Reviewerly/exHarmony huggingface dataset:
SWIFT: Super-fast and Robust Privacy-Preserving Machine Learning

Performing machine learning (ML) computation on private data while maintaining data privacy, aka Privacy-preserving Machine Learning~(PPML), is an emergent field of research. Recently, PPML has seen a visible shift towards the adoption of the Secure Outsourced Computation~(SOC) paradigm due to the heavy computation that it entails. In the SOC paradigm, computation is outsourced to a set of powerful and specially equipped servers that provide service on a pay-per-use basis. In this work, we propose SWIFT, a robust PPML framework for a range of ML algorithms in SOC setting, that guarantees output delivery to the users irrespective of any adversarial behaviour. Robustness, a highly desirable feature, evokes user participation without the fear of denial of service. At the heart of our framework lies a highly-efficient, maliciously-secure, three-party computation (3PC) over rings that provides guaranteed output delivery (GOD) in the honest-majority setting. To the best of our knowledge, SWIFT is the first robust and efficient PPML framework in the 3PC setting. SWIFT is as fast as (and is strictly better in some cases than) the best-known 3PC framework BLAZE (Patra et al. NDSS'20), which only achieves fairness. We extend our 3PC framework for four parties (4PC). In this regime, SWIFT is as fast as the best known fair 4PC framework Trident (Chaudhari et al. NDSS'20) and twice faster than the best-known robust 4PC framework FLASH (Byali et al. PETS'20). We demonstrate our framework's practical relevance by benchmarking popular ML algorithms such as Logistic Regression and deep Neural Networks such as VGG16 and LeNet, both over a 64-bit ring in a WAN setting. For deep NN, our results testify to our claims that we provide improved security guarantee while incurring no additional overhead for 3PC and obtaining 2x improvement for 4PC.
Short text classification based on word2vec and improved TDFIDF merge weighting

With the development of information technology, the amount of information in contemporary society has proliferated exponentially. It contains not only the information we need, but also the redundant information that we do not need. Therefore, how to pass the massive data the algorithm processing, filtering out the information we do not need, has always been a hotspot in the field of information retrieval. TF-IDF is one of the statistical methods, but the traditional TF-IDF method simply highlights the importance of small frequency vocabulary, but it does not reflect the role of the keyword in the context; once encountered some very useful vocabulary, it will have a bad influence on the classification results. Therefore, we introduce the word2vec model and the improved TFIDF algorithm for combining weights. Experiments show that the model retrieval results are better than the traditional two models.
Instability, Computational Efficiency and Statistical Accuracy

Many statistical estimators are defined as the fixed point of a data-dependent operator, with estimators based on minimizing a cost function being an important special case. The limiting performance of such estimators depends on the properties of the population-level operator in the idealized limit of infinitely many samples. We develop a general framework that yields bounds on statistical accuracy based on the interplay between the deterministic convergence rate of the algorithm at the population level, and its degree of (in)stability when applied to an empirical object based on $n$ samples. Using this framework, we analyze both stable forms of gradient descent and some higher-order and unstable algorithms, including Newton's method and its cubic-regularized variant, as well as the EM algorithm. We provide applications of our general results to several concrete classes of models, including Gaussian mixture estimation, non-linear regression models, and informative non-response models. We exhibit cases in which an unstable algorithm can achieve the same statistical accuracy as a stable algorithm in exponentially fewer steps -- namely, with the number of iterations being reduced from polynomial to logarithmic in sample size $n$.
Scalable Privacy-Preserving Distributed Learning

In this paper, we address the problem of privacy-preserving distributed learning and evaluation of machine learning models by analyzing it in the widespread MapReduce abstraction that we extend with privacy constraints. Following this abstraction, we instantiate SPINDLE (Scalable Privacy-preservINg Distributed LEarning), an operational distributed system that supports the privacy-preserving training and evaluation of generalized linear models on distributed datasets. SPINDLE enables the efficient execution of distributed gradient descent while ensuring data and model confidentiality, as long as at least one of the data providers is honest-but-curious. The trained model is then used for oblivious predictions on confidential data. SPINDLE is able to efficiently perform demanding training tasks that require a high number of iterations on large input data with thousands of features, distributed among hundreds of data providers. It relies on a multiparty homomorphic encryption scheme to execute high-depth computations on encrypted data without significant overhead. It further leverages on its distributed construction and the packing capabilities of the cryptographic scheme to efficiently parallelize the computations at multiple levels. In our evaluation, SPINDLE performs the training of a logistic-regression model on a dataset of one million samples with 32 features distributed among 160 data providers in less than 176 seconds, yielding similar accuracy to non-secure centralized models.
NEJM-enzh: A Parallel Corpus for English-Chinese Translation in the Biomedical Domain

