Reviewerly/exHarmony huggingface dataset:
Literature Study of Convolutional Neural Network Algorithm for Batik Classification

Batik is a hereditary cultural heritage that has high aesthetic value and deep philosophy. Currently, Indonesian batik has various types of different motifs and patterns, which are spread in Indonesia with their names and meanings. Batik classification uses Convolutional Neural Network as a pattern recognition method, especially batik image classification. The method used is a literature study, looking at studies from several journals regarding the Convolutional Neural Network Algorithm in Classification and providing conclusions about the usefulness of the algorithm. Analysis This literature study analyzes each journal from previous research related to the Convolutional Neural Network Algorithm in classifying Batik. The results of the analysis, conducted a discussion to better know the characteristics and application of Convolutional Neural Network in the classification of Batik. After discussing, this analysis ends with conclusions about the Convolutional Neural Network algorithm in classifying Batik. Based on previous studies, it can be seen that the convolution neural network can work well for image classification with large datasets. By evaluating the method that has been described by considering the architecture and the level of accuracy, namely getting an accuracy level of 100% with an image size of 128 x 128 and regarding the classification of batik, it shows that image size, image quality, image patterns affect the batik classification process.
Design and Construction of a PBL Based Evaluation Index System for Classroom Music Education

The quality of classroom education is essential to the construction and development of departments and colleges, and a mirror of the teaching level and quality of teachers. Project-based learning (PBL) tries to create problem scenarios for learners, and encourage them to think deeper and communicate more. Inspired by PBL, this paper analyzes the existing evaluation index systems (EISs) for classroom music education in colleges, and summarizes the application of PBL in music teaching. Besides, a novel EIS is designed and constructed for classroom music education. The results show that: PBL stresses the changes of learners through the learning, and enables teachers to combine pre-, in-, and post-class methods to comprehensively evaluate the music literacy of students; PBL exerts a positive effect on students’ learning. The results lay a theoretical basis for reforming the EIS of classroom music education.
Photon-number squeezing in nano- and microlasers

Based on theoretical predictions on the appearance of antibunching before the laser threshold at the nano- and microscale, we analyze the amount of photon-number squeezing naturally produced in the laser emission. Up to 3 dB photon number noise reduction is obtained in comparison with the coherent emission, with output power in the range of pW and with negligible effects due to pump fluctuations. The scheme requires a moderately high Q cavity and holds promise for the construction of a simple and effective photon-number squeezed source.
K-Nearest Neighbor (K-NN) algorithm with Euclidean and Manhattan in classification of student graduation

K-Nearest Neighbor (K-NN) algorithm is a classification algorithm that has been proven to solve various classification problems. Two approaches that can be used in this algorithm are K-NN with Euclidean and K-NN with Manhattan. The research aims to apply the K-NN algorithm with Euclidean and K-NN with Manhattan to classify the accuracy of graduation. Student graduation is determined by the variables of gender, major, number of first-semester credits, number of second-semester credits, number of third-semester credits, grade point on the first semester, grade point on the second semester, grade point on the third semester, and age. These variables determine the accuracy of student graduation, timely or untimely. The implementation of the K-NN algorithm is carried out using Rapidminer software. The results were obtained after testing 380 training data and 163 testing data. The best accuracy system was achieved at K=7 with a value of 85.28%. The two algorithmic approaches did not affect the accuracy of the results. Furthermore, the addition of the value of K did not completely affect the accuracy.
Threshold Key Management Scheme for Blockchain-Based Intelligent Transportation Systems

Intelligent transportation systems (ITS) have always been an important application of Internet of Things (IoT). Today, big data and cloud computing have further promoted the construction and development of ITS. At the same time, the development of blockchain has also brought new features and convenience to ITS. However, due to the endless emergence of increasingly advanced types of attacks, the security of blockchain-based ITS needs more attention from industry and academia. In this paper, we focus on exploring the primitives in cryptography to guarantee the security of blockchain-based ITS. In particular, the authentication, encryption, and key management schemes in cryptography are discussed. Furthermore, we propose two methods for achieving the threshold key management in blockchain-based ITS. The proposed threshold key management scheme (with threshold <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>t</mi> </math> ) enables various stakeholders to recover a secret if the number of participated stakeholders is at least <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>t</mi> </math> . It should be noted that the proposed threshold key management scheme is efficient and secure for multiple users in blockchain-based ITS, especially for the data-sharing scenario.
A Three-Stage Learning Framework for Low-Resource Knowledge-Grounded Dialogue Generation

