Reviewerly/exHarmony huggingface dataset:
XClusters: Explainability-first Clustering

We study the problem of explainability-first clustering where explainability becomes a first-class citizen for clustering. Previous clustering approaches use decision trees for explanation, but only after the clustering is completed. In contrast, our approach is to perform clustering and decision tree training holistically where the decision tree's performance and size also influence the clustering results. We assume the attributes for clustering and explaining are distinct, although this is not necessary. We observe that our problem is a monotonic optimization where the objective function is a difference of monotonic functions. We then propose an efficient branch-and-bound algorithm for finding the best parameters that lead to a balance of cluster distortion and decision tree explainability. Our experiments show that our method can improve the explainability of any clustering that fits in our framework.
Embedding generic monadic transformer into Scala

Dotty-cps-async is an open-source package that consists of scala macro, which implements generic async/await via monadic cps transform, and library, which provides monadic substitutions for higher-order functions from the standard library. It allows developers to use direct control flow constructions of the base language instead of monadic DSL for various applications. Behind well-known async/await operations, the package provides options for transforming higher-order function applications, generating call-chain proxies, and automatic coloring.
Makine Çevirisi Çıktılarının Post-Editing İşleminde Çevirmenlere Bazı Öneriler

With the development of technology, jobs once performed by a human have now been undertaken by machines with advanced software.As a result, everyday life becomes more manageable in most areas, and new working areas have appeared.Translation has also been affected by these innovations.When developing the Skopos Theory, Reiss and Vermeer (1984) defined a translator as a cultural/bilingual expert responsible for interlingual and intercultural communication.In those years when the expert identity of the translator was explained, computers were not ordinary, and translators generally worked isolated behind closed doors.Nonetheless, in the twenty-first century, the need for translation increased, and new areas of expertise in translation have become widespread.Audiovisual translation, game translation, localization, and post-editing are, for example, a few of them.Based on a descriptive approach, the study handles the concept of post-editing, a new activity a translator can undertake in a translation project, by offering some suggestions through samples to prospective and professional translators.These suggestions may be helpful in the translation of the text to be correctly received by the target audience.It is hoped that these suggestions will be beneficial in increasing the quality/clarity of translation and productivity.While the definition of post-editing is explained in detail, it is observed that the post-editing process within the translation projects might vary from translation to translation.Individual translators might obey different approaches during the post-editing of raw translations.One of the primary reasons can be that the concept of post-editing is not understood well by prospective translators.This can cause some problems when they graduate.Because translators blindly sticking to machine translation may plagiarise unconsciously in translation instead of applying to post-editing as it should be.Therefore the translator may not reflect their expertise in translation.This situation may return to making less effort in translation over time and pose an ethical problem.However, through the post-editing, the translator gives the final version to the raw output.The translator's identity should always be at the forefront, though.Because even though the translators spend less time during the translation compared to previous periods, or at least the time wasted in translation has been compensated by new technologies, the translator is an expert in knowing when and under what conditions he/she will interfere with the translation thanks to his/her professional identity.In this way, the translator can determine whether light or full post-editing is required by positioning himself/herself in the translation process and considering the text type/traditions.Then the translated text can give an impression as if it were translated following the correct post-editorial decisions.This is necessary for making the mechanical structure of the text produced by the machine humanized.
Robust Collaborative Learning with Linear Gradient Overhead

Collaborative learning algorithms, such as distributed SGD (or D-SGD), are prone to faulty machines that may deviate from their prescribed algorithm because of software or hardware bugs, poisoned data or malicious behaviors. While many solutions have been proposed to enhance the robustness of D-SGD to such machines, previous works either resort to strong assumptions (trusted server, homogeneous data, specific noise model) or impose a gradient computational cost that is several orders of magnitude higher than that of D-SGD. We present MoNNA, a new algorithm that (a) is provably robust under standard assumptions and (b) has a gradient computation overhead that is linear in the fraction of faulty machines, which is conjectured to be tight. Essentially, MoNNA uses Polyak's momentum of local gradients for local updates and nearest-neighbor averaging (NNA) for global mixing, respectively. While MoNNA is rather simple to implement, its analysis has been more challenging and relies on two key elements that may be of independent interest. Specifically, we introduce the mixing criterion of $(\alpha, \lambda)$-reduction to analyze the non-linear mixing of non-faulty machines, and present a way to control the tension between the momentum and the model drifts. We validate our theory by experiments on image classification and make our code available at https://github.com/LPD-EPFL/robust-collaborative-learning.
Equivariant Transduction through Invariant Alignment

