Reviewerly/exHarmony huggingface dataset:
Exploring Paracrawl for Document-level Neural Machine Translation

Document-level neural machine translation (NMT) has outperformed sentence-level NMT on a number of datasets. However, document-level NMT is still not widely adopted in real-world translation systems mainly due to the lack of large-scale general-domain training data for document-level NMT. We examine the effectiveness of using Paracrawl for learning document-level translation. Paracrawl is a large-scale parallel corpus crawled from the Internet and contains data from various domains. The official Paracrawl corpus was released as parallel sentences (extracted from parallel webpages) and therefore previous works only used Paracrawl for learning sentence-level translation. In this work, we extract parallel paragraphs from Paracrawl parallel webpages using automatic sentence alignments and we use the extracted parallel paragraphs as parallel documents for training document-level translation models. We show that document-level NMT models trained with only parallel paragraphs from Paracrawl can be used to translate real documents from TED, News and Europarl, outperforming sentence-level NMT models. We also perform a targeted pronoun evaluation and show that document-level models trained with Paracrawl data can help context-aware pronoun translation.
Replication and Verifiability in Requirements Engineering: the NLP for RE Case

[Context] Study replication is essential for theory building and empirical validation. [Problem] Despite its empirical vocation, requirements engineering (RE) research has given limited attention to study replication, threatening thereby the ability to verify existing results and use previous research as a baseline. [Solution] In this perspective paper, we -- a group of experts in natural language processing (NLP) for RE -- reflect on the challenges for study replication in NLP for RE. Concretely: (i) we report on hands-on experiences of replication, (ii) we review the state-of-the-art and extract replication-relevant information, and (iii) we identify, through focus groups, challenges across two typical dimensions of replication: data annotation and tool reconstruction. NLP for RE is a research area that is suitable for study replication since it builds on automated tools which can be shared, and quantitative evaluation that enable direct comparisons between results. [Results] Replication is hampered by several factors, including the context specificity of the studies, the heterogeneity of the tasks involving NLP, the tasks' inherent hairiness, and, in turn, the heterogeneous reporting structure. To address these issues, we propose an ID card whose goal is to provide a structured summary of research papers, with an emphasis on replication-relevant information. [Contribution] We contribute in this study with: (i) a set of reflections on replication in NLP for RE, (ii) a set of recommendations for researchers in the field to increase their awareness on the topic, and (iii) an ID card that is intended to primarily foster replication, and can also be used in other contexts, e.g., for educational purposes. Practitioners will also benefit from the results since replications increase confidence on research findings.
Observer-Feedback-Feedforward Controller Structures in Reinforcement Learning

The paper proposes the use of structured neural networks for reinforcement learning based nonlinear adaptive control. The focus is on partially observable systems, with separate neural networks for the state and feedforward observer and the state feedback and feedforward controller. The observer dynamics are modelled by recurrent neural networks while a standard network is used for the controller. As discussed in the paper, this leads to a separation of the observer dynamics to the recurrent neural network part, and the state feedback to the feedback and feedforward network. The structured approach reduces the computational complexity and gives the reinforcement learning based controller an {\em understandable} structure as compared to when one single neural network is used. As shown by simulation the proposed structure has the additional and main advantage that the training becomes significantly faster. Two ways to include feedforward structure are presented, one related to state feedback control and one related to classical feedforward control. The latter method introduces further structure with a separate recurrent neural network that processes only the measured disturbance. When evaluated with simulation on a nonlinear cascaded double tank process, the method with most structure performs the best, with excellent feedforward disturbance rejection gains.
Is augmentation effective to improve prediction in imbalanced text datasets?

Imbalanced datasets present a significant challenge for machine learning models, often leading to biased predictions. To address this issue, data augmentation techniques are widely used in natural language processing (NLP) to generate new samples for the minority class. However, in this paper, we challenge the common assumption that data augmentation is always necessary to improve predictions on imbalanced datasets. Instead, we argue that adjusting the classifier cutoffs without data augmentation can produce similar results to oversampling techniques. Our study provides theoretical and empirical evidence to support this claim. Our findings contribute to a better understanding of the strengths and limitations of different approaches to dealing with imbalanced data, and help researchers and practitioners make informed decisions about which methods to use for a given task.
ORIGAMI: A flexible state channels design for public blockchain systems