Machine translation requires large amounts of parallel text. While such datasets are abundant in domains such as newswire, they are less accessible in the biomedical domain. Chinese and English are two of the most widely spoken languages, yet to our knowledge a parallel corpus in the biomedical domain does not exist for this language pair. In this study, we develop an effective pipeline to acquire and process an English-Chinese parallel corpus, consisting of about 100,000 sentence pairs and 3,000,000 tokens on each side, from the New England Journal of Medicine (NEJM). We show that training on out-of-domain data and fine-tuning with as few as 4,000 NEJM sentence pairs improve translation quality by 25.3 (13.4) BLEU for en$\to$zh (zh$\to$en) directions. Translation quality continues to improve at a slower pace on larger in-domain datasets, with an increase of 33.0 (24.3) BLEU for en$\to$zh (zh$\to$en) directions on the full dataset.
Phone Features Improve Speech Translation

End-to-end models for speech translation (ST) more tightly couple speech recognition (ASR) and machine translation (MT) than a traditional cascade of separate ASR and MT models, with simpler model architectures and the potential for reduced error propagation. Their performance is often assumed to be superior, though in many conditions this is not yet the case. We compare cascaded and end-to-end models across high, medium, and low-resource conditions, and show that cascades remain stronger baselines. Further, we introduce two methods to incorporate phone features into ST models. We show that these features improve both architectures, closing the gap between end-to-end models and cascades, and outperforming previous academic work -- by up to 9 BLEU on our low-resource setting.
Inquiry-Based Education: Innovation in Participatory Inquiry Paradigm

Inquiry-based learning is fundamental for the development of higher order thinking skills that guides learners to inquire meaningful questions that led to relevant answers, therefore awaken learners’ curiosity and wonder. Re-cent ameliorations in technology have captivated the enthusiasm of both educators and researchers to develop inquiry-based classroom activities that emphasize the application of educational technology in the domain of school science education. Thus, we have designed a learning application “AIBASE”, which assists primary school students in generating hypotheses during Science experiments. The instructional framework was used to scru-tinize the effectiveness of using “AIBASE” in aiding the learning process. The results implied improvements in students’ performance level. In addi-tion, this paper also highlights the main criteria for inquiry-based learning.
Joint State Composition Theorems for Public-Key Encryption and Digital Signature Functionalities with Local Computation

Abstract In frameworks for universal composability, complex protocols can be built from sub-protocols in a modular way using composition theorems. However, as first pointed out and studied by Canetti and Rabin, this modular approach often leads to impractical implementations. For example, when using a functionality for digital signatures within a more complex protocol, parties have to generate new verification and signing keys for every session of the protocol. This motivates to generalize composition theorems to so-called joint state (composition) theorems, where different copies of a functionality may share some state, e.g., the same verification and signing keys. In this paper, we present a joint state theorem which is more general than the original theorem of Canetti and Rabin, for which several problems and limitations are pointed out. We apply our theorem to obtain joint state realizations for three functionalities: public-key encryption, replayable public-key encryption, and digital signatures. Unlike most other formulations, our functionalities model that ciphertexts and signatures are computed locally, rather than being provided by the adversary. To obtain the joint state realizations, the functionalities have to be designed carefully. Other formulations proposed in the literature are shown to be unsuitable. Our work is based on the IITM model. Our definitions and results demonstrate the expressivity and simplicity of this model. For example, unlike Canetti’s UC model, in the IITM model no explicit joint state operator needs to be defined and the joint state theorem follows immediately from the composition theorem in the IITM model.
Latent Domain Learning with Dynamic Residual Adapters

A practical shortcoming of deep neural networks is their specialization to a single task and domain. While recent techniques in domain adaptation and multi-domain learning enable the learning of more domain-agnostic features, their success relies on the presence of domain labels, typically requiring manual annotation and careful curation of datasets. Here we focus on a less explored, but more realistic case: learning from data from multiple domains, without access to domain annotations. In this scenario, standard model training leads to the overfitting of large domains, while disregarding smaller ones. We address this limitation via dynamic residual adapters, an adaptive gating mechanism that helps account for latent domains, coupled with an augmentation strategy inspired by recent style transfer techniques. Our proposed approach is examined on image classification tasks containing multiple latent domains, and we showcase its ability to obtain robust performance across these. Dynamic residual adapters significantly outperform off-the-shelf networks with much larger capacity, and can be incorporated seamlessly with existing architectures in an end-to-end manner.
Manual Speech Synthesis Data Acquisition - From Script Design to Recording Speech

