Reviewerly/exHarmony huggingface dataset:
A neuro-symbolic approach for multimodal reference expression comprehension

Human-Machine Interaction (HMI) systems have gained huge interest in recent years, with reference expression comprehension being one of the main challenges. Traditionally human-machine interaction has been mostly limited to speech and visual modalities. However, to allow for more freedom in interaction, recent works have proposed the integration of additional modalities, such as gestures in HMI systems. We consider such an HMI system with pointing gestures and construct a table-top object picking scenario inside a simulated virtual reality (VR) environment to collect data. Previous works for such a task have used deep neural networks to classify the referred object, which lacks transparency. In this work, we propose an interpretable and compositional model, crucial to building robust HMI systems for real-world application, based on a neuro-symbolic approach to tackle this task. Finally we also show the generalizability of our model on unseen environments and report the results.
Efficient Parameterized Pattern Matching in Sublinear Space

The parameterized matching problem is a variant of string matching, which is to search for all parameterized occurrences of a pattern $P$ in a text $T$. In considering matching algorithms, the combinatorial natures of strings, especially periodicity, play an important role. In this paper, we analyze the properties of periods of parameterized strings and propose a generalization of Galil and Seiferas's exact matching algorithm (1980) into parameterized matching, which runs in $O(\pi|T|+|P|)$ time and $O(\log{|P|}+|{\rm\Pi}|)$ space in addition to the input space, where ${\rm\Pi}$ is the parameter alphabet and $\pi$ is the number of parameter characters appearing in $P$ plus one.
SGFormer: Simplifying and Empowering Transformers for Large-Graph Representations

Learning representations on large-sized graphs is a long-standing challenge due to the inter-dependence nature involved in massive data points. Transformers, as an emerging class of foundation encoders for graph-structured data, have shown promising performance on small graphs due to its global attention capable of capturing all-pair influence beyond neighboring nodes. Even so, existing approaches tend to inherit the spirit of Transformers in language and vision tasks, and embrace complicated models by stacking deep multi-head attentions. In this paper, we critically demonstrate that even using a one-layer attention can bring up surprisingly competitive performance across node property prediction benchmarks where node numbers range from thousand-level to billion-level. This encourages us to rethink the design philosophy for Transformers on large graphs, where the global attention is a computation overhead hindering the scalability. We frame the proposed scheme as Simplified Graph Transformers (SGFormer), which is empowered by a simple attention model that can efficiently propagate information among arbitrary nodes in one layer. SGFormer requires none of positional encodings, feature/graph pre-processing or augmented loss. Empirically, SGFormer successfully scales to the web-scale graph ogbn-papers100M and yields up to 141x inference acceleration over SOTA Transformers on medium-sized graphs. Beyond current results, we believe the proposed methodology alone enlightens a new technical path of independent interest for building Transformers on large graphs.
SegT: A Novel Separated Edge-guidance Transformer Network for Polyp Segmentation

Accurate segmentation of colonoscopic polyps is considered a fundamental step in medical image analysis and surgical interventions. Many recent studies have made improvements based on the encoder-decoder framework, which can effectively segment diverse polyps. Such improvements mainly aim to enhance local features by using global features and applying attention methods. However, relying only on the global information of the final encoder block can result in losing local regional features in the intermediate layer. In addition, determining the edges between benign regions and polyps could be a challenging task. To address the aforementioned issues, we propose a novel separated edge-guidance transformer (SegT) network that aims to build an effective polyp segmentation model. A transformer encoder that learns a more robust representation than existing CNN-based approaches was specifically applied. To determine the precise segmentation of polyps, we utilize a separated edge-guidance module consisting of separator and edge-guidance blocks. The separator block is a two-stream operator to highlight edges between the background and foreground, whereas the edge-guidance block lies behind both streams to strengthen the understanding of the edge. Lastly, an innovative cascade fusion module was used and fused the refined multi-level features. To evaluate the effectiveness of SegT, we conducted experiments with five challenging public datasets, and the proposed model achieved state-of-the-art performance.
$\texttt{causalAssembly}$: Generating Realistic Production Data for Benchmarking Causal Discovery