Public blockchain systems offer security guarantees that cannot be matched by any centralised system. This offering has attracted a lot of interest and has exposed a significant limitation of most blockchain designs with regards to scalability. One of the scaling solutions proposed is state channels which enables serving given applications with minimum number of transactions. Existing state channels designs set multiple compatibility requirements for applications to be deployed. Origami is a novel state channels design which removes most of the requirements of existing approaches, while it also offers a number of new features. Origami enables dynamic groups of users to interact in an unordered way completely off-chain after an initial on-boarding on-chain transaction. The proposed design is analysed in detail and compared to existing schemes, while a formal security analysis validates the security properties it offers.
Controllable Neural Symbolic Regression

In symbolic regression, the goal is to find an analytical expression that accurately fits experimental data with the minimal use of mathematical symbols such as operators, variables, and constants. However, the combinatorial space of possible expressions can make it challenging for traditional evolutionary algorithms to find the correct expression in a reasonable amount of time. To address this issue, Neural Symbolic Regression (NSR) algorithms have been developed that can quickly identify patterns in the data and generate analytical expressions. However, these methods, in their current form, lack the capability to incorporate user-defined prior knowledge, which is often required in natural sciences and engineering fields. To overcome this limitation, we propose a novel neural symbolic regression method, named Neural Symbolic Regression with Hypothesis (NSRwH) that enables the explicit incorporation of assumptions about the expected structure of the ground-truth expression into the prediction process. Our experiments demonstrate that the proposed conditioned deep learning model outperforms its unconditioned counterparts in terms of accuracy while also providing control over the predicted expression structure.
Proof-of-Principle Demonstration of Fully-Passive Quantum Key Distribution

Quantum key distribution (QKD) offers information-theoretic security based on the fundamental laws of physics. However, device imperfections, such as those in active modulators, may introduce side-channel leakage, thus compromising practical security. Attempts to remove active modulation, including passive decoy intensities preparation and polarization encoding, have faced theoretical constraints and inadequate security verification, thus hindering the achievement of a fully passive QKD scheme. Recent research has systematically analyzed the security of a fully passive modulation protocol. Based on this, we utilize the gain-switching technique in combination with the post-selection scheme and perform a proof-of-principle demonstration of a fully passive quantum key distribution with polarization encoding at channel losses of 7.2 dB, 11.6 dB, and 16.7 dB. Our work demonstrates the feasibility of active-modulation-free QKD in polarization-encoded systems.
ReLight My NeRF: A Dataset for Novel View Synthesis and Relighting of Real World Objects

In this paper, we focus on the problem of rendering novel views from a Neural Radiance Field (NeRF) under unobserved light conditions. To this end, we introduce a novel dataset, dubbed ReNe (Relighting NeRF), framing real world objects under one-light-at-time (OLAT) conditions, annotated with accurate ground-truth camera and light poses. Our acquisition pipeline leverages two robotic arms holding, respectively, a camera and an omni-directional point-wise light source. We release a total of 20 scenes depicting a variety of objects with complex geometry and challenging materials. Each scene includes 2000 images, acquired from 50 different points of views under 40 different OLAT conditions. By leveraging the dataset, we perform an ablation study on the relighting capability of variants of the vanilla NeRF architecture and identify a lightweight architecture that can render novel views of an object under novel light conditions, which we use to establish a non-trivial baseline for the dataset. Dataset and benchmark are available at https://eyecan-ai.github.io/rene.
Secure Computation with Shared EPR Pairs (Or: How to Teleport in Zero-Knowledge)

Can a sender non-interactively transmit one of two strings to a receiver without knowing which string was received? Does there exist minimally-interactive secure multiparty computation that only makes (black-box) use of symmetric-key primitives? We provide affirmative answers to these questions in a model where parties have access to shared EPR pairs, thus demonstrating the cryptographic power of this resource. First, we construct a one-shot (i.e., single message) string oblivious transfer (OT) protocol with random receiver bit in the shared EPR pairs model, assuming the (sub-exponential) hardness of LWE. Building on this, we show that {\em secure teleportation through quantum channels} is possible. Specifically, given the description of any quantum operation $Q$, a sender with (quantum) input $\rho$ can send a single classical message that securely transmits $Q(\rho)$ to a receiver. That is, we realize an ideal quantum channel that takes input $\rho$ from the sender and provably delivers $Q(\rho)$ to the receiver without revealing any other information. This immediately gives a number of applications in the shared EPR pairs model: (1) non-interactive secure computation of unidirectional \emph{classical} randomized functionalities, (2) NIZK for QMA from standard (sub-exponential) hardness assumptions, and (3) a non-interactive \emph{zero-knowledge} state synthesis protocol. Next, we construct a two-round (round-optimal) secure multiparty computation protocol for classical functionalities in the shared EPR pairs model that is \emph{unconditionally-secure} in the (quantum-accessible) random oracle model.
Contrastive Tuning: A Little Help to Make Masked Autoencoders Forget