Atli Þor Sigurgeirsson, atlithors@ru.is, Reykjavik University Gunnar Thor Ornolfsson, gunnarthor@hi.is, Arni Magnusson institute of Icelandic studies Dr. Jon Guðnason, jg@ru.is In this paper we present the work of collecting a large amount of high quality speech synthesis data for Icelandic. 8 speakers will be recorded for 20 hours each. A script design strategy is proposed and three scripts have been generated to maximize diphone coverage, varying in length. The largest reading script contains 14,400 prompts and includes 87.3% of all Icelandic diphones at least once and 81% of all Icelandic diphones at least twenty times. A recording client was developed to facilitate recording sessions. The client supports easily importing scripts and maintaining multiple collections in parallel. The recorded data can be downloaded straight from the client. Recording sessions are carried out in a professional studio under supervision and started October of 2019. As of writing, 58.7 hours of high quality speech data has been collected. The scripts, the recording software and the speech data will later be released under a CC-BY 4.0 license.
Predictive side decoding for human-centered multiple description image coding

Abstract Multiple description coding (MDC) provides a favorable solution for human-centered image communication, which takes into account people’s varying watching situations as well as people’s demand for real-time image display. As an effective technique for MDC, three-description lattice vector quantization (3D-LVQ) is considered for image coding in this paper. Based on intra- and inter-correlation in the 3D-LVQ index assignment as well as wavelet intra-subband correlation, a novel predictive decoding method for 3D-LVQ-based image coding is proposed to enhance side decoding performance, which attempts to predict lost descriptions (sublattice points) in a good way for better reconstructions of wavelet vectors (fine lattice points) in the side decoding. Experimental results validate effectiveness of the proposed decoding scheme in terms of rate-distortion performance.
The Critical Catalog: Library Information Systems, Tricksterism, and Social Justice

In this paper, we describe the Critical Catalog, a grant-funded research through design project intended to investigate metadata elements, values, and organizational structures necessary to intentionally advocate for diversity and expose library users to resources from populations traditionally marginalized in literature and publishing. Drawing on principles from critical design, the prototype functions as a critical intervention intended to raise questions and stimulate debate, rather than a purely technical fix to deeply social concerns. A detailed reflective discussion of the design process reveals how existing infrastructural constraints shaped design decisions that led to increased advocacy and a stronger activist standpoint. We discuss the use of metadata as design material for social justice, the application of tricksterism in HCI, and how both practical limitations from professional contexts and imposed limitations based on identities and positions of power can lead to surprising places, meanings, and questions.
MixColumns Coefficient Property and Security of the AES with A Secret S-Box

The MixColumns operation is an important component providing diffusion for the AES. The branch number of it ensures that any continuous four rounds of the AES have at least 25 active S-Boxes, which makes the AES secure against the differential and linear cryptanalysis. However, the choices of the coefficients of the MixColumns matrix may undermine the AES security against some novel-type attacks. A particular property of the AES MixColumns matrix coefficient has been noticed in recent papers that each row or column of the matrix has elements that sum to zero. Several attacks have been developed taking advantage of the coefficient property. In this paper we investigate further the influence of the specific coefficient property on the AES security. Our target, which is also one of the targets of the previous works, is a 5-round AES variant with a secret S-Box. We will show how we take advantage of the coefficient property to extract the secret key directly without any assistance of the S-Box information. Compared with the previous similar attacks, the present attacks here are the best in terms of the complexity under the chosen-plaintext scenario.
Decision support system for the machine learning methods selection in big data mining

This article focuses on the aspects of the decision support system for the machine learning methods selection in big data mining development.The paper includes the results of the analysis of the problem of intellectual processing and analysis of big data.It describes proposal ways of using metadata as a basis for the formation of an analytical rating for evaluating machine learning methods.The paper presents the results of designing and using a decision support system for evaluating machine learning methods for solving data mining problems.The developed decision support system allows us to reduce the analysis time of suitable methods for solving machine learning problems by a data science analyst, taking into account the specifics of the input data arrays, their volumes, structure and other metadata.
An Evaluation Dataset for Identifying Communicative Functions of Sentences in English Scholarly Papers

