Reviewerly/exHarmony huggingface dataset:
Adaptive Node Participation for Straggler-Resilient Federated Learning

Federated learning is prone to multiple system challenges including system heterogeneity where clients have different computation and communication capabilities. Such heterogeneity in clients’ computation speeds has a negative effect on the scalability of federated learning algorithms and causes significant slow-down in their runtime due to the existence of stragglers. In this chapter, we propose a novel straggler-resilient federated learning method that incorporates statistical characteristics of the clients’ data to adaptively select the clients in order to speed up the learning procedure. The key idea of our algorithm is to start the training procedure with faster nodes and gradually involve the slower nodes in the model training once the statistical accuracy of the data corresponding to the current participating nodes is reached. The proposed approach reduces the overall runtime required to achieve the statistical accuracy of data of all nodes, as the solution for each stage is close to the solution of the subsequent stage with more samples and can be used as a warm-start. Our numerical experiments demonstrate significant speedups in wall-clock time of our straggler-resilient method compared to other federated learning benchmarks.
Quantum Universally Composable Oblivious Linear Evaluation

Oblivious linear evaluation is a generalization of oblivious transfer, whereby two distrustful parties obliviously compute a linear function, f (x) = ax + b, i.e., each one provides their inputs that remain unknown to the other, in order to compute the output f (x) that only one of them receives. From both a structural and a security point of view, oblivious linear evaluation is fundamental for arithmetic-based secure multi-party computation protocols. In the classical case, oblivious linear evaluation protocols can be generated using oblivious transfer, and their quantum counterparts can, in principle, be constructed as straightforward extensions using quantum oblivious transfer. Here, we present the first, to the best of our knowledge, quantum protocol for oblivious linear evaluation that, furthermore, does not rely on quantum oblivious transfer. We start by presenting a semi-honest protocol and then extend it to the dishonest setting employing a commit-and-open strategy. Our protocol uses high-dimensional quantum states to obliviously compute f (x) on Galois Fields of prime and prime-power dimension. These constructions utilize the existence of a complete set of mutually unbiased bases in prime-power dimension Hilbert spaces and their linear behaviour upon the Heisenberg-Weyl operators. We also generalize our protocol to achieve vector oblivious linear evaluation, where several instances of oblivious linear evaluation are generated, thus making the protocol more efficient. We prove the protocols to have static security in the framework of quantum universal composability.
Coupling Deep Imputation with Multitask Learning for Downstream Tasks on Genomics Data

Genomics data such as RNA gene expression, methylation and micro RNA expression are valuable sources of information for various clinical predictive tasks. For example, predicting survival outcomes, cancer histology type and other patients' related information is possible using not only clinical data but molecular data as well. Moreover, using these data sources together, for example in multitask learning, can boost the performance. However, in practice, there are many missing data points which leads to significantly lower patient numbers when analysing full cases, which in our setting refers to all modalities being present. In this paper we investigate how imputing data with missing values using deep learning coupled with multitask learning can help to reach state-of-the-art performance results using combined genomics modalities, RNA, micro RNA and methylation. We propose a generalised deep imputation method to impute values where a patient has all modalities present except one. Interestingly enough, deep imputation alone outperforms multitask learning alone for the classification and regression tasks across most combinations of modalities. In contrast, when using all modalities for survival prediction we observe that multitask learning alone outperforms deep imputation alone with statistical significance (adjusted p-value 0.03). Thus, both approaches are complementary when optimising performance for downstream predictive tasks.
MQT Bench: Benchmarking Software and Design Automation Tools for Quantum Computing

Quantum software tools for a wide variety of design tasks on and across different levels of abstraction are crucial in order to eventually realize useful quantum applications. This requires practical and relevant benchmarks for new software tools to be empirically evaluated and compared to the current state of the art. Although benchmarks for specific design tasks are commonly available, the demand for an overarching cross-level benchmark suite has not yet been fully met and there is no mutual consolidation in how quantum software tools are evaluated thus far. In this work, we propose the MQT Bench benchmark suite (as part of the Munich Quantum Toolkit, MQT) based on four core traits: (1) cross-level support for different abstraction levels, (2) accessibility via an easy-to-use web interface (https://www.cda.cit.tum.de/mqtbench) and a Python package, (3) provision of a broad selection of benchmarks to facilitate generalizability, as well as (4) extendability to future algorithms, gate-sets, and hardware architectures. By comprising more than 70,000 benchmark circuits ranging from 2 to 130 qubits on four abstraction levels, MQT Bench presents a first step towards benchmarking different abstraction levels with a single benchmark suite to increase comparability, reproducibility, and transparency.
3D Common Corruptions and Data Augmentation