Algorithms for causal discovery have recently undergone rapid advances and increasingly draw on flexible nonparametric methods to process complex data. With these advances comes a need for adequate empirical validation of the causal relationships learned by different algorithms. However, for most real data sources true causal relations remain unknown. This issue is further compounded by privacy concerns surrounding the release of suitable high-quality data. To help address these challenges, we gather a complex dataset comprising measurements from an assembly line in a manufacturing context. This line consists of numerous physical processes for which we are able to provide ground truth causal relationships on the basis of a detailed study of the underlying physics. We use the assembly line data and associated ground truth information to build a system for generation of semisynthetic manufacturing data that supports benchmarking of causal discovery methods. To accomplish this, we employ distributional random forests in order to flexibly estimate and represent conditional distributions that may be combined into joint distributions that strictly adhere to a causal model over the observed variables. The estimated conditionals and tools for data generation are made available in our Python library $\texttt{causalAssembly}$. Using the library, we showcase how to benchmark several well-known causal discovery algorithms.
Shape Guided Gradient Voting for Domain Generalization

Domain generalization aims to address the domain shift between training and testing data. To learn the domain invariant representations, the model is usually trained on multiple domains. It has been found that the gradients of network weight relative to a specific task loss can characterize the task itself. In this work, with the assumption that the gradients of a specific domain samples under the classification task could also reflect the property of the domain, we propose a Shape Guided Gradient Voting (SGGV) method for domain generalization. Firstly, we introduce shape prior via extra inputs of the network to guide gradient descending towards a shape-biased direction for better generalization. Secondly, we propose a new gradient voting strategy to remove the outliers for robust optimization in the presence of shape guidance. To provide shape guidance, we add edge/sketch extracted from the training data as an explicit way, and also use texture augmented images as an implicit way. We conduct experiments on several popular domain generalization datasets in image classification task, and show that our shape guided gradient updating strategy brings significant improvement of the generalization.
PTDRL: Parameter Tuning using Deep Reinforcement Learning

A variety of autonomous navigation algorithms exist that allow robots to move around in a safe and fast manner. However, many of these algorithms require parameter re-tuning when facing new environments. In this paper, we propose PTDRL, a parameter-tuning strategy that adaptively selects from a fixed set of parameters those that maximize the expected reward for a given navigation system. Our learning strategy can be used for different environments, different platforms, and different user preferences. Specifically, we attend to the problem of social navigation in indoor spaces, using a classical motion planning algorithm as our navigation system and training its parameters to optimize its behavior. Experimental results show that PTDRL can outperform other online parameter-tuning strategies.
Explaining the Model and Feature Dependencies by Decomposition of the Shapley Value

Shapley values have become one of the go-to methods to explain complex models to end-users. They provide a model agnostic post-hoc explanation with foundations in game theory: what is the worth of a player (in machine learning, a feature value) in the objective function (the output of the complex machine learning model). One downside is that they always require outputs of the model when some features are missing. These are usually computed by taking the expectation over the missing features. This however introduces a non-trivial choice: do we condition on the unknown features or not? In this paper we examine this question and claim that they represent two different explanations which are valid for different end-users: one that explains the model and one that explains the model combined with the feature dependencies in the data. We propose a new algorithmic approach to combine both explanations, removing the burden of choice and enhancing the explanatory power of Shapley values, and show that it achieves intuitive results on simple problems. We apply our method to two real-world datasets and discuss the explanations. Finally, we demonstrate how our method is either equivalent or superior to state-to-of-art Shapley value implementations while simultaneously allowing for increased insight into the model-data structure.
Handwritten Text Recognition from Crowdsourced Annotations

In this paper, we explore different ways of training a model for handwritten text recognition when multiple imperfect or noisy transcriptions are available. We consider various training configurations, such as selecting a single transcription, retaining all transcriptions, or computing an aggregated transcription from all available annotations. In addition, we evaluate the impact of quality-based data selection, where samples with low agreement are removed from the training set. Our experiments are carried out on municipal registers of the city of Belfort (France) written between 1790 and 1946. % results The results show that computing a consensus transcription or training on multiple transcriptions are good alternatives. However, selecting training samples based on the degree of agreement between annotators introduces a bias in the training data and does not improve the results. Our dataset is publicly available on Zenodo: https://zenodo.org/record/8041668.
Grammatical gender in Swedish is predictable using recurrent neural networks

The grammatical gender of Swedish nouns is a mystery. While there are few rules that can indicate the gender with some certainty, it does in general not depend on either meaning or the structure of the word. In this paper we demonstrate the surprising fact that grammatical gender for Swedish nouns can be predicted with high accuracy using a recurrent neural network (RNN) working on the raw character sequence of the word, without using any contextual information.
Guiding Language Models of Code with Global Context using Monitors