Masked Image Modeling (MIM) methods, like Masked Autoencoders (MAE), efficiently learn a rich representation of the input. However, for adapting to downstream tasks, they require a sufficient amount of labeled data since their rich features code not only objects but also less relevant image background. In contrast, Instance Discrimination (ID) methods focus on objects. In this work, we study how to combine the efficiency and scalability of MIM with the ability of ID to perform downstream classification in the absence of large amounts of labeled data. To this end, we introduce Masked Autoencoder Contrastive Tuning (MAE-CT), a sequential approach that utilizes the implicit clustering of the Nearest Neighbor Contrastive Learning (NNCLR) objective to induce abstraction in the topmost layers of a pre-trained MAE. MAE-CT tunes the rich features such that they form semantic clusters of objects without using any labels. Notably, MAE-CT does not rely on hand-crafted augmentations and frequently achieves its best performances while using only minimal augmentations (crop & flip). Further, MAE-CT is compute efficient as it requires at most 10% overhead compared to MAE re-training. Applied to large and huge Vision Transformer (ViT) models, MAE-CT excels over previous self-supervised methods trained on ImageNet in linear probing, k-NN and low-shot classification accuracy as well as in unsupervised clustering accuracy. With ViT-H/16 MAE-CT achieves a new state-of-the-art in linear probing of 82.2%.
GenCorres: Consistent Shape Matching via Coupled Implicit-Explicit Shape Generative Models

This paper introduces GenCorres, a novel unsupervised joint shape matching (JSM) approach. Our key idea is to learn a mesh generator to fit an unorganized deformable shape collection while constraining deformations between adjacent synthetic shapes to preserve geometric structures such as local rigidity and local conformality. GenCorres presents three appealing advantages over existing JSM techniques. First, GenCorres performs JSM among a synthetic shape collection whose size is much bigger than the input shapes and fully leverages the datadriven power of JSM. Second, GenCorres unifies consistent shape matching and pairwise matching (i.e., by enforcing deformation priors between adjacent synthetic shapes). Third, the generator provides a concise encoding of consistent shape correspondences. However, learning a mesh generator from an unorganized shape collection is challenging, requiring a good initialization. GenCorres addresses this issue by learning an implicit generator from the input shapes, which provides intermediate shapes between two arbitrary shapes. We introduce a novel approach for computing correspondences between adjacent implicit surfaces, which we use to regularize the implicit generator. Synthetic shapes of the implicit generator then guide initial fittings (i.e., via template-based deformation) for learning the mesh generator. Experimental results show that GenCorres considerably outperforms state-of-the-art JSM techniques. The synthetic shapes of GenCorres also achieve salient performance gains against state-of-the-art deformable shape generators.
FFR V1.0: Fon-French Neural Machine Translation

Africa has the highest linguistic diversity in the world. On account of the importance of language to communication, and the importance of reliable, powerful and accurate machine translation models in modern inter-cultural communication, there have been (and still are) efforts to create state-of-the-art translation models for the many African languages. However, the low-resources, diacritical and tonal complexities of African languages are major issues facing African NLP today. The FFR is a major step towards creating a robust translation model from Fon, a very low-resource and tonal language, to French, for research and public use. In this paper, we describe our pilot project: the creation of a large growing corpora for Fon-to-French translations and our FFR v1.0 model, trained on this dataset. The dataset and model are made publicly available.
Development of Media Based Electronic Book Local Wisdom to Improve Elementary Students’ Literacy

E-book is a digital-based learning media that visualizes abstract material into visual form. The purpose of this research is to develop E-book learning media based on local wisdom. The development of this product uses an adaptation of the research and development model (Research &amp; Development) using the ADDIE model. The result showed that the development of valid E-book media from material validator experts was 95% and 87% of learning media with very valid criteria. The result of the effectiveness of classical completeness from a pretest value of 6% increased to 94% very good category. The results of the normalized N-gain test from an average result of 0.627 are in the medium category with criteria quite effectively used in increasing the reading literacy of fifth grade students. Keywords: E-book, local wisdom, reading literacy
The artificial intelligence entity as a legal person

This article discusses the legal position of an Artificial Intelligence Entity (AIE) that is able to create something novel, like an artwork. Rather than examining a fictitious being, I inquire the position of human beings, comparing it with that of a conceivable AIE. The nature of an AIE in terms of matter and mind is compared with that of a human being, after which it is inquired whether it may, like a human being, be deemed a legal person. This analysis serves as a prelude to the question of whether an AIE may be a copyright owner.
ESTIMATION OF MAXIMUM LIKELIHOOD WEIGHTED LOGISTIC REGRESSION USING GENETIC ALGORITHM (CASE STUDY: INDIVIDUAL WORK STATUS IN MALANG CITY)