We introduce a set of image transformations that can be used as corruptions to evaluate the robustness of models as well as data augmentation mechanisms for training neural networks. The primary distinction of the proposed transformations is that, unlike existing approaches such as Common Corruptions, the geometry of the scene is incorporated in the transformations -- thus leading to corruptions that are more likely to occur in the real world. We also introduce a set of semantic corruptions (e.g. natural object occlusions). We show these transformations are `efficient' (can be computed on-the-fly), `extendable' (can be applied on most image datasets), expose vulnerability of existing models, and can effectively make models more robust when employed as `3D data augmentation' mechanisms. The evaluations on several tasks and datasets suggest incorporating 3D information into benchmarking and training opens up a promising direction for robustness research.
3D GAN Inversion for Controllable Portrait Image Animation

Millions of images of human faces are captured every single day; but these photographs portray the likeness of an individual with a fixed pose, expression, and appearance. Portrait image animation enables the post-capture adjustment of these attributes from a single image while maintaining a photorealistic reconstruction of the subject's likeness or identity. Still, current methods for portrait image animation are typically based on 2D warping operations or manipulations of a 2D generative adversarial network (GAN) and lack explicit mechanisms to enforce multi-view consistency. Thus these methods may significantly alter the identity of the subject, especially when the viewpoint relative to the camera is changed. In this work, we leverage newly developed 3D GANs, which allow explicit control over the pose of the image subject with multi-view consistency. We propose a supervision strategy to flexibly manipulate expressions with 3D morphable models, and we show that the proposed method also supports editing appearance attributes, such as age or hairstyle, by interpolating within the latent space of the GAN. The proposed technique for portrait image animation outperforms previous methods in terms of image quality, identity preservation, and pose transfer while also supporting attribute editing.
Maximum Consensus by Weighted Influences of Monotone Boolean Functions

Maximisation of Consensus (MaxCon) is one of the most widely used robust criteria in computer vision. Tennakoon et al. (CVPR2021), made a connection between MaxCon and estimation of influences of a Monotone Boolean function. In such, there are two distributions involved: the distribution defining the influence measure; and the distribution used for sampling to estimate the influence measure. This paper studies the concept of weighted influences for solving MaxCon. In particular, we study the Bernoulli measures. Theoretically, we prove the weighted influences, under this measure, of points belonging to larger structures are smaller than those of points belonging to smaller structures in general. We also consider another “natural” family of weighting strategies: sampling with uniform measure concentrated on a particular (Hamming) level of the cube. One can choose to have matching distributions: the same for defining the measure as for implementing the sampling. This has the advantage that the sampler is an unbiased estimator of the measure. Based on weighted sampling, we modify the algorithm of Tennakoon et al., and test on both synthetic and real datasets. We show some modest gains of Bernoulli sampling, and we illuminate some of the interactions between structure in data and weighted measures and weighted sampling.
Jam or Cream First? Modeling Ambiguity in Neural Machine Translation with SCONES

The softmax layer in neural machine translation is designed to model the distribution over mutually exclusive tokens. Machine translation, however, is intrinsically uncertain: the same source sentence can have multiple semantically equivalent translations. Therefore, we propose to replace the softmax activation with a multi-label classification layer that can model ambiguity more effectively. We call our loss function Single-label Contrastive Objective for Non-Exclusive Sequences (SCONES). We show that the multi-label output layer can still be trained on single reference training data using the SCONES loss function. SCONES yields consistent BLEU score gains across six translation directions, particularly for medium-resource language pairs and small beam sizes. By using smaller beam sizes we can speed up inference by a factor of 3.9x and still match or improve the BLEU score obtained using softmax. Furthermore, we demonstrate that SCONES can be used to train NMT models that assign the highest probability to adequate translations, thus mitigating the "beam search curse". Additional experiments on synthetic language pairs with varying levels of uncertainty suggest that the improvements from SCONES can be attributed to better handling of ambiguity.
ANALISA PERBANDINGAN MODEL PREDICTION DALAM PREDIKSI HARGA SAHAM MENGGUNAKAN METODE LINEAR REGRESSION, RANDOM FOREST REGRESSION DAN MULTILAYER PERCEPTRON

