes. Our proposed inference scheme is a two-step approach. In the first step (Max), the likelihood function is approximated by a Gaussian density with mean and covariance equal to either (a) the maximum likelihood estimate and the inverse observed information, respectively, or (b) the mean and covariance of the normalized likelihood function. In the second step (Smooth), the latent parameters and hyperparameters are inferred and smoothed with the approximated likelihood function. The proposed method ensures that the uncertainty from the first step is correctly propagated to the second step. Because the prior density for the latent parameters is assumed to be Gaussian and the approximated likelihood function is Gaussian, the approximate posterior density of the latent parameters (conditional on the hyperparameters) is also Gaussian, thus facilitating efficient posterior inference in high dimensions. Furthermore, the approximate marginal posterior distribution of the hyperparameters is tractable, and as a result, the hyperparameters can be sampled independently of the latent parameters. We show that the computational cost of Max-and-Smooth is close to being insensitive to the number of independent data replicates, and that it scales well with increased dimension of the latent parameter vector provided that its Gaussian prior density is specified with a sparse precision matrix. In the case of a large number of independent data replicates, sparse precision matrices, and high-dimensional latent vectors, the speedup is substantial in comparison to an MCMC scheme that infers the posterior density from the exact likelihood function. The accuracy of the Gaussian approximation to the likelihood function increases with the number of data replicates per latent model parameter. The proposed inference scheme is demonstrated on one spatially referenced real dataset and on simulated data mimicking spatial, temporal, and spatio-temporal inference problems. Our results show that Max-and-Smooth is accurate and fast.
ExplFrame: Exploiting Page Frame Cache for Fault Analysis of Block Ciphers

Page Frame Cache (PFC) is a purely software cache, present in modern Linux based operating systems (OS), which stores the page frames that were recently released by the processes running on a particular CPU. In this paper, we show that the page frame cache can be maliciously exploited by an adversary to steer the pages of a victim process to some pre-decided attacker-chosen locations in the memory. We practically demonstrate an end-to-end attack, ExplFrame, where an attacker having only user-level privilege is able to force a victim process's memory pages to vulnerable locations in DRAM and deterministically conduct Rowhammer to induce faults. As a case study, we induce single bit faults in the T-tables on OpenSSL (v1.1.1) AES using our proposed attack ExplFrame. We also propose an improvised fault analysis technique which can exploit any Rowhammer-induced bit-flips in the AES T-tables.
Evolution in Education: Chatbots

Artificial intelligence (AI) programs that simulate interactive human conversation, known as Chatbots, are one of the ongoing trends in the global market. Companies adopt Chatbots in order to offer better services to their customers. Businesses have realized that they are able to enhance the process of customer engagement and operational efficiency through Chatbot technology. Furthermore, most of us have experienced communication of this form in many aspects of our everyday life. This paper examines how Chatbots have evolved over the years, what the advantages and disadvantages of using them are and tries to explain the rise taking place nowadays. Subsequently, it explores the potential of applying this technology in educational settings. Personalized and adaptive learning seems to be imperative today and Chatbot technology can offer invaluable services towards that direction. Finally, it investigates the possibility of using them as virtual teaching assistants relieving teachers from the burden of repetitive tasks and helping them focus more on providing quality education to their students.
On Enabling GDPR Compliance in Business Processes Through Data-Driven Solutions

Abstract The collection and long-term retention of excessive data enables organisations to process data for insights in non-primary processes. The discovery of insights is promoted to be useful both for organisations and the customers. However, long-term possession of data on one hand risks the privacy of data belonging beings in cases of data breaches and on the other hand results in the customers distrust. General Data Protection Regulation (GDPR) abstractly defined the data processing boundaries of the personal data of European Union’s citizens. The processing principles of GDPR, in line with the spirit of privacy by design and default , provide directions on the collection, storage, and processing of personal data. Concomitantly, the data subject rights provide customers with necessary control over their personal data stationed at the data controller’s premises. The accountability principle of GDPR requires compliance in place and also the ability to demonstrate it. In this work, we are providing three solutions to enable GDPR compliance in business processes. First, we are proposing intra-process data degradation, a solution for continuous data minimisation during the course of business processes. The proposed approach results in reduced data maintenance and breach losses. Second, we adapt process mining techniques for ascertaining compliance of business process execution to data subject rights. Finally, we present a scheme to utilise differential privacy technique to enable GDPR-compliant business process discovery. Additionally, we offer links to two effective tools that demonstrate our first and second contributions.
Research on Load Clustering Based on Singular Value Decomposition and K-means Clustering Algorithm