Weighted Logistic Regression (WLR) is a method used to overcome imbalanced data or rare events by using weighting and is part of the development of a simple logistic regression model. Parameter estimation of the WLR model uses Maximum Likelihood estimation. The maximum likelihood parameter estimator value is obtained using an optimization approach. The Genetic algorithm is an optimization computational algorithm that is used to optimize the estimation of model parameters. This study aims to estimate the Maximum Likelihood Weighted Logistic Regression with the applied genetic algorithm and determine the significant variables that affect the working status of individuals in Malang City. The data used is the result of data collection from the National Labor Force Survey of Malang City in 2020. The results of the analysis show that the variable education completed and the number of household members has a significant effect on individual work status in Malang City.
Selecting the Important Features to Classify the Archaeological Fragments by Using Statistical Tools

Ceramics were one of the earliest materials used by humans, dating back to prehistoric times. As such, they offer a unique perspective into the evolution of human civilization, from the development of agriculture and trade to the rise of complex societies and the emergence of art. Feature selection, the process of representing an object in the least dimensions, is one of the most important and difficult steps in pattern recognition. Therefore, meticulous selection of important features for classification is required. In this study, we propose a method based on Multidimensional Scaling (MDS) to reduce the dimensions of ancient ceramic fragment features. This method focuses on selecting the most important features based on the density of the grayscale image and texture. Finally, we use the Euclidean distance equation to classify objects into similar groups. With a database containing more than 300 images, the experiment achieved an impressive 90% success rate in accurately categorizing fragments as either similar or non-similar. These results demonstrate the effectiveness and promise of the proposed approach for image classification tasks, emphasizing the potential of statistical methods and image processing techniques for addressing complex computer vision challenges.
Prices and Reviews as Information Cues in E-Commerce Recommender Systems

Online retailers widely use prices and reviews, as additional information cues along with thumbnail pictures, when making product recommendations to consumers. They may use both prices and reviews (“dual cues”), either one (“single cue”), or simply display pictures of recommended products. The authors conducted a field experiment at a US online retailer to examine how the number of information cues in product recommender systems (i.e., no prices or reviews, single cue, dual cue) affects consumers’ search engagement and purchases. Results showed a nonlinear purchase effect: Providing information cues was better than not providing them; single cue conditions resulted in higher transaction values than dual cue conditions and also kindled more pre-purchase search activities, such as recommender impressions and clicks, non-recommender product views, and engagement with the keyword search tool. Further, increased search frictions in single cue conditions stimulated consumers’ search engagement, whereas reduced search frictions in dual cue conditions facilitated consumer rejection of recommended products that did not match consumer preferences. The findings imply that marketers can use information cues strategically in recommendation systems to create search frictions by withholding some information to motivate consumers to engage more in the search process, leading to more purchases.
Big Data Stream Mining Using Integrated Framework with Classification and Clustering Methods

Abstract: The causes of numerous sorts of big data and data stream problems include the quick development of industry firms, the vast amount of data generated by these innovations, and the exponential growth of industrial company websites. There are numerous stream data mining algorithms for classification and grouping, each with its own unique set of attributes and important features. Ensemble classifiers aid in enhancing the greatest prediction performance results from these cutting-edge techniques. Ensemble approaches teach multiple types of classifiers and clusters rather than a single classifier. Their machine learning prediction findings are merged to form a voting schedule. This research offered a framework for stream data mining based on miss categorization stream data, utilizing the advantages of assembly technology. Real-world data streams are used in experiments. The experimental results are compared to modern popular ensemble techniques such as Boosting and Bagging. The test results show an increase in accuracy rate and decrease in classification time.
Off-Line and Online Handwritten Character Recognition Using RNN-GRU Algorithm

Abstract: Recognizing handwritten characters is an extremely difficult task in the domains of pattern recognition and computer vision. It involves the use of a process that enables computers to identify and convert handwritten or printed characters, such as letters and numbers, into a digital format that is usable by the computer. Currently, the RNN-CNN hybrid algorithm is employed to predict handwritten text in images with an accuracy rate of 91.5%. However, the existing system can only recognize characters and words character-by-character and word-by-word. The proposed system aims to address this limitation by enabling line-byline recognition and the conversion of handwritten text to OCR. To achieve this, the system utilizes the GRU algorithm to predict the next letter in incomplete words. Furthermore, the IAM dataset, consisting of 135,000 annotated sentences, is utilized to detect and rectify spelling errors in texts.