Stock is one type of long-term investment that is quite in demand by the public because this investment brings quite a large profit for its investors. However, in relation to this, stock price movements, in general, tend to be non-linear and non-stationary, this is because stock prices can be influenced by several factors whose results can change the pattern of stock price values either up or down, so this can make it difficult to stock prices prediction. In this study, a comparative analysis of prediction models was carried out in predicting stock prices using a technical approach based on past data, while the data used were historical stock prices by taking data samples from three issuers from the Indonesian capital market. There are three methods that were tested in this study, including Linear Regression (LR), Random Forest Regression (RFR), and Multilayer Perceptron (MLP). The test was carried out with two data modeling, namely partitioning which was validated with Cross-Validation, and data modeling with Cross-Validation without partitioning. In this study, the prediction model with LR is able to produce a fairly low error prediction value with the lowest RMSE score of 0.010 and the highest RMSE of 0.012, the lowest MAPE of 1.2%, and the highest of 1.9%, the lowest MAE of 0.006 and the highest. of 0.009, and the highest R2 value was 99.8% and the lowest was 99.6%. It can be concluded that in this study, the Linear Regression prediction model is able to predict historical data on stock prices better than the RFR and MLP models.
Mitigating the Rashomon Effect in Counterfactual Explanation: A Game-theoretic Approach

Counterfactual examples (CEs) are generally created to interpret the decision of a model. In this case, if a model makes a certain decision for an instance, the CEs of that instance reverse the decision of the model. There are many advantages of using counterfactuals as a way of explaining model decisions; however, there is one issue known as the Rashomon Effect that might dissuade target/intended users from using counterfactuals. If someone is presented with too many options, this might be overwhelming to them, and they might end up choosing an option that is not optimal or ideal for them. In this case, the Rashomon Effect is an impediment to realizing the full potential of counterfactual explanations. To utilize the full power of CEs and make them more helpful, we need to address the Rashomon Effect. In this work, we focus on this issue from game-theoretic perspectives to help target users make informed and feasible decisions by finding highly suitable CEs. In this case, finding good counterfactuals will be a game between two players where each of them tries to find better CEs.
Brainish: Formalizing A Multimodal Language for Intelligence and Consciousness

Having a rich multimodal inner language is an important component of human intelligence that enables several necessary core cognitive functions such as multimodal prediction, translation, and generation. Building upon the Conscious Turing Machine (CTM), a machine model for consciousness proposed by Blum and Blum (2021), we describe the desiderata of a multimodal language called Brainish, comprising words, images, audio, and sensations combined in representations that the CTM's processors use to communicate with each other. We define the syntax and semantics of Brainish before operationalizing this language through the lens of multimodal artificial intelligence, a vibrant research area studying the computational tools necessary for processing and relating information from heterogeneous signals. Our general framework for learning Brainish involves designing (1) unimodal encoders to segment and represent unimodal data, (2) a coordinated representation space that relates and composes unimodal features to derive holistic meaning across multimodal inputs, and (3) decoders to map multimodal representations into predictions (for fusion) or raw data (for translation or generation). Through discussing how Brainish is crucial for communication and coordination in order to achieve consciousness in the CTM, and by implementing a simple version of Brainish and evaluating its capability of demonstrating intelligence on multimodal prediction and retrieval tasks on several real-world image, text, and audio datasets, we argue that such an inner language will be important for advances in machine models of intelligence and consciousness.
Regulating the international activities of Russian higher education institutions

One of the key conditions for increasing of Russian education competitiveness in the international market is the active involvement of universities in various international cooperation forms. For this purpose, an analysis of international, federal, local normative legal acts regulating the international activities of Russian higher education institutions was carried out. The international aspect was considered using a comparative legal analysis of agreements between Russia and other countries. The article presents the results of the analysis of the principles of international agreements regulating the educational process in Russian universities. According to the research, a model, regulating the international activities of Russia’s universities, was analysed. The study of regulatory legal acts in Russia’s higher education led to the conclusion about the legislation elaboration level, to outline the prospects for development and the possibility of expanding the domestic universities international activities.
GPUNet: Searching the Deployable Convolution Neural Networks for GPUs

Customizing Convolution Neural Networks (CNN) for production use has been a challenging task for DL practitioners. This paper intends to expedite the model customization with a model hub that contains the optimized models tiered by their inference latency using Neural Architecture Search (NAS). To achieve this goal, we build a distributed NAS system to search on a novel search space that consists of prominent factors to impact latency and accuracy. Since we target GPU, we name the NAS optimized models as GPUNet, which establishes a new SOTA Pareto frontier in inference latency and accuracy. Within 1$ms$, GPUNet is 2x faster than EfficientNet-X and FBNetV3 with even better accuracy. We also validate GPUNet on detection tasks, and GPUNet consistently outperforms EfficientNet-X and FBNetV3 on COCO detection tasks in both latency and accuracy. All of these data validate that our NAS system is effective and generic to handle different design tasks. With this NAS system, we expand GPUNet to cover a wide range of latency targets such that DL practitioners can deploy our models directly in different scenarios.
Explainability Is in the Mind of the Beholder: Establishing the Foundations of Explainable Artificial Intelligence