The ability to generalize compositionally is key to understanding the potentially infinite number of sentences that can be constructed in a human language from only a finite number of words. Investigating whether NLP models possess this ability has been a topic of interest: SCAN (Lake and Baroni, 2018) is one task specifically proposed to test for this property. Previous work has achieved impressive empirical results using a group-equivariant neural network that naturally encodes a useful inductive bias for SCAN (Gordon et al., 2020). Inspired by this, we introduce a novel group-equivariant architecture that incorporates a group-invariant hard alignment mechanism. We find that our network's structure allows it to develop stronger equivariance properties than existing group-equivariant approaches. We additionally find that it outperforms previous group-equivariant networks empirically on the SCAN task. Our results suggest that integrating group-equivariance into a variety of neural architectures is a potentially fruitful avenue of research, and demonstrate the value of careful analysis of the theoretical properties of such architectures.
Turning Normalizing Flows into Monge Maps with Geodesic Gaussian Preserving Flows

Normalizing Flows (NF) are powerful likelihood-based generative models that are able to trade off between expressivity and tractability to model complex densities. A now well established research avenue leverages optimal transport (OT) and looks for Monge maps, i.e. models with minimal effort between the source and target distributions. This paper introduces a method based on Brenier's polar factorization theorem to transform any trained NF into a more OT-efficient version without changing the final density. We do so by learning a rearrangement of the source (Gaussian) distribution that minimizes the OT cost between the source and the final density. We further constrain the path leading to the estimated Monge map to lie on a geodesic in the space of volume-preserving diffeomorphisms thanks to Euler's equations. The proposed method leads to smooth flows with reduced OT cost for several existing models without affecting the model performance.
A Capability and Skill Model for Heterogeneous Autonomous Robots

Teams of heterogeneous autonomous robots become increasingly important due to their facilitation of various complex tasks. For such heterogeneous robots, there is currently no consistent way of describing the functions that each robot provides. In the field of manufacturing, capability modeling is considered a promising approach to semantically model functions provided by different machines. This contribution investigates how to apply and extend capability models from manufacturing to the field of autonomous robots and presents an approach for such a capability model.
Homophone Reveals the Truth: A Reality Check for Speech2Vec

Generating spoken word embeddings that possess semantic information is a fascinating topic. Compared with text-based embeddings, they cover both phonetic and semantic characteristics, which can provide richer information and are potentially helpful for improving ASR and speech translation systems. In this paper, we review and examine the authenticity of a seminal work in this field: Speech2Vec. First, a homophone-based inspection method is proposed to check the speech embeddings released by the author of Speech2Vec. There is no indication that these embeddings are generated by the Speech2Vec model. Moreover, through further analysis of the vocabulary composition, we suspect that a text-based model fabricates these embeddings. Finally, we reproduce the Speech2Vec model, referring to the official code and optimal settings in the original paper. Experiments showed that this model failed to learn effective semantic embeddings. In word similarity benchmarks, it gets a correlation score of 0.08 in MEN and 0.15 in WS-353-SIM tests, which is over 0.5 lower than those described in the original paper. Our data and code are available.
Quantum circuit simulation of superchannels

Quantum simulation is one of the central discipline to demonstrate the power of quantum computing. In recent years, the theoretical framework of quantum superchannels has been developed and applied widely as the extension of quantum channels. In this work, we study the quantum circuit simulation task of superchannels. We develop a quantum superchannel simulation algorithm based on the convex decomposition into sum of extreme superchannels, which can reduce the circuit cost. We demonstrate the algorithm by numerical simulation of qubit superchannels with high accuracy, making it applicable to current experimental platforms.
In Differential Privacy, There is Truth: On Vote Leakage in Ensemble Private Learning

When learning from sensitive data, care must be taken to ensure that training algorithms address privacy concerns. The canonical Private Aggregation of Teacher Ensembles, or PATE, computes output labels by aggregating the predictions of a (possibly distributed) collection of teacher models via a voting mechanism. The mechanism adds noise to attain a differential privacy guarantee with respect to the teachers' training data. In this work, we observe that this use of noise, which makes PATE predictions stochastic, enables new forms of leakage of sensitive information. For a given input, our adversary exploits this stochasticity to extract high-fidelity histograms of the votes submitted by the underlying teachers. From these histograms, the adversary can learn sensitive attributes of the input such as race, gender, or age. Although this attack does not directly violate the differential privacy guarantee, it clearly violates privacy norms and expectations, and would not be possible at all without the noise inserted to obtain differential privacy. In fact, counter-intuitively, the attack becomes easier as we add more noise to provide stronger differential privacy. We hope this encourages future work to consider privacy holistically rather than treat differential privacy as a panacea.
DRAMA: Joint Risk Localization and Captioning in Driving

