Reviewerly/exHarmony huggingface dataset:
A Reversible Data Hiding Method in Compressible Encrypted Images

We propose a reversible data hiding (RDH) method in compressible encrypted images called the encryption-then-compression (EtC) images. The proposed method allows us to not only embed a payload in encrypted images but also compress the encrypted images containing the payload. In addition, the proposed RDH method can be applied to both plain images and encrypted ones, and the payload can be extracted flexibly in the encrypted domain or from the decrypted images. Various RDH methods have been studied in the encrypted domain, but they are not considered to be two-domain data hiding, and the resultant images cannot be compressed by using image coding standards, such as JPEG-LS and JPEG 2000. In our experiment, the proposed method shows high performance in terms of lossless compression efficiency by using JPEG-LS and JPEG 2000, data hiding capacity, and marked image quality.
Adversarial Semantic Decoupling for Recognizing Open-Vocabulary Slots

Open-vocabulary slots, such as file name, album name, or schedule title, significantly degrade the performance of neural-based slot filling models since these slots can take on values from a virtually unlimited set and have no semantic restriction nor a length limit.In this paper, we propose a robust adversarial model-agnostic slot filling method that explicitly decouples local semantics inherent in open-vocabulary slot words from the global context.We aim to depart entangled contextual semantics and focus more on the holistic context at the level of the whole sentence.Experiments on two public datasets show that our method consistently outperforms other methods with a statistically significant margin on all the open-vocabulary slots without deteriorating the performance of normal slots.
Improving Bilingual Lexicon Induction for Low Frequency Words

This paper designs a Monolingual Lexicon Induction task and observes that two factors accompany the degraded accuracy of bilingual lexicon induction for rare words.First, a diminishing margin between similarities in low frequency regime, and secondly, exacerbated hubness at low frequency.Based on the observation, we further propose two methods to address these two factors, respectively.The larger issue is hubness.Addressing that improves induction accuracy significantly, especially for low-frequency words.
Semantic Query Integration With Reason

Graph-based data models allow for flexible data representation. In particular, semantic data based on RDF and OWL fuels use cases ranging from general knowledge graphs to domain specific knowledge in various technological or scientific domains. The flexibility of such approaches, however, makes programming with semantic data tedious and error-prone. In particular the logics-based data descriptions employed by OWL are problematic for existing error-detecting techniques, such as type systems. In this paper, we present DOTSpa, an advanced integration of semantic data into programming. We embed description logics, the logical foundations of OWL, into the type checking process of a statically typed programming language and provide typed data access through an embedding of the query language SPARQL. In addition, we demonstrate a concrete implementation of the approach, by extending the Scala programming language. We qualitatively compare programs using our approach to equivalent programs using a state-of-the-art library, in terms of how both frameworks aid users in the handling of typical failure scenarios.
Grounded Compositional Outputs for Adaptive Language Modeling

Language models have emerged as a central component across NLP, and a great deal of progress depends on the ability to cheaply adapt them (e.g., through finetuning) to new domains and tasks.A language model's vocabulary-typically selected before training and permanently fixed later-affects its size and is part of what makes it resistant to such adaptation.Prior work has used compositional input embeddings based on surface forms to ameliorate this issue.In this work, we go one step beyond and propose a fully compositional output embedding layer for language models, which is further grounded in information from a structured lexicon (WordNet), namely semantically related words and free-text definitions.To our knowledge, the result is the first word-level language model with a size that does not depend on the training vocabulary.We evaluate the model on conventional language modeling as well as challenging cross-domain settings with an open vocabulary, finding that it matches or outperforms previous state-of-theart output embedding methods and adaptation approaches.Our analysis attributes the improvements to sample efficiency: our model is more accurate for low-frequency words.
Does Deep Learning Work Well for Categorical Datasets with Mainly Nominal Attributes?

Given the complexity of real-world datasets, it is difficult to present data structures using existing deep learning (DL) models. Most research to date has concentrated on datasets with only one type of attribute: categorical or numerical. Categorical data are common in datasets such as the German (-categorical) credit scoring dataset, which contains numerical, ordinal, and nominal attributes. The heterogeneous structure of this dataset makes very high accuracy difficult to achieve. DL-based methods have achieved high accuracy (99.68%) for the Wisconsin Breast Cancer Dataset, whereas DL-inspired methods have achieved high accuracy (97.39%) for the Australian credit dataset. However, to our knowledge, no such method has been proposed to classify the German credit dataset. This study aimed to provide new insights into the reasons why DL-based and DL-inspired classifiers do not work well for categorical datasets, mainly consisting of nominal attributes. We also discuss the problems associated with using nominal attributes to design high-performance classifiers. Considering the expanded utility of DL, this study's findings should aid in the development of a new type of DL that can handle categorical datasets consisting of mainly nominal attributes, which are commonly used in risk evaluation, finance, banking, and marketing.
Quantum key distribution with simply characterized light sources