Neural conversation models have shown great potentials towards generating fluent and informative responses by introducing external background knowledge.Nevertheless, it is laborious to construct such knowledge-grounded dialogues, and existing models usually perform poorly when transfer to new domains with limited training samples.Therefore, building a knowledge-grounded dialogue system under the low-resource setting is a still crucial issue.In this paper, we propose a novel threestage learning framework based on weakly supervised learning which benefits from large scale ungrounded dialogues and unstructured knowledge base.To better cooperate with this framework, we devise a variant of Transformer with decoupled decoder which facilitates the disentangled learning of response generation and knowledge incorporation.Evaluation results on two benchmarks indicate that our approach can outperform other state-of-the-art methods with less training data, and even in zero-resource scenario, our approach still performs well.
Model for Phishing Websites Classification Using Artificial Neural Network

Internet users might be exposed to various forms of threats that can create economic harm, identity fraud, and lack of faith in e-commerce and online banking by consumers as the internet has become a necessary part of everyday activities. Phishing can be regarded as a type of web extortions described as the skill of imitating an honest company's website aimed at obtaining private information for example usernames, passwords, and bank information. The accuracy of classification is very significant in order to produce high accuracy results and least error rate in classification of phishing websites. The objective of this research is to model a suitable neural network classifier and then use the model to class the phishing website data set and evaluate the performance of the classifier. This research will use a phishing website data set which was retrieved from UCI repository and will be experimented using Encog Workbench tool. The main expected outcome from this study is the preliminary ANN classifier which classifies the target class of the phishing websites data set accurately, either phishy, suspicious or legitimate ones. The results indicate that ANN (9-5-1) model outperforms other models by achieving the highest accuracy and the least MSE value which is 0.04745.
Text-to-Table: A New Way of Information Extraction

We study a new problem setting of information extraction (IE), referred to as text-to-table .In text-to-table, given a text, one creates a table or several tables expressing the main content of the text, while the model is learned from text-table pair data.The problem setting differs from those of the existing methods for IE.First, the extraction can be carried out from long texts to large tables with complex structures.Second, the extraction is entirely data-driven, and there is no need to explicitly define the schemas.As far as we know, there has been no previous work that studies the problem.In this work, we formalize textto-table as a sequence-to-sequence (seq2seq) problem.We first employ a seq2seq model finetuned from a pre-trained language model to perform the task.We also develop a new method within the seq2seq approach, exploiting two additional techniques in table generation: table constraint and table relation embeddings.We consider text-to-table as an inverse problem of the well-studied table-to-text, and make use of four existing table-to-text datasets in our experiments on text-to-table.Experimental results show that the vanilla seq2seq model can outperform the baseline methods of using relation extraction and named entity extraction.The results also show that our method can further boost the performances of the vanilla seq2seq model.We further discuss the main challenges of the proposed task.The code and data are available at https://github.com/shirley-wu/text_to_table.
Sentence Bottleneck Autoencoders from Transformer Language Models

Representation learning for text via pretraining a language model on a large corpus has become a standard starting point for building NLP systems.This approach stands in contrast to autoencoders, also trained on raw text, but with the objective of learning to encode each input as a vector that allows full reconstruction.Autoencoders are attractive because of their latent space structure and generative properties.We therefore explore the construction of a sentence-level autoencoder from a pretrained, frozen transformer language model.We adapt the masked language modeling objective as a generative, denoising one, while only training a sentence bottleneck and a single-layer modified transformer decoder.We demonstrate that the sentence representations discovered by our model achieve better quality than previous methods that extract representations from pretrained transformers on text similarity tasks, style transfer (an example of controlled generation), and single-sentence classification tasks in the GLUE benchmark, while using fewer parameters than large pretrained models. 1
Haptic Ankle Platform for Interactive Walking in Virtual Reality

This article presents an impedance type ankle haptic interface for providing users with an immersive navigation experience in virtual reality (VR). The ankle platform, actuated by an electric motor with feedback control, enables the use of foot-tapping gestures to create a walking experience like a real one and to haptically render different types of walking terrains. Experimental studies demonstrated that the interface can be easily used to generate virtual walking and is capable of rendering terrains, such as hard and soft surfaces, and multi-layer complex dynamic terrains. The designed system is a seated-type VR locomotion interface, therefore allowing its user to maintain a stable seated posture to comfortably navigate a virtual scene.
Novel deep learning framework for wideband spectrum characterization at sub-Nyquist rate