Language models of code (LMs) work well when the surrounding code provides sufficient context. This is not true when it becomes necessary to use types, functionality or APIs defined elsewhere in the repository or a linked library, especially those not seen during training. LMs suffer from limited awareness of such global context and end up hallucinating. Integrated development environments (IDEs) assist developers in understanding repository context using static analysis. We extend this assistance, enjoyed by developers, to LMs. We propose monitor-guided decoding (MGD) where a monitor uses static analysis to guide the decoding. We construct a repository-level dataset PragmaticCode for method-completion in Java and evaluate MGD on it. On models of varying parameter scale, by monitoring for type-consistent object dereferences, MGD consistently improves compilation rates and agreement with ground truth. Further, LMs with fewer parameters, when augmented with MGD, can outperform larger LMs. With MGD, SantaCoder-1.1B achieves better compilation rate and next-identifier match than the much larger text-davinci-003 model. We also conduct a generalizability study to evaluate the ability of MGD to generalize to multiple programming languages (Java, C# and Rust), coding scenarios (e.g., correct number of arguments to method calls), and to enforce richer semantic constraints (e.g., stateful API protocols). Our data and implementation are available at https://github.com/microsoft/monitors4codegen .
Jamming Detection in Low-BER Mobile Indoor Scenarios via Deep Learning

The current state of the art on jamming detection relies on link-layer metrics. A few examples are the bit-error-rate (BER), the packet delivery ratio, the throughput, and the increase in the signal-to-noise ratio (SNR). As a result, these techniques can only detect jamming \emph{ex-post}, i.e., once the attack has already taken down the communication link. These solutions are unfit for mobile devices, e.g., drones, which might lose the connection to the remote controller, being unable to predict the attack. Our solution is rooted in the idea that a drone unknowingly flying toward a jammed area is experiencing an increasing effect of the jamming, e.g., in terms of BER and SNR. Therefore, drones might use the above-mentioned phenomenon to detect jamming before the decrease of the BER and the increase of the SNR completely disrupt the communication link. Such an approach would allow drones and their pilots to make informed decisions and maintain complete control of navigation, enhancing security and safety. This paper proposes Bloodhound+, a solution for jamming detection on mobile devices in low-BER regimes. Our approach analyzes raw physical-layer information (I-Q samples) acquired from the wireless channel. We assemble this information into grayscale images and use sparse autoencoders to detect image anomalies caused by jamming attacks. To test our solution against a wide set of configurations, we acquired a large dataset of indoor measurements using multiple hardware, jamming strategies, and communication parameters. Our results indicate that Bloodhound+ can detect indoor jamming up to 20 meters from the jamming source at the minimum available relative jamming power, with a minimum accuracy of 99.7\%. Our solution is also robust to various sampling rates adopted by the jammer and to the type of signal used for jamming.
ON THE ISSUE OF OUTLOOK UNIVERSALS RENDERING IN ARTISTIC TRANSLATION

The article is dedicated to the issue of possible challenges in rendering outlook universals in translation of English fantasy novels into Ukrainian and the ways of overcoming them.It is proved that untranslatability of outlook universals is related to the discrepancy in psycholinguistic and ethnolinguistic peculiarities of the source and target language cultures.Thus, translation adequacy may be achieved by comprising two opposite strategies: foreignization and domestication.The active participants include the author of the source text as the representative of the authentic foreign culture and the translator, as the mediator and interpreter.Among the possible challenges of translation there are idiomatic expressions with cultural and historical background, occasional words and talking names.Among other possible challenges at work there are typologically distant language discrepancies, partial correspondences, the uniqueness of the source text fantasy novel atmosphere as the reflection of the national mentality as well as the author's imagination, and the insufficient amount of preliminary background information necessary for adequate translation.In the focus of the article there are famous fantasy novels from J.K. Rowling's Harry Potter's Saga, namely "Harry Potter and the Sorcerer's Stone" and "Harry Potter and the Chambre of Secrets" and their Ukrainian translations.Expressions or lexical units which cannot be rendered in translation are termed as lacunas.Lacunas don't prove the impossibility of translation, they indicate at the absence of the proper equivalent with reference to the social, cultural, ethnic and historic peculiarities.The processes of demotivation, loss of mental and cultural coloring in translation and the resulting idiomaticity are also investigated.Descriptive translation and translator's footnote are suggested as the most appropriate translation transformations though often combined with transcription or transplantation.The overview of the theoretical groundings comprises the most relevant research works on the issue of untranslatability in the paradigm of different approaches and trends, including the French translation school.It is resumed that foreignization and domestication strategies often go hand in hand and prove to prevent possible semantic and expressive losses when used jointly.Untranslatability of the talking names mostly stands for the challenges in translation caused by the absence of the full equivalent either due to the lack of intelligence or competence in a certain field of knowledge or activity, sometimes this may also be predetermined by the insufficient psycholinguistic or sociocultural preparation of the translator.The perspective for further research is seen in the study of the linguistic, social, cultural and psycholinguistic levels in translation.
Analyzing the Student’s Phonological Awareness and its Relation to the Reading Skill in EFL Classroom