Formulaic expressions, such as ‘in this paper we propose’, are used by authors of scholarly papers to perform communicative functions; the communicative function of the present example is ‘stating the aim of the paper’. Collecting such expressions and pairing them with their communicative functions would be highly valuable for various tasks, particularly for writing assistance. However, such collection and paring in a principled and automated manner would require high-quality annotated data, which are not available. In this study, we address this shortcoming by creating a manually annotated dataset for detecting communicative functions in sentences. Starting from a seed list of labelled formulaic expressions, we retrieved new sentences from scholarly papers in the ACL Anthology and asked multiple human evaluators to label communicative functions. To show the usefulness of our dataset, we conducted a series of experiments that determined to what extent sentence representations acquired by recent models, such as word2vec and BERT, can be employed to detect communicative functions in sentences.
Performing Structured Improvisations with pre-trained Deep Learning Models

The quality of outputs produced by deep generative models for music have seen a dramatic improvement in the last few years. However, most deep learning models perform in "offline" mode, with few restrictions on the processing time. Integrating these types of models into a live structured performance poses a challenge because of the necessity to respect the beat and harmony. Further, these deep models tend to be agnostic to the style of a performer, which often renders them impractical for live performance. In this paper we propose a system which enables the integration of out-of-the-box generative models by leveraging the musician's creativity and expertise.
Entity Linking and Lexico-Semantic Patterns for Ontology Learning

Ontology learning from a text written in natural language is a well-studied domain. However, the applicability of techniques for ontology learning from natural language texts is strongly dependent on the characteristics of the text corpus and the language used. In this paper, we present our work so far in entity linking and enhancing the ontology with extracted relations between concepts. We discuss the benefits of adequately designed lexico-semantic patterns in ontology learning. We propose a preliminary set of lexico-semantic patterns designed for the Czech language to learn new relations between concepts in the related domain ontology in a semi-supervised approach. We utilize data from the urban planning and development domain to evaluate the introduced technique. As a partial prototypical implementation of the stack, we present Annotace, a text annotation service that provides links between the ontology model and the textual documents in Czech.
Human Pose Annotation Using a Motion Capture System for Loose-Fitting Clothes

This paper proposes a framework for automatically annotating the keypoints of a human body in images for learning 2D pose estimation models. Ground-truth annotations for supervised learning are difficult and cumbersome in most machine vision tasks. While considerable contributions in the community provide us a huge number of pose-annotated images, all of them mainly focus on people wearing common clothes, which are relatively easy to annotate the body keypoints. This paper, on the other hand, focuses on annotating people wearing loose-fitting clothes (e.g., Japanese Kimono) that occlude many body keypoints. In order to automatically and correctly annotate these people, we divert the 3D coordinates of the keypoints observed without loose-fitting clothes, which can be captured by a motion capture system (MoCap). These 3D keypoints are projected to an image where the body pose under loose-fitting clothes is similar to the one captured by the MoCap. Pose similarity between bodies with and without loose-fitting clothes is evaluated with 3D geometric configurations of MoCap markers that are visible even with loose-fitting clothes (e.g., markers on the head, wrists, and ankles). Experimental results validate the effectiveness of our proposed framework for human pose estimation.
3. The Graphic Communication Actor: Generating Visual Rhetoric for Development Initiatives

Abstract The art of using language for public expression in order to persuade target audience to support development initiatives is a key reason for graphic communication. This requires communication actors particularly, the graphic encoder to know salient input and output variables of communication for effective mediation. However, the prevalence ignorance of these variables, often results in ineffective media production that is counter-productive to development. Therefore, this paper focused on production of practical rhetoric in graphic language for development programmes. The paper employed the critical-historical-analytic examination and content analysis methods. It introduced the reader to the need for practical rhetoric in visual communication. Furthermore, it highlighted the salient input and output variables that the graphic communication actor need be conversant with in order to produce visual rhetoric, using the McGuire’s Communication/persuasion Matrix. And it exemplified graphic media that result from application or neglect of the knowledge of the variables. The paper found that consideration of the variables afforded production of effective rhetoric in graphic language. The paper ended with the need for graphic encoders to internalize knowledge of the input and output variables and utilize it during the process of media production to generate visual rhetoric with desired effect.