Limited availability and high auction cost of the sub-6 GHz spectrum led to the introduction of spectrum-sharing in 5G networks. This demands base stations with the capability of automatic wideband spectrum characterization (AWSC) to identify available vacant spectrum and parameters (carrier frequency, modulation scheme, etc.) of occupied bands. Since WSC at Nyquist sampling (NS) is area and power-hungry and conventional statistical AWSC performs poorly at a low signal-to-noise ratio (SNR), we propose a novel sub-Nyquist sampling (SNS) based deep-learning framework. It is a single unified pipeline that accomplishes two tasks: (1) Reconstruct the signal directly from the sub-Nyquist samples, and (2) Identify the occupancy status and modulation scheme of all bands. The proposed non-iterative approach based reconstruction provides the occupancy status of all bands in a single forward pass leading to significant improvement in execution time over state-of-the-art iterative methods. In addition, the proposed approach does not need complex signal conditioning between reconstruction and characterization. We extensively compare the performance of our framework for a wide range of modulation schemes, SNR and channel conditions. We show that the proposed framework outperforms existing SNS based characterization and its performance approaches NS based framework with an increase in SNR without compromising computation complexity. A single unified deep-learning framework makes the proposed method a good candidate for reconfigurable platforms.
Translation Error Detection as Rationale Extraction

Recent Quality Estimation (QE) models based on multilingual pre-trained representations have achieved very competitive results in predicting the overall quality of translated sentences.However, detecting specifically which translated words are incorrect is a more challenging task, especially when dealing with limited amounts of training data.We hypothesize that, not unlike humans, successful QE models rely on translation errors to predict overall sentence quality.By exploring a set of feature attribution methods that assign relevance scores to the inputs to explain model predictions, we study the behaviour of state-of-theart sentence-level QE models and show that explanations (i.e.rationales) extracted from these models can indeed be used to detect translation errors.We therefore (i) introduce a novel semi-supervised method for word-level QE; and (ii) propose to use the QE task as a new benchmark for evaluating the plausibility of feature attribution, i.e. how interpretable model explanations are to humans.
Modeling of Surgical Procedures Using Statecharts for Semi-Autonomous Robotic Surgery

In this paper we propose a new methodology to model surgical procedures that is specifically tailored to semi-autonomous robotic surgery. We propose to use a restricted version of statecharts to merge the bottom-up approach, based on data-driven techniques (e.g., machine learning), with the top-down approach based on knowledge representation techniques. We consider medical knowledge about the procedure and sensing of the environment in two concurrent regions of the statecharts to facilitate re-usability and adaptability of the modules. Our approach allows producing a well defined procedural model exploiting the hierarchy capability of the statecharts, while machine learning modules act as soft sensors to trigger state transitions. Integrating data driven and prior knowledge techniques provides a robust, modular, flexible and re-configurable methodology to define a surgical procedure which is comprehensible by both humans and machines. We validate our approach on the three surgical phases of a Robot-Assisted Radical Prostatectomy (RARP) that directly involve the assistant surgeon: bladder mobilization, bladder neck transection, and vesicourethral anastomosis, all performed on synthetic manikins.
FDFB: Full Domain Functional Bootstrapping Towards Practical Fully Homomorphic Encryption

Computation on ciphertexts of all known fully homomorphic encryption (FHE) schemes induces some noise, which, if too large, will destroy the plaintext. Therefore, the bootstrapping technique that re-encrypts a ciphertext and reduces the noise level remains the only known way of building FHE schemes for arbitrary unbounded computations. The bootstrapping step is also the major efficiency bottleneck in current FHE schemes. A promising direction towards improving concrete efficiency is to exploit the bootstrapping process to perform useful computation while reducing the noise at the same time. We show a bootstrapping algorithm, which embeds a lookup table and evaluates arbitrary functions of the plaintext while reducing the noise. Depending on the choice of parameters, the resulting homomorphic encryption scheme may be either an exact FHE or homomorphic encryption for approximate arithmetic. Since we can evaluate arbitrary functions over the plaintext space, we can use the natural homomorphism of Regev encryption to compute affine functions without bootstrapping almost for free. Consequently, our algorithms are particularly suitable for arithmetic circuits over a finite field with many additions and scalar multiplication gates. We achieve significant speedups when compared to binary circuit-based FHE. For example, we achieve 280-1200x speedups when computing an affine function of size 784 followed by any univariate function when compared to FHE schemes that compute binary circuits. With our bootstrapping algorithm, we can efficiently convert between arithmetic and boolean plaintexts and extend the plaintext space using the Chinese remainder theorem. Furthermore, we can run the computation in an exact and approximate mode where we trade-off the size of the plaintext space with approximation error. We provide a tight error analysis and show several parameter sets for our bootstrapping. Finally, we implement our algorithm and provide extensive tests. We demonstrate our algorithms by evaluating different neural networks in several parameter and accuracy settings.
ICDAR 2021 Competition on Historical Document Classification