Considering the shortcomings of existing clustering algorithms in clustering quality, this paper proposes a load clustering research based on singular value decomposition and K-means clustering algorithm. First, eight characteristic indexes of load are extracted, and the singular value is used to decompose the load characteristics of the user side. The solved singular value reflects the importance of this type of load characteristic. The load characteristics corresponding to the data with large singular value are taken as the main load characteristics to complete the dimensionality reduction of the data. Then, the clustering evaluation index SSE is used to compare the effects of direct clustering of load characteristics and clustering after singular value decomposition. The results show that the proposed method has better clustering effect on load characteristics. Finally, K-means clustering algorithm is used to cluster the load characteristics.
Discrete-phase-randomized measurement-device-independent quantum key distribution

Measurement-device-independent quantum key distribution removes all detector-side attacks in quantum cryptography, and in the meantime doubles the secure distance. The source side, however, is still vulnerable to various attacks. In particular, the continuous phase randomization assumption on the source side is normally not fulfilled in experimental implementation and may potentially open a loophole. In this work, we first show that indeed there are loopholes for imperfect phase randomization in measurement-device-independent quantum key distribution by providing a concrete attack. Then we propose a discrete-phase-randomized measurement-device-independent quantum key distribution protocol as a solution to close this source-side loophole.
Employing One-Class SVM Classifier Ensemble for Imbalanced Data Stream Classification

The classification of imbalanced data streams is gaining more and more interest. However, apart from the problem that one of the class is not well represented, there are problems typical for data stream classification, such as limited resources, lack of access to the true labels and the possibility of occurrence of the concept drift. Possibility of concept drift appearing enforces design in the method adaptation mechanism. In this article, we propose the OCEIS classifier (One-Class support vector machine classifier Ensemble for Imbalanced data Stream). The main idea is to supply the committee with one-class classifiers trained on clustered data for each class separately. The results obtained from experiments carried out on synthetic and real data show that the proposed method achieves results at a similar level as the state of the art methods compared with it.
Efficiently Processing Complex-Valued Data in Homomorphic Encryption

Abstract We introduce a new homomorphic encryption scheme that is natively capable of computing with complex numbers. This is done by generalizing recent work of Chen, Laine, Player and Xia, who modified the Fan–Vercauteren scheme by replacing the integral plaintext modulus t by a linear polynomial X − b . Our generalization studies plaintext moduli of the form X m + b . Our construction significantly reduces the noise growth in comparison to the original FV scheme, so much deeper arithmetic circuits can be homomorphically executed.
AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning

Outlier detection is an important data mining task with numerous practical applications such as intrusion detection, credit card fraud detection, and video surveillance. However, given a specific complicated task with big data, the process of building a powerful deep learning based system for outlier detection still highly relies on human expertise and laboring trials. Although Neural Architecture Search (NAS) has shown its promise in discovering effective deep architectures in various domains, such as image classification, object detection, and semantic segmentation, contemporary NAS methods are not suitable for outlier detection due to the lack of intrinsic search space, unstable search process, and low sample efficiency. To bridge the gap, in this paper, we propose AutoOD, an automated outlier detection framework, which aims to search for an optimal neural network model within a predefined search space. Specifically, we firstly design a curiosity-guided search strategy to overcome the curse of local optimality. A controller, which acts as a search agent, is encouraged to take actions to maximize the information gain about the controller's internal belief. We further introduce an experience replay mechanism based on self-imitation learning to improve the sample efficiency. Experimental results on various real-world benchmark datasets demonstrate that the deep model identified by AutoOD achieves the best performance, comparing with existing handcrafted models and traditional search methods.
Robust Variational Autoencoders for Outlier Detection and Repair of Mixed-Type Data.

We focus on the problem of unsupervised cell outlier detection and repair in mixed-type tabular data. Traditional methods are concerned only with detecting which rows in the dataset are outliers. However, identifying which cells are corrupted in a specific row is an important problem in practice, and the very first step towards repairing them. We introduce the Robust Variational Autoencoder (RVAE), a deep generative model that learns the joint distribution of the clean data while identifying the outlier cells, allowing their imputation (repair). RVAE explicitly learns the probability of each cell being an outlier, balancing different likelihood models in the row outlier score, making the method suitable for outlier detection in mixed-type datasets. We show experimentally that not only RVAE performs better than several state-of-the-art methods in cell outlier detection and repair for tabular data, but also that is robust against the initial hyper-parameter selection.