Abstract To guarantee the security of quantum key distribution (QKD), security proofs of QKD protocols have assumptions on the devices. Commonly used assumptions are, for example, each random bit information chosen by a sender to be precisely encoded on an optical emitted pulse and the photon-number probability distribution of the pulse to be exactly known. These typical assumptions imposed on light sources such as the above two are rather strong and would be hard to verify in practical QKD systems. The goal of the paper is to replace those strong assumptions on the light sources with weaker ones. In this paper, we adopt the differential-phase-shift (DPS) QKD protocol and drastically mitigate the requirements on light sources, while for the measurement unit, trusted and photon-number-resolving detectors are assumed. Specifically, we only assume the independence among emitted pulses, the independence of the vacuum emission probability from a chosen bit, and upper bounds on the tail distribution function of the total photon number in a single block of pulses for single, two and three photons. Remarkably, no other detailed characterizations, such as the amount of phase modulation, are required. Our security proof significantly relaxes demands for light sources, which paves a route to guarantee implementation security with simple verification of the devices.
INTERACTIVE MULTIMEDIA IN EFL CLASSROOM: A STUDY OF TEACHING READING COMPREHENSION AT JUNIOR HIGH SCHOOL IN INDONESIA

The objectives of the research were to find out: (1) whether or not the use of interactive multimedia improve students reading comprehension in the Eighth-Grade of SMP Negeri 4 Panca Rijang and (2) whether or not the use of interactive multimedia interest students’ in learning reading comprehension in the Eighth-Grade of SMP Negeri 4 Panca Rijang. This research employed mixed method design that applied experimental and control group. The population of the research was the Eighth-Grade students of SMP Negeri 4 Panca Rijang. The result of data analysis showed that there was significant difference between achievement of the students who used interactive multimedia and those who did not use interactive multimedia (conventional technique) in reading comprehension. It was proved by the mean score of experimental group was higher than control group in post-test (59.35 &gt; 46.75). Furthermore, the result of the t-test value (3.581) was greater than t-table (α = 0.05; df = 38; t-table = 2.021) which means that H1 was accepted. The data analysis of questionnaire showed that the students had high interest toward the use of interactive multimedia in reading comprehension. It was supported by 4 students (20%) who were strongly interested, 14 students (70%) who were interested and 2 students (10%) who were moderate. The mean score of students’ answers in questionnaire (79.55) was classified as interested category. Based on the data analysis, the researcher concluded that: (1) the application of interactive multimedia enhanced the students’ ability in reading comprehension; (2) the application of interactive multimedia strategy in reading comprehension was interesting for the students.
Detection and localization enhancement for satellite images with small forgeries using modified GAN-based CNN structure

The image forgery process can be simply defined as inserting some objects of different sizes to vanish some structures or scenes. Satellite images can be forged in many ways, such as copy-paste, copy-move, and splicing processes. Recent approaches present a generative adversarial network (GAN) as an effective method for identifying the presence of spliced forgeries and identifying their locations with a higher detection accuracy of large- and medium-sized forgeries. However, such recent approaches clearly show limited detection accuracy of small-sized forgeries. Accordingly, the localization step of such small-sized forgeries is negatively impacted. In this paper, two different approaches for detecting and localizing small-sized forgeries in satellite images are proposed. The first approach is inspired by a recently presented GAN-based approach and is modified to an enhanced version. The experimental results manifest that the detection accuracy of the first proposed approach noticeably increased to 86% compared to its inspiring one with 79% for the small-sized forgeries. Whereas, the second proposed approach uses a different design of a CNN-based discriminator to significantly enhance the detection accuracy to 94%, using the same dataset obtained from NASA and the US Geological Survey (USGS) for validation and testing. Furthermore, the results show a comparable detection accuracy in large- and medium-sized forgeries using the two proposed approaches compared to the competing ones. This study can be applied in the forensic field, with clear discrimination between the forged and pristine images.
Rethinking Supervised Learning and Reinforcement Learning in Task-Oriented Dialogue Systems