This competition investigated the performance of historical document classification. The analysis of historical documents is a difficult challenge commonly solved by trained humanists. We provided three different classification tasks, which can be solved individually or jointly: font group/script type, location, date. The document images are provided by several institutions and are taken from handwritten and printed books as well as from charters. In contrast to previous competitions, all participants relied upon Deep Learning based approaches. Nevertheless, we saw a great performance variety of the different submitted systems. The easiest task seemed to be font group recognition while the script type classification and location classification were more challenging. In the dating task, the best system achieved a mean absolute error of about 22 years.
Software Engineering in Small Software Companies: Consolidating and Integrating Empirical Literature Into a Process Tool Adoption Framework

Small software companies face numerous challenges of complexity, unstructured software development processes and scarce resources. This notwithstanding, the companies have dominated the software market by 80 percent. The practice and products of these companies are still persistently marred by quality issues arising from the processes, with evidence indicating that process tools do not fit the unique contexts in which they operate. Significant strides have been made in transforming software development practice; however, the challenges are still evidently apparent. Hence the need to establish how knowledge areas are applied in process practice, understand the context of software development and its implication in practice, how process tools are utilised in practice and evaluate quality of research in software literature The researchers undertook a systematic mapping study to determine the state of practice in empirical literature on software engineering of SSCs by examining and classifying 1096 publications. Other than the finding that research quality was low and affecting generalisation and transferability, the results also revealed interesting findings which we finally consolidated and integrated to develop two contributions (i) a software development process adoption theoretical framework which provides new insights of understanding software development and (ii) a 3-point guideline for research quality.
Who Am I?

To enable targeted ads, companies profile Internet users, automatically inferring potential interests and demographics. While current profiling centers on users' web browsing data, smartphones and other devices with rich sensing capabilities portend profiling techniques that draw on methods from ubiquitous computing. Unfortunately, even existing profiling and ad-targeting practices remain opaque to users, engendering distrust, resignation, and privacy concerns. We hypothesized that making profiling visible at the time and place it occurs might help users better understand and engage with automatically constructed profiles. To this end, we built a technology probe that surfaces the incremental construction of user profiles from both web browsing and activities in the physical world. The probe explores transparency and control of profile construction in real time. We conducted a two-week field deployment of this probe with 25 participants. We found that increasing the visibility of profiling helped participants anticipate how certain actions can trigger specific ads. Participants' desired engagement with their profile differed in part based on their overall attitudes toward ads. Furthermore, participants expected algorithms would automatically determine when an inference was inaccurate, no longer relevant, or off-limits. Current techniques typically do not do this. Overall, our findings suggest that leveraging opportunistic moments within pervasive computing to engage users with their own inferred profiles can create more trustworthy and positive experiences with targeted ads.
Filling the Gaps in Ancient Akkadian Texts: A Masked Language Modelling Approach

We present models which complete missing text given transliterations of ancient Mesopotamian documents, originally written on cuneiform clay tablets (2500 BCE -100 CE).Due to the tablets' deterioration, scholars often rely on contextual cues to manually fill in missing parts in the text in a subjective and time-consuming process.We identify that this challenge can be formulated as a masked language modelling task, used mostly as a pretraining objective for contextualized language models.Following, we develop several architectures focusing on the Akkadian language, the lingua franca of the time.We find that despite data scarcity (1M tokens) we can achieve state of the art performance on missing tokens prediction (89% hit@5) using a greedy decoding scheme and pretraining on data from other languages and different time periods.Finally, we conduct human evaluations showing the applicability of our models in assisting experts to transcribe texts in extinct languages.
Boomeyong: Embedding Yoyo within Boomerang and its Applications to Key Recovery Attacks on AES and Pholkos

This work investigates a generic way of combining two very effective and well-studied cryptanalytic tools, proposed almost 18 years apart, namely the boomerang attack introduced by Wagner in FSE 1999 and the yoyo attack by Ronjom et al. in Asiacrypt 2017. In doing so, the s-box switch and ladder switch techniques are leveraged to embed a yoyo trail inside a boomerang trail. As an immediate application, a 6-round key recovery attack on AES-128 is mounted with time complexity of 278. A 10-round key recovery attack on recently introduced AES-based tweakable block cipher Pholkos is also furnished to demonstrate the applicability of the new technique on AES-like constructions. The results on AES are experimentally verified by applying and implementing them on a small scale variant of AES. We provide arguments that draw a relation between the proposed strategy with the retracing boomerang attack devised in Eurocrypt 2020. To the best of our knowledge, this is the first attempt to merge the yoyo and boomerang techniques to analyze SPN ciphers and warrants further attention as it has the potential of becoming an important cryptanalysis tool.