Considering the functionality of situational awareness in safety-critical automation systems, the perception of risk in driving scenes and its explainability is of particular importance for autonomous and cooperative driving. Toward this goal, this paper proposes a new research direction of joint risk localization in driving scenes and its risk explanation as a natural language description. Due to the lack of standard benchmarks, we collected a large-scale dataset, DRAMA (Driving Risk Assessment Mechanism with A captioning module), which consists of 17,785 interactive driving scenarios collected in Tokyo, Japan. Our DRAMA dataset accommodates video- and object-level questions on driving risks with associated important objects to achieve the goal of visual captioning as a free-form language description utilizing closed and open-ended responses for multi-level questions, which can be used to evaluate a range of visual captioning capabilities in driving scenarios. We make this data available to the community for further research. Using DRAMA, we explore multiple facets of joint risk localization and captioning in interactive driving scenarios. In particular, we benchmark various multi-task prediction architectures and provide a detailed analysis of joint risk localization and risk captioning. The data set is available at https://usa.honda-ri.com/drama
Fair Robust Active Learning by Joint Inconsistency

Fairness and robustness play vital roles in trustworthy machine learning. Observing safety-critical needs in various annotation-expensive vision applications, we introduce a novel learning framework, Fair Robust Active Learning (FRAL), generalizing conventional active learning to fair and adversarial robust scenarios. This framework allows us to achieve standard and robust minimax fairness with limited acquired labels. In FRAL, we then observe existing fairness-aware data selection strategies suffer from either ineffectiveness under severe data imbalance or inefficiency due to huge computations of adversarial training. To address these two problems, we develop a novel Joint INconsistency (JIN) method exploiting prediction inconsistencies between benign and adversarial inputs as well as between standard and robust models. These two inconsistencies can be used to identify potential fairness gains and data imbalance mitigations. Thus, by performing label acquisition with our inconsistency-based ranking metrics, we can alleviate the class imbalance issue and enhance minimax fairness with limited computation. Extensive experiments on diverse datasets and sensitive groups demonstrate that our method obtains the best results in standard and robust fairness under white-box PGD attacks compared with existing active data selection baselines.
Attention Beats Concatenation for Conditioning Neural Fields

Neural fields model signals by mapping coordinate inputs to sampled values. They are becoming an increasingly important backbone architecture across many fields from vision and graphics to biology and astronomy. In this paper, we explore the differences between common conditioning mechanisms within these networks, an essential ingredient in shifting neural fields from memorization of signals to generalization, where the set of signals lying on a manifold is modelled jointly. In particular, we are interested in the scaling behaviour of these mechanisms to increasingly high-dimensional conditioning variables. As we show in our experiments, high-dimensional conditioning is key to modelling complex data distributions, thus it is important to determine what architecture choices best enable this when working on such problems. To this end, we run experiments modelling 2D, 3D, and 4D signals with neural fields, employing concatenation, hyper-network, and attention-based conditioning strategies -- a necessary but laborious effort that has not been performed in the literature. We find that attention-based conditioning outperforms other approaches in a variety of settings.
A Unified Data Model for Blockchains

The popularity of blockchains has been steadily rising since its inception in 2009. Its original focus was the creation of a distributed trustless system for a digital currency. Bitcoin was the first widely used blockchain. However, many more have been further created: Ethereum, Solana, Hyperledger Fabric, to name a few. Due to it, blockchain networks and its properties are being scrutinised by data scientists. This topic is known as blockchain analysis. However, analysis ́ tasks are hard to be accomplished over multiple blockchains. Even though most have similar concepts, each one has its own data modeled differently. As a consequence, scientists end up needing to do rework in order to apply similar analytics and algorithms in more than one blockchain network. In fact, blockchain data modeling is an open issue. This master’s thesis aims at proposing an unified model for blockchain data. For the best of our knowledge, there is no similar work in the literature.
Challenges on Classifying Data Streams with Concept Drift

Concept Drift is a common problem when we are working with Machine Learning. It refers to changes in the target concept over time, which may deteriorate the model’s accuracy. A recurrent problem on concept drift is to find datasets that reflect real-world scenarios. In this work, we show some datasets known to have Concept Drift, and propose changes in an existing method (Dynse), which include making it capable of handling data streams, instead of batches, and putting some trigger on it, to make its window adaptive by detecting concept drift.
A Study of Database Models for Social Network Analysis