Explainable artificial intelligence and interpretable machine learning are research domains growing in importance. Yet, the underlying concepts remain somewhat elusive and lack generally agreed definitions. While recent inspiration from social sciences has refocused the work on needs and expectations of human recipients, the field still misses a concrete conceptualisation. We take steps towards addressing this challenge by reviewing the philosophical and social foundations of human explainability, which we then translate into the technological realm. In particular, we scrutinise the notion of algorithmic black boxes and the spectrum of understanding determined by explanatory processes and explainees' background knowledge. This approach allows us to define explainability as (logical) reasoning applied to transparent insights (into, possibly black-box, predictive systems) interpreted under background knowledge and placed within a specific context -- a process that engenders understanding in a selected group of explainees. We then employ this conceptualisation to revisit strategies for evaluating explainability as well as the much disputed trade-off between transparency and predictive power, including its implications for ante-hoc and post-hoc techniques along with fairness and accountability established by explainability. We furthermore discuss components of the machine learning workflow that may be in need of interpretability, building on a range of ideas from human-centred explainability, with a particular focus on explainees, contrastive statements and explanatory processes. Our discussion reconciles and complements current research to help better navigate open questions -- rather than attempting to address any individual issue -- thus laying a solid foundation for a grounded discussion and future progress of explainable artificial intelligence and interpretable machine learning.
Comparative Analysis of Boosting Algorithms Over MNIST Handwritten Digit Dataset

Several machine learning algorithms have been adopted for detection of handwritten digits from the MNIST dataset. However, most commonly implemented algorithms are convolutional neural network (CNN), for the extraction of features and support vector machine (SVM), artificial neural network (ANN) and random forest, for classification. In this paper, boosting algorithms like extreme gradient boost (XGBoost), advanced gradient boost (AdaBoost), and gradient boosting have been used to detect handwritten digits from MNIST dataset. To compare the performance of the algorithms confusion metrics, recall, F1 score, and precision were used. From the results, it was found that AdaBoost outperformed the XGBoost and gradient boosting (GB) algorithms with 96.86% accuracy.
Extracting associations and meanings of objects depicted in artworks through bi-modal deep networks

We present a novel bi-modal system based on deep networks to address the problem of learning associations and simple meanings of objects depicted in "authored" images, such as fine art paintings and drawings.Our overall system processes both the images and associated texts in order to learn associations between images of individual objects, their identities and the abstract meanings they signify.Unlike past deep net that describe depicted objects and infer predicates, our system identifies meaning-bearing objects ("signifiers") and their associations ("signifieds") as well as basic overall meanings for target artworks.Our system had precision of 48% and recall of 78% with an F1 metric of 0.6 on a curated set of Dutch vanitas paintings, a genre celebrated for its concentration on conveying a meaning of great import at the time of their execution.We developed and tested our system on fine art paintings but our general methods can be applied to other authored images.1.
Intelligent Diagnosis of Pipeline Defects Based on Extreme Learning Machine

In order to carry out effective defect diagnosis of in-service oil and gas pipelines to ensure the safe and stable operation of oil and gas pipelines, this article proposes a pipeline defect intelligent diagnosis method based on particle swarm optimization and extreme learning machine. First, extract the defect signal features to form a feature vector set; then use the particle swarm algorithm to optimize the extreme learning machine to obtain hidden node thresholds; finally, divide the feature vector set into a testing set and a training set, and use improved extreme learning The machine is used as a pattern recognition algorithm for defect pattern recognition. Experimental results show that the method proposed in this paper can effectively perform fault pattern recognition, with a recognition accuracy of 92%, and is suitable for intelligent diagnosis of pipeline defects.
Methods and techniques of formation of arithmetic musical competence in students

The purpose of this study is to form the arithmetic musical competency of students on the basis of Al-Farabi's theoretical musical heritage.In this context, the work The Great Book of Music is of great importance.In this work, he gives not only a scientific explanation of the origin of sounds as properties of matter, but also gives an idea of the arithmetic principles of the emergence of harmony and musical melodies.As a result of this study, it was determined that the problem of the formation of arithmetic musical competence in the learning process is relatively new and insufficiently studied.Arithmetic musical competence can be formed on the basis of teaching the method of musical theory of composition Al-Farabi.This method is based on the fact that musical sounds and intervals, which are real physical phenomena, can be characterised using numbers and numerical ratios.