Dialogue policy learning for Task-oriented Dialogue Systems (TDSs) has enjoyed great progress recently mostly through employing Reinforcement Learning (RL) methods.However, these approaches have become very sophisticated.It is time to re-evaluate it.Are we really making progress developing dialogue agents only based on RL?We demonstrate how (1) traditional supervised learning together with (2) a simulator-free adversarial learning method can be used to achieve performance comparable to state-of-the-art (SOTA) RL-based methods.First, we introduce a simple dialogue action decoder to predict the appropriate actions.Then, the traditional multilabel classification solution for dialogue policy learning is extended by adding dense layers to improve the dialogue agent performance.Finally, we employ the Gumbel-Softmax estimator to alternatively train the dialogue agent and the dialogue reward model without using RL.Based on our extensive experimentation, we can conclude the proposed methods can achieve more stable and higher performance with fewer efforts, such as the domain knowledge required to design a user simulator and the intractable parameter tuning in reinforcement learning.Our main goal is not to beat RL with supervised learning, but to demonstrate the value of rethinking the role of RL and supervised learning in optimizing TDSs.
Leap Motion Sensor for Natural User Interface

In Human Computer Interaction (HCI) research area, there is an increasing tendency to make devices as simple and as natural as possible for use. These devices are aiming to make input and output techniques, interaction, etc., easier. In the input domain, sensors monitor and interpret head, eye, face and even whole body movements. Interacting with the computer, hands are the most effective tool of general purpose, due to their functionality in communication and manipulation. Using hands as an input device is an attractive method for ensuring interaction between man and computer. This paper gives an overview of the Leap Motion devices as a technology that enables natural interaction between man and computer in NUI (natural user interface) implementation. The main idea to maximize the naturalness of the user environment and the use of hand movements is in the application of the sensor fusion of two Leap Motion devices. Applying sensor fusion of two Leap Motion devices will increase the range of hand movement and the interaction within the environment, which will again contribute to the natural user interface (NUI). The suggested method can be used offline or in real time, and can benefit from a wide range of applications, where the gesture of the hand and fingers is the focus of significance and estimation effect.
Development of an AI Userbot for Engineering Design Education Using an Intent and Flow Combined Framework

As the inclusion of users in the design process receives greater attention, designers need to not only understand users, but also further cooperate with them. Therefore, engineering design education should also follow this trend, in order to enhance students’ ability to communicate and cooperate with users in the design practice. However, it is difficult to find users on teaching sites to cooperate with students because of time and budgetary constraints. With the development of artificial intelligence (AI) technology in recent years, chatbots may be the solution to finding specific users to participate in teaching. This study used Dialogflow and Google Assistant to build a system architecture, and applied methods of persona and semi-structured interviews to develop AI virtual product users. The system has a compound dialog mode (combining intent- and flow-based dialog modes), with which multiple chatbots can cooperate with students in the form of oral dialog. After four college students interacted with AI userbots, it was proven that this system can effectively participate in student design activities in the early stage of design. In the future, more AI userbots could be developed based on this system, according to different engineering design projects for engineering design teaching.
Pulsed Quantum Annealing

We propose a modified quantum annealing protocol, i.e., pulsed quantum annealing (PQA), in order to increase the success probability by a pulse application during the quantum annealing process.It is well known that the success probability of the conventional quantum annealing is reduced due to the Landau-Zener transitions.By applying a pulse to the system, we modulate the success probability and increase it, compared to the conventional quantum annealing, by optimizing the pulse parameters.We demonstrate our findings for a single qubit both numerically and analytically.The analytical model is based on the tranfer matrix approach and it is in good agreement with the full numerical results.We also investigate the PQA protocol for multi-qubit cases i.e., random spin-glass instances, and we present an overall increase of the success probability over the conventional quantum annealing, by optimizing the pulse parameters.Our results indicate that PQA can be used to design future high-performance quantum annealing machines, especially for hard instances that the conventional QA protocol behaves poorly.
On the robustness of the hybrid qubit computational gates through simulated randomized benchmarking protocols

One of the main challenges in building a quantum processor is to characterize the environmental noise. Noise characterization can be achieved by exploiting different techniques, such as randomization where several sequences of random quantum gates are applied to the qubit under test to derive statistical characteristics about the affecting noises. A scalable and robust algorithm able to benchmark the full set of Clifford gates using randomization techniques is called randomized benchmarking. In this study, we simulated randomized benchmarking protocols in a semiconducting all-electrical three-electron double-quantum dot qubit, i.e. hybrid qubit, under different error models, that include quasi-static Gaussian and the more realistic 1/f noise model, for the input controls. The average error of specific quantum computational gates is extracted through interleaved randomized benchmarking obtained including Clifford gates between the gate of interest. It provides an estimate of the fidelity as well as theoretical bounds for the average error of the gate under test.
Multi-pretraining for Large-scale Text Classification