This paper discusses conceptual and logical data models for social media datasets and applications. On the one hand, we focus on the data representation requirements from the available APIs of some social network systems. On the other hand, we consider those application requirements for information manipulation. We propose a conceptual meta-model and one possible instantiation. We also give preliminary practical results considering relational and graph database systems.
Voice Detection and Music Smart Classroom Teaching Application Based on Mobile Edge Computing

Audio monitoring information technology plays an important role in the application of monitoring systems, and it is an indispensable and important link. Whether intelligent audio monitoring management can be successfully realized, the key is to successfully detect abnormal sounds from a variety of external environment background sounds. The core technology of abnormal sound detection is a pattern classification task. The dimension of features is fixed in the traditional abnormal sound detection model. Such an ordinary solution will lead to a long time-consuming detection process and increase the boundary error. Traditional speech detection is not good enough for sound discrimination in a noisy environment, so this paper proposes an abnormal speech detection technology based on moving edge computing. Aiming at the noisy environment of the music classroom, the determination of objective function should be further optimized. Through the related technology, a certain sound can be quickly identified and analyzed in the music classroom to promote the development of the music wisdom classroom, and music wisdom classrooms can be used as a computer-aided system to help music teachers better grasp the learning situation of students, put forward relevant guidance strategies, improve students' learning enthusiasm, and enhance teachers' teaching efficiency so as to promote the progress of music teaching.
Exposure Fusion Framework in Deep Learning-Based Radiology Report Generator

Writing a radiology report is time-consuming and requires experienced radiologists. Hence a technology that could generate an automatic report would be beneficial. The key problem in developing an automated report-generating system is providing a coherent predictive text. To accomplish this, it is important to ensure the image has good quality so that the model can learn the parts of the image in interpreting, especially in medical images that tend to be noise-prone in the acquisition process. This research uses the Exposure Fusion Framework method to enhance the quality of medical images to increase the model performance in producing coherent predictive text. The model used is an encoder-decoder with visual feature extraction using a pre- trained ChexNet, Bidirectional Encoder Representation from Transformer (BERT) embedding for text feature, and Long-short Term Memory (LSTM) as a decoder. The model's performance with EFF enhancement obtained a 7% better result than without enhancement processing using an evaluation value of Bilingual Evaluation Understudy (BLEU) with n-gram 4. It can be concluded that using the enhancement method effectively increases the model's performance.
Feature Selection Using Hybrid Binary Grey Wolf Optimizer for Arabic Text Classification

Feature selection in Arabic text is a challenging task due to the complex and rich nature of Arabic. The feature selection requires solution quality, stability, conver- gence speed, and the ability to find the global optimal. This study proposes a feature selection method using the Hybrid Binary Gray Wolf Optimizer (HBGWO) for Ara- bic text classification. The HBGWO method combines the local search capabilities or exploratory of the BGWO and the search capabilities around the best solutions or exploits of the PSO. HBGWO method also combines SCA’s capabilities in finding global solutions. The data set used Arabic text from islambook.com, which consists of five Hadith books. The books selected five classes: Tauhid, Prayer, Zakat, Fasting, and Hajj. The results showed that the BGWO-PSO-SCA feature selection method with the fitness function search and classification method using SVM could per- form better on Arabic text classification problems. BGWO-PSO with fitness function and the classification method using SVM (C=1.0) gives a high accuracy value of 76.37% compared to without feature selection. The BGWO-PSO-SCA feature selec- tion method provides an accuracy value of 88.08%. This accuracy value is higher than the BGWO-PSO feature selection and other feature selection methods.
Encontrando Regras de Associação sem Especificar Suporte Mínimo e Confiança Mínima

The extraction of information and knowledge in databases has been assuming a relevant role in aiding decision making. One of the main areas of research is association rule mining. This area makes possible to capture the relationships among the attributes present in a database. Most algorithms used to extract association rules use support and confidence as parameters. Support represents the proportion of a given rule in the database and confidence represents the validity of this rule. Thus, professionals responsible for data analysis need to identify and define support and confidence thresholds (minimum support and minimum confidence, respectively) to obtain association rules. However, in certain contexts, it is difficult to identify good values for support and confidence in order to obtain the desired rules. In these situations, it may be necessary to run several queries with different values of support and confidence until the desired rules are obtained. The purpose of this research is to examine association rules mining techniques and algorithms capable of obtaining association rules without the need of specifying support and confidence, to propose new algorithms and analyze these algorithms in terms of performance and quality of the rules obtained.