Phonological awareness has been claimed as one of the significant factors in learning to read. This paper reported our research on students' phonological awareness in EFL classrooms. We wanted to examine whether the phonological awareness tasks correlate with reading accuracy. Firstly, we analyzed their phonological awareness in the tasks of identification, substitution, addition, and deletion in English phonemes and syllables. Then, we discussed the relationship between phonological awareness and reading skills. We observed the students (N=143) of the even semesters (II, IV, VI, and VIII) at the English Education Department (Academic Year 2021/2022), Universitas Bandar Lampung, Indonesia. From the complete task of knowing students' phonological awareness that has been carried out, there was an increase in the average score from Semester II to Semester VIII students. The highest mean score was found in the student's test scores of Semester VIII (91.2; SD 6.3) and the lowest for Semester II students (62.5; SD 11.4). To test whether the phonological awareness tasks correlated with reading accuracy, the Pearson product-moment correlation coefficient (r) was calculated, and the results showed that the reading accuracy had a positive correlation either for the phoneme (r = 0.472) and syllable (r = 0.534) tasks and the correlation was significant (p &lt; .01).
Strategies and Reasons When Addressing Grammatical Problems in Thesis Writing

English grammar plays an important role in writing, including master’s theses. To write an acceptable thesis, students often use strategies to address grammatical errors. They also have reasons for their strategy preferences. In this mixed-methods study, we explored strategies graduate students use to address grammatical errors when writing theses and their reasons for using them. To investigate this, we engaged graduate English language learners (N = 68) at an Indonesian university. The findings showed that the most frequently used strategy was checking online sources (M = 3.99), while the least used strategy was paying someone to edit (M = 1.21).
Practical Equivariances via Relational Conditional Neural Processes

Conditional Neural Processes (CNPs) are a class of metalearning models popular for combining the runtime efficiency of amortized inference with reliable uncertainty quantification. Many relevant machine learning tasks, such as in spatio-temporal modeling, Bayesian Optimization and continuous control, inherently contain equivariances -- for example to translation -- which the model can exploit for maximal performance. However, prior attempts to include equivariances in CNPs do not scale effectively beyond two input dimensions. In this work, we propose Relational Conditional Neural Processes (RCNPs), an effective approach to incorporate equivariances into any neural process model. Our proposed method extends the applicability and impact of equivariant neural processes to higher dimensions. We empirically demonstrate the competitive performance of RCNPs on a large array of tasks naturally containing equivariances.
Eigen Background Subtraction for Industrial Flaw Detection: Application to High-Pressure Turbine Blade CT Scans

Abstract We propose a state-of-the-art approach that is the first to use Eigen background subtraction to reveal flaws in three-dimensional Computed Tomography data. Our method is composed of two main steps. During the first step, principal component analysis (PCA) is applied on flaw-free blade stack data. From a statistical perspective, a series of “flaw-free” characteristic functions is extracted. The second step consists of decomposing the blade of interest according to the functions calculated from PCA. This projection allows the construction of a synthetic blade without any flaws. A subtraction between the synthetic blade and real blade highlights the abnormal variations. The main advantage of this technique is that the processing remains applicable even when the measurement system or parts of the system have variability that is greater than the flaw size.
ANALYSIS OF THE CALCULATION OF REBAR BEAM STRUCTURE OF FLOOR 6 ZONE 1 INDIGO BANDUNG HOTEL PROJECT USING BUILDING INFORMATION MODELING (BIM)

This study aims to find out the results of calculating the requirements for rebar beam structures in the Hotel Indigo Bandung Development Project using the Building Information Modeling (BIM) method with the help of Autodesk Revit 2020 software, to be further studied against conventional methods. The type of research used in writing the review is quantitative research. The methods used in collecting data in this study are observation, interviews, and documentation studies. Data collection is used to find the truth and accuracy of the data obtained carefully and accurately so that the truth can be accounted for. The results showed that the total calculation of rebar requirements using the conventional method weighed 18843.211 kg, the total calculation of rebar requirements using the Building Information Modeling method with the help of Autodesk Revit 2020 software was 18272.58 kg, there was a difference of 570.628 kg or a difference of 3%.