Deep neural network-based pretraining methods have achieved impressive results in many natural language processing tasks including text classification.However, their applicability to large-scale text classification with numerous categories (e.g., several thousands) is yet to be well-studied, where the training data is insufficient and skewed in terms of categories.In addition, existing pretraining methods usually involve excessive computation and memory overheads.In this paper, we develop a novel multi-pretraining framework for largescale text classification.This multi-pretraining framework includes both a self-supervised pretraining and a weakly supervised pretraining.We newly introduce an out-of-context words detection task on the unlabeled data as the self-supervised pretraining.It captures the topic-consistency of words used in sentences, which is proven to be useful for text classification.In addition, we propose a weakly supervised pretraining, where labels for text classification are obtained automatically from an existing approach.Experimental results clearly show that both pretraining approaches are effective for large-scale text classification task.The proposed scheme exhibits significant improvements as much as 3.8% in terms of macro-averaging F 1 -score over strong pretraining methods, while being computationally efficient.
ArraMon: A Joint Navigation-Assembly Instruction Interpretation Task in Dynamic Environments

For embodied agents, navigation is an important ability but not an isolated goal.Agents are also expected to perform specific tasks after reaching the target location, such as picking up objects and assembling them into a particular arrangement.We combine Vision-and-Language Navigation, assembling of collected objects, and object referring expression comprehension, to create a novel joint navigationand-assembly task, named ARRAMON.During this task, the agent (similar to a PokéMON GO player) is asked to find and collect different target objects one-by-one by navigating based on natural language (English) instructions in a complex, realistic outdoor environment, but then also ARRAnge the collected objects part-by-part in an egocentric grid-layout environment.To support this task, we implement a 3D dynamic environment simulator and collect a dataset with human-written navigation and assembling instructions, and the corresponding ground truth trajectories.We also filter the collected instructions via a verification stage, leading to a total of 7.7K task instances (30.8K instructions and paths).We present results for several baseline models (integrated and biased) and metrics (nDTW, CTC, rPOD, and PTC), and the large model-human performance gap demonstrates that our task is challenging and presents a wide scope for future work. 1 1 Our dataset, simulator, and code are publicly available at: https://github.com/hyounghk/ArraMonNavigation Phase: Turn around to face the speed limit sign.Go to the sign and then turn right around the corner.Go to the booth and a little past it and to the right there is a brown hourglass.Pick it up.Assembly Phase: Place the hourglass to the right of the red mug in front of you.TURN 1 TURN 2 Navigation Phase: Turn left to face the dumpster.Go around the building corner, and past the phone booth to the next intersection.At the intersection turn left.Next to the yellow building there is a green bucket.Pick the bucket up.Assembly Phase: Turn right and place the bucket in front of the striped red mug.
Rank-One Network: An Effective Framework for Image Restoration

The principal rank-one (RO) components of an image represent the self-similarity of the image, which is an important property for image restoration. However, the RO components of a corrupted image could be decimated by the procedure of image denoising. We suggest that the RO property should be utilized and the decimation should be avoided in image restoration. To achieve this, we propose a new framework comprised of two modules, i.e., the RO decomposition and RO reconstruction. The RO decomposition is developed to decompose a corrupted image into the RO components and residual. This is achieved by successively applying RO projections to the image or its residuals to extract the RO components. The RO projections, based on neural networks, extract the closest RO component of an image. The RO reconstruction is aimed to reconstruct the important information, respectively from the RO components and residual, as well as to restore the image from this reconstructed information. Experimental results on four tasks, i.e., noise-free image super-resolution (SR), realistic image SR, gray-scale image denoising, and color image denoising, show that the method is effective and efficient for image restoration, and it delivers superior performance for realistic image SR and color image denoising. Our source code is available online.
Implementation of Interactive Learning Media on Chemical Materials

This paper aims to identify the implementations of interactive learning media on chemical materials based onf android, web and software to improve the learning media in the class. Learning media is one of the learning components that has an important role in Teaching and Learning Activities. The use of media should be a part that must get the attention of the teacher/facilitator in every learning activity. Therefore, the teacher/facilitator needs to learn how to determine learning media in order to make the achievement of learning objectives effective in the teaching and learning process.
Two-Party Privacy-Preserving Set Intersection with FHE

A two-party private set intersection allows two parties, the client and the server, to compute an intersection over their private sets, without revealing any information beyond the intersecting elements. We present a novel private set intersection protocol based on Shuhong Gao’s fully homomorphic encryption scheme and prove the security of the protocol in the semi-honest model. We also present a variant of the protocol which is a completely novel construction for computing the intersection based on Bloom filter and fully homomorphic encryption, and the protocol’s complexity is independent of the set size of the client. The security of the protocols relies on the learning with errors and ring learning with error problems. Furthermore, in the cloud with malicious adversaries, the computation of the private set intersection can be outsourced to the cloud service provider without